Advanced Gas Chromatography in Food Analysis (Food Chemistry, Function and Analysis): Volume 17 1788011279, 9781788011273

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Published on 16 October 2019 on https://pubs.rsc.org | doi:10.1039/9781788015752-FP001

Food Chemistry, Function and Analysis

Advanced Gas Chromatography in Food Analysis Edited by Peter Q. Tranchida

Published on 16 October 2019 on https://pubs.rsc.org | doi:10.1039/9781788015752-FP001

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Advanced Gas Chromatography in Food Analysis

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Food Chemistry, Function and Analysis

Published on 16 October 2019 on https://pubs.rsc.org | doi:10.1039/9781788015752-FP001

Series editors: Gary Williamson, University of Leeds, UK Alejandro G. Marangoni, University of Guelph, Canada Juliet A. Gerrard, University of Auckland, New Zealand

Titles in the series: 1: Food Biosensors 2: Sensing Techniques for Food Safety and Quality Control 3: Edible Oil Structuring: Concepts, Methods and Applications 4: Food Irradiation Technologies: Concepts, Applications and Outcomes 5: Non-​extractable Polyphenols and Carotenoids: Importance in Human Nutrition and Health 6: Cereal Grain-​based Functional Foods: Carbohydrate and Phytochemical Components 7: Steviol Glycosides: Cultivation, Processing, Analysis and Applications in Food 8: Legumes: Nutritional Quality, Processing and Potential Health Benefits 9: Tomato Chemistry, Industrial Processing and Product Development 10: Food Contact Materials Analysis: Mass Spectrometry Techniques 11: Vitamin E: Chemistry and Nutritional Benefits 12: Anthocyanins from Natural Sources: Exploiting Targeted Delivery for Improved Health 13: Carotenoid Esters in Foods: Physical, Chemical and Biological Properties 14: Eggs as Functional Foods and Nutraceuticals for Human Health 15: Rapid Antibody-​based Technologies in Food Analysis 16: DNA Techniques to Verify Food Authenticity: Applications in Food Fraud 17: Advanced Gas Chromatography in Food Analysis

How to obtain future titles on publication: A standing order plan is available for this series. A standing order will bring delivery of each new volume immediately on publication.

For further information please contact: Book Sales Department, Royal Society of Chemistry, Thomas Graham House, Science Park, Milton Road, Cambridge, CB4 0WF, UK Telephone: +44 (0)1223 420066, Fax: +44 (0)1223 420247, E-mail: [email protected] Visit our website at www.rsc.org/​books

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Advanced Gas Chromatography in Food Analysis Edited by

Peter Q. Tranchida University of Messina, Italy E-mail: [email protected]

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Food Chemistry, Function and Analysis No. 17 Print ISBN:  978-​1-​78801-​127-​3 PDF ISBN:  978-​1-​78801-​575-​2 EPUB ISBN:  978-​1-​78801-​899-​9 Print ISSN: 2398-​0656 Electronic ISSN: 2398-​0664 A catalogue record for this book is available from the British Library © The Royal Society of Chemistry 2020 All rights reserved Apart from fair dealing for the purposes of research for non-​commercial purposes or for private study, criticism or review, as permitted under the Copyright, Designs and Patents Act 1988 and the Copyright and Related Rights Regulations 2003, this publication may not be reproduced, stored or transmitted, in any form or by any means, without the prior permission in writing of The Royal Society of Chemistry or the copyright owner, or in the case of reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency in the UK, or in accordance with the terms of the licences issued by the appropriate Reproduction Rights Organization outside the UK. Enquiries concerning reproduction outside the terms stated here should be sent to The Royal Society of Chemistry at the address printed on this page. Whilst this material has been produced with all due care, The Royal Society of Chemistry cannot be held responsible or liable for its accuracy and completeness, nor for any consequences arising from any errors or the use of the information contained in this publication. The publication of advertisements does not constitute any endorsement by The Royal Society of Chemistry or Authors of any products advertised. The views and opinions advanced by contributors do not necessarily reflect those of The Royal Society of Chemistry which shall not be liable for any resulting loss or damage arising as a result of reliance upon this material. The Royal Society of Chemistry is a charity, registered in England and Wales, Number 207890, and a company incorporated in England by Royal Charter (Registered No. RC000524), registered office: Burlington House, Piccadilly, London W1J 0BA, UK, Telephone: +44 (0) 20 7437 8656. Visit our website at www.rsc.org/​books Printed in the United Kingdom by CPI Group (UK) Ltd, Croydon, CR0 4YY, UK

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Preface The present book, Advanced Gas Chromatography in Food Analysis, has the main intent of providing in-​depth and broad information to all interested in the analysis of foods using gas chromatography (GC). Up-​to-​date information on the wide array of usable analytical options and possibilities is reported, along with basic principles, theory and practical aspects. All chapters are accompanied by pertinent GC-​related applications, covering a vast range of hot topics in the field of food analysis. The title of the book could lead to the conclusion that the GC separation of mixtures of volatile food constituents occupies the most important role amongst those composing the analytical chain. Due to the highly challenging nature of food analysis, in its multitude of applicational possibilities, such a conclusion would be erroneous. A satisfactory GC process must be accompanied by effective sample preparation, a sufficiently powerful form of detection and adequate use of the data. The sample preparation step can be skipped in only in a handful of food-​ product applications; for example, when a food is industrially extracted from its original source (e.g., through cold pressing, distillation, etc.) and is in a suitable form for direct GC analysis (e.g., essential oils, distilled beverages, etc.). Most often, matrix interferences must be reduced to protect the GC system from contamination, to avoid enhanced or suppressed detection responses and to reach the required levels of sensitivity (e.g., for the determination of phytosanitary compounds in vegetables, of dioxins in animal-​ derived products, of mineral oil contamination in seed oils, etc.). A distinction can be made between sample-​preparation methodologies applied to the extraction of volatile and less-​volatile compounds, with the latter being usually more labor-​intensive (Part 1). However, the attainment of reliable analytical results is strongly related to sample preparation, whatever process is applied. Food Chemistry, Function and Analysis No. 17 Advanced Gas Chromatography in Food Analysis Edited by Peter Q. Tranchida © The Royal Society of Chemistry 2020 Published by the Royal Society of Chemistry, www.rsc.org

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Proceeding onto the GC step, in many cases a single capillary column does not provide enough specificity and separation power to deliver sufficiently resolved analytes to the detection system. Only sometimes is it sufficient to change the type of stationary phase or to use a longer column to reach the required level of resolution (Part 2). In other cases, the use of more than one GC dimension (multidimensional GC), or the exploitation of a liquid chromatography (LC) step prior to the GC one, is a much more advisable option. In such respect, a considerable amount of attention is herein devoted to heart-​cutting (e.g., GC-​GC, LC-​GC) and comprehensive (e.g., GC×GC, LC×GC) multidimensional methodologies (Part 4). In particular, comprehensive two-​ dimensional GC is the most powerful analytical tool available today for the separation of volatile compounds, not only those related to foods. A faster GC separation is the main requirement whenever there is a need for a rapid analytical feedback, or when the daily sample throughput is high. There is a variety of options for high-​speed GC analysis, with the most common described herein (micro-​bore columns, vacuum outlet conditions, resistive column heating, short capillary columns) in both theoretical and practical terms (Part 3). Emphasis is devoted to high-​speed GC applications involving the use of mass spectrometry. Once prepared, a sample that is fully representative of the food of origin, and which has been subjected to a satisfactory GC-​based separation process, then reliable analyte-​to-​analyte identification and, often, absolute quantification must be achieved. The present contribution contains information relative to a variety of detectors used in the GC food analysis field, with particular focus on the two most powerful and important systems, one instrumental –​ mass spectrometry (MS) –​and the other biological –​olfactometry (O). An entire chapter is devoted to GC-​O, witnessing the fundamental contribution of this technology toward the full understanding of the impact of specific volatiles on food aroma (Part 5). With regard to MS, its fundamental role for the untargeted, pre-​targeted and post-​targeted analysis of volatile food constituents is highlighted (Part 2). The continuously growing importance and necessity of MS, in particular for the determination of food contaminants at very low concentration levels, is a factor accepted by all. Last, but certainly not least, Part 6 reports the basic principles and highlights the usefulness of chemometrics for experimental design and data treatment. In particular, it will be shown how suitable statistical approaches can be of great help within the context of important issues related to food, such as authenticity, quality, definition of geographical origin, etc., in one-​and multidimensional GC applications. I would like to conclude by saying a big thank you to the reviewers for their anonymous, albeit fundamental, role in enabling improvements to each chapter. I must also express my gratitude to all 20 authors for their support in this project and their enthusiastic participation. I do hope that the knowledge and personal experience that has been put by them into each chapter, along with my personal efforts as editor, are appreciated by the reader. Peter Q. Tranchida

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Contents Part 1: Novel and Conventional Sample Preparation Processes Chapter 1 Headspace Sampling: An “Evergreen” Method in Constant Evolution to Characterize Food Flavors through their Volatile Fraction E. Liberto, C. Bicchi, C. Cagliero, C. Cordero, P. Rubiolo and B. Sgorbini 1.1 Food Analysis and the Volatile Fraction: A General Introduction 1.1.1 Headspace Sampling Modes 1.1.2 Headspace Sampling: A Short History and Recent Evolution 1.1.3 Dynamic Headspace Sampling 1.1.4 Static and Trapped Headspace 1.1.5 High-​concentration Capacity Headspace Techniques 1.1.6 Headspace and Volatile Quantitation 1.1.7 A Short Overview of the S-​HS and HCC-​HS Quantitation Approaches 1.1.8 Headspace as a Tool for Fingerprinting and Profiling 1.1.5 Conclusions List of Abbreviations References Food Chemistry, Function and Analysis No. 17 Advanced Gas Chromatography in Food Analysis Edited by Peter Q. Tranchida © The Royal Society of Chemistry 2020 Published by the Royal Society of Chemistry, www.rsc.org

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Published on 16 October 2019 on https://pubs.rsc.org | doi:10.1039/9781788015752-FP007

Chapter 2 Sample Preparation for the Gas Chromatography Analysis of Semi-​volatiles and Non-​volatile Compounds in Food Samples M. L. Sanz and L. Ramos 2.1 Introduction 2.2 Extraction Techniques in Use for Food Analysis 2.2.1 Extraction Techniques for the Treatment of Liquid Samples 2.2.2 Extraction Techniques for the Treatment of Solid Samples 2.2.3 Derivatization 2.3 Representative Applications 2.3.1 Microcontaminants 2.3.2 Process-​generated Food Toxicants 2.3.3 Carbohydrates 2.3.4 Fatty Acids Acknowledgments References

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Part 2: Conventional Gas Chromatography Chapter 3 Conventional Gas Chromatography: Basic Principles and Instrumental Aspects Colin F. Poole 3.1 Introduction 3.2 Basic Principles 3.2.1 Retention 3.2.2 Band Broadening 3.2.3 Resolution 3.3 Sample Introduction 3.3.1 Hot Vaporizing Injectors 3.3.2 Cold On-​column Injectors 3.3.3 Programmed-​temperature Vaporizer Injectors 3.3.4 Large-​volume Injection 3.4 Column Technology 3.4.1 Wall-​coated Open-​tubular Columns 3.4.2 Stationary Phases 3.4.3 Chiral Stationary Phases 3.4.4 Porous-​layer Open-​tubular Columns 3.5 Detection Options 3.5.1 Ionization Detectors

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3.5.2 Bulk Property Detectors 3.5.3 Optical Detection References Chapter 4 Conventional Gas Chromatography: Mass Spectrometry Hyphenation and Applications in Food Analysis Hans-Gerd Janssen, Alan García Cicourel and Peter Q. Tranchida 4.1 Gas Chromatography–​Mass Spectrometry: Introduction 4.2 Gas Chromatography–​Mass Spectrometry: Principles and Instrumentation 4.2.1 Ionization Methods 4.2.2 Mass Analyzers 4.3 Applications in Food Analysis using GC-​MS 4.3.1 Single Quadrupole MS Applications 4.3.2 Time-​of-​flight MS Applications 4.3.3 Triple Quadrupole MS Applications  4.3.4 Hybrid MS Applications References

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Part 3: High-​speed Gas Chromatography Chapter 5 High-​speed Gas Chromatography: Basic Theory, General Principles, Practical Aspects and Food Analysis Peter Q. Tranchida and Luigi Mondello 5.1 Introduction 5.2 Basic Theory, General Principles, Practical Aspects and Food Analysis 5.2.1 Micro-​bore Columns 5.2.2 Vacuum Outlet Conditions 5.2.3 Resistive Column Heating 5.2.4 Short Capillary Columns References

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Part 4: Two-​dimensional Gas Chromatography-​based Processes: Principles, Practical Aspects and Applications in Food Analysis Chapter 6 Heart-​cutting Two-​dimensional Gas Chromatography Hans-Georg Schmarr 6.1 Definitions and Fundamental Considerations

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6.2 Technical Implementations for H/​C MDGC 6.3 Optimization in MDGC Applications 6.3.1 Benefits of Narrow Heart-​cut Windows 6.3.2 Overcoming Loss of Selectivity with Wide Heart-​cut Windows in the First Dimension by MS Detection 6.4 Applications of H/​C MDGC in Food and Flavor Analysis 6.5 Multidimensional GC in Authenticity Control: Enantioselectivity and Isotope Discrimination 6.6 Preparative H/​C MDGC 6.7 Future Perspectives: Chip-​based MDGC Acknowledgments References Chapter 7 Comprehensive Two-​dimensional Gas Chromatography Peter Q. Tranchida and Luigi Mondello 7.1 Introduction 7.2 Basic Theory, General Principles, Practical and Instrumental Aspects 7.2.1 Modulation Techniques 7.2.2 Column Optimization Aspects 7.2.3 Detection 7.3 Applications in the Field of Food Analysis 7.3.1 Mass Spectrometry Detection 7.3.2 Other Detectors 7.3.3 Hybrid Multidimensional Gas Chromatography 7.4 Conclusions References Chapter 8 Multidimensional LC-​GC M. Biedermann and K. Grob 8.1 Introduction 8.1.1 Why Online LC-​GC? 8.1.2 History of Online LC-​GC 8.2 Concepts for the GC Introduction of Large Sample Volumes 8.2.1 Split/​Splitless Injection

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8.3 On-​column Techniques 8.3.1 Solvent Trapping for Volatile Solutes 8.3.2 Reconcentration by the Retention Gap Effect 8.3.3 Solvent Vapor Exit 8.3.4 Partially or Fully Concurrent Eluent Evaporation? 8.3.5 Gas Discharge Versus Overflow 8.4 Problems with Water-​containing Eluents 8.5 Interfaces for Online Transfer 8.5.1 The Y-​interface 8.5.2 PTV Injector 8.5.3 Through Oven Transfer Adsorption Desorption Interface 8.5.4 Vaporizer Chamber/​Pre-​column Solvent Split Interface 8.6 Other Interfaces 8.6.1 On-​column Injector 8.6.2 Loop-​type Interface 8.6.3 Wire Interface 8.6.4 Swing Interface 8.7 LC-​GC Instrumentation 8.8 HPLC Pre-​separation 8.9 Summarized Description of the Two Preferred Transfer Techniques 8.9.1 Partially Concurrent Evaporation with the Y-​interface 8.9.2 Concurrent Eluent Evaporation with the Y-​interface 8.10 Applications 8.10.1 Mineral Hydrocarbons in Food and Related Samples 8.10.2 Analysis of Mineral Oil Products 8.10.3 Environmental Contaminants 8.10.4 Determination of Food Irradiation 8.10.5 Sterenes in Edible Oils 8.10.6 Sterols in the Unsaponifiable Fraction of Edible Oils 8.10.7 Isomerization of Δ7 sterols 8.10.8 Minor Components in Edible Oils 8.10.9 Methyl-​, Ethyl-​and Wax Esters in Olive Oil 8.10.10 Nervonic Acid in Meat-​derived Foods 8.10.11 Epoxidized Soybean Oil

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8.10.12 Pesticide Residues 8.10.13 Migration of Trimellitic Acid into Food 8.10.14 Flavor Compounds 8.10.15 Pharmaceutical Products 8.10.16 Organic Compounds in Water 8.10.17 Polymers and Additives 8.10.18 Comprehensive Two-​dimensional LC-​GC 8.11 Conclusions References

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Part 5: Gas Chromatography–​Olfactometry Chapter 9 Gas Chromatography–​Olfactometry: Principles, Practical Aspects and Applications in Food Analysis M. Steinhaus 9.1 Introduction 9.2 The Principle of Gas Chromatography–​Olfactometry 9.3 GC-​O: Practical Aspects 9.3.1 Sample Introduction 9.3.2 Column Parameters 9.3.3 Effluent Splitting and Sniffing Systems 9.3.4 The Sniffer in its Role as Human GC Detector 9.4 Sample Preparation Techniques Preceding GC-​O 9.4.1 Sample Homogenization 9.4.2 Steam Distillation 9.4.3 Solvent Extraction Methods 9.4.4 Solvent-​free Extraction Approaches 9.4.5 Headspace Sampling Techniques 9.4.6 Sample Preparation Techniques –​Conclusion 9.5 Odorant Ranking Approaches in GC-​O 9.5.1 Odorant Ranking by Intensity Measurement 9.5.2 Odorant Ranking by Detection Frequency 9.5.3 Odorant Ranking by Dilution to Threshold 9.6 Structure Assignment of Odorants Detected by GC-​O 9.7 GC-​O Data Interpretation and Substantiation 9.7.1 Limitations of GC-​O Results 9.7.2 Odorant Quantitation and Calculation of Odor Activity Values 9.7.3 Odor Reconstitution and Omission Experiments 9.8 Applications of GC-​O in Food Analysis 9.8.1 Using GC-​O to Discover Novel Odor-​active Compounds in Foods

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9.8.2 Using GC-​O to Substantiate Varietal Aroma Differences 9.8.3 Using GC-​O to Substantiate Off-​Flavors in Food 9.8.4 Using GC-​O for the Targeted Optimization of Food Processing 9.8.5 Using GC-​O for the Approximation of Odor Threshold Values in Air 9.9 Conclusion and Perspective Acknowledgments References

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Part 6: Chemometrics Chapter 10 Chemometrics: Basic Principles and Applications M. Casale, C. Malegori, P. Oliveri, E. Liberto, P. Rubiolo, C. Bicchi and C. Cordero 10.1 Basic Principles 10.1.1 Multivariate Design of Experiments 10.1.2 Data Pretreatment 10.1.3 Pattern Recognition Methods 10.1.4 Classification and Class-​modeling Methods (Supervised Pattern Recognition) 10.1.5 Regression Methods (Supervised Pattern Recognition) 10.2 Applications in Food Analysis Involving 1D and 2D GC Separations 10.2.1 Multivariate Design of Experiments in Food Analysis 10.2.2 Chemometrics in Food Analysis Data Elaboration: Overview 10.2.3 Data Analysis Challenges in Omics Investigations 10.2.4 Future Perspectives and Innovative Approaches Acknowledgment List of Acronyms References Subject Index

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Part 1 Novel and Conventional Sample Preparation Processes

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CHAPTER 1

Headspace Sampling: An “Evergreen” Method in Constant Evolution to Characterize Food Flavors through their Volatile Fraction E. LIBERTO, C. BICCHI*, C. CAGLIERO, C. CORDERO, P. RUBIOLO AND B. SGORBINI Laboratory of Pharmaceutical Biology and Food Chemistry, Dipartimento di Scienza e Tecnologia del Farmaco, Via Pietro Giuria 9 –​I-​10125 Torino, Italy *Email: [email protected]

1.1 Food Analysis and the Volatile Fraction: A General Introduction The volatile fraction of a food plays a fundamental role in its characterization and its acceptance and appreciation by consumers. In chemical terms, the volatile fraction of a matrix of vegetable origin can be defined as a mixture of volatiles, which can be sampled because of their ability to vaporize spontaneously, and/​or under suitable conditions, or by adopting appropriate techniques.1–​3 In general, the term volatile fraction is therefore an umbrella term

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including a group of approaches and/​or techniques that produce samples representative of the volatiles characterizing a food matrix, which may have different and mutually non-​comparable compositions, however; e.g., headspace, essential oils, aromas, flavors, fragrances and extracts obtained by specific techniques. In the food field, the volatile fraction plays a fundamental role in the flavor definition of a foodstuff. The concept of flavor involves a holistic description of food perception, as indicated by the International Standards Organization definition.4 According to that definition, flavor is a “complex combination of the olfactory, gustatory and trigeminal sensations perceived during tasting. The flavor may be influenced by tactile, thermal, painful and/​or kinesthetic effects”. Flavor, therefore, necessarily entails the involvement of a biological interaction (mainly related to the sensory field) that, for the perception of aroma and taste, is induced by the interaction of bioactive molecules with chemoreceptors located in the nose and on the tongue. The volatile fraction of a food is at the origin of its aroma, which can be defined as that combination of volatiles that can be perceived both orthonasally and retronasally by the odor receptor sites of the smell organ, i.e., the olfactory tissue of the nasal cavity (known as regio olfactoria). Its importance is testified by studies examining crossmodal interaction in actual perception, suggesting that for some foodstuffs up to 80–​90% of the taste comes from the nose.5,6 These basic assumptions closely fit the guiding principles of omic sciences, and in particular of metabolomics. This discipline was defined by Oliver et al. in 1998 as the systematic study of the unique chemical metabolite fingerprints (the metabolome) resulting from specific cellular processes.7 However, metabolomics, used in combination with sensory perception, is too general a discipline to fully meet the specific needs of the food field, whose final goal is the objectification of aroma and taste on a molecular basis. This need was met by Schieberle and Hofmann, who, in 2011, introduced “Molecular Sensory Science” or “Sensomics”, a subdiscipline whose aim is to identify key food aroma and taste compounds at the molecular level, and to map the combinatorial code of aroma and taste active key molecules sensed by human chemosensory receptors and integrated by the brain.8,9 The practical fallout of this concept is the so-​called flavor blueprint or flavor signature of a food, i.e., the combinatorial code of the entire set of odor-​and taste-​active food components in their natural concentrations in the food.8,9 Taken together, these definitions make it possible to be in line with the more general goal of omic sciences, i.e., to achieve the so-​called higher level of information. This is information in which the chemical data resulting from a research investigation are suitable to describe directly a biological or quality characteristic of the matrix under investigation. In analytical terms, metabolomics, and thus sensomics, implies the comprehensive and quantitative analysis of the largest possible array of low-​ molecular-​weight components ( uopt) are desirable for fast separations and become a de facto acceptable compromise if the loss of efficiency is tolerable (< 10%). That being said, for thin-​film columns hydrogen and to a lesser extent helium, is the preferred choice of carrier gases for gas–​liquid chromatography. Thin-​ film columns are typically operated at a mobile-​phase velocity of about 2uopt. For thick-​film columns (df > 1 μm) the contribution to the plate height from slow mass transfer in the stationary phase is no longer negligible. The uopt value is reduced by about a factor of 2 and Hmin shows a dependence on the retention factor. The ascending portion in the van Deemter plot at the high-​ velocity side is also steeper than for thin-​film columns for all three carrier gases. Choice of the optimum carrier gas is less straightforward, but the differences for the three carrier gases are not great and nitrogen is a reasonable choice for many applications due to its lower Hmin. The concept of a column plate number and a plate height are not applicable for temperature-​programmed separations.20,22 Compounds well-​retained at the start of a linear temperature program have roughly similar peak widths after separation, with a wide range of retention times. In these circumstances, the plate number is a peak-​specific property and not a column parameter.

3.2.3 Resolution The separation of any two peaks in a chromatogram depends on both their peak to peak separation and their peak widths. Numerically, this is expressed 1 N2 He

H 0.5

H2 0

0

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40 60 Average Velocity

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Figure 3.1 Simulated van Deemter plot for a thin-​film wall-​coated open-​tubular column with hydrogen, helium or nitrogen as carrier gas. Plate height H (mm) and average linear velocity (cm s–​1).

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as their resolution, RS, given by RS = 2∆t /​(wb1 + wb2) for Gaussian peaks, where ∆t is the difference in retention time for the two peaks and wb1 and wb2 are the peak widths at the baseline. For peaks of similar height RS = 1 corresponds to 94% valley separation and is adequate for quantification (RS = 1.5 corresponds to baseline separation). For peaks of significantly different height and/​or asymmetric peaks a larger value of RS is required for acceptable separation. The main use of resolution is as a target parameter to assess the success of changing separation variables during method development. For multicomponent mixtures a critical pair (or pairs) is identified as the target value and the separation of all other peak pairs will exceed this minimum value. Several models have been proposed to relate RS to the experimental variables that control efficiency, selectivity and the separation time.33,34 Two of the most widely used models are



 N   α − 1   k AV  RS =    (3.5)   2   α + 2   1 + k AV 



 N   α − 1   k2  RS =    (3.6)   4   α   1 + k2 

where k1 and k2 are the retention factors for the two peaks in order of elution and kAV their average value. For both models it is assumed that the peaks are symmetrical and near neighbors with a low RS value. For eqn (3.6) it is further assumed that the average of the two peak widths is identical to the peak width of the second peak. To a first approximation the three contributions to RS in eqns 3.5 and 3.6 can be treated as independent. Resolution can always be improved by increasing the column plate number, but only increases as the √N. Increasing N by increasing the column length has a limited impact on RS and a significant penalty in terms of the separation time. Doubling the column length will only increase N by √2 while doubling the separation time. Reducing the column internal diameter while maintaining the same column length is the better option, as can be seen from the typical column data presented in Table 3.3. The most powerful approach to increase RS is to increase the separation factor α. The minimum useful separation factor for gas chromatography is α ≈ 1.005 but will require a column with N ≈ 106 to reach a RS ≈ 1 and will not be easy to achieve. A separation factor of α ≈ 1.05 only requires N ≈ 104 and is not considered difficult as this value falls within the range of values for columns in general use. Although large changes in α are not required to improve RS it does require selection of a different stationary phase. The latter is identified either intuitively or empirically by trial-​and-​error experiments. Although low retention factors favor fast separations, a minimum retention factor value for the most difficult to separate peak pair is required. Resolution is impossible without retention (k ≈ 0) but only a modest value (k ≈ 4–​8) is required for near full resolution, while RS changes only slowly for k

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> 10 and so larger retention factors do not lead to a significant increase in resolution. Just as important, when some of the critical pairs in a separation are moved beyond the optimum retention factor range to achieve the necessary separation capacity for the analysis, these critical pairs do not undergo a significant loss of resolution. A general dilemma for varied compounds is that sensible changes made to improve RS for a particular peak pair (or pairs) will likely result in a lower resolution of other peak pairs (except for changes in the column plate count). In general, increasing the plate count is the easy option to improve RS while selectivity optimization is the more powerful approach, although procedurally less predictable.

3.3 Sample Introduction Once packed columns had given way to open-​tubular columns, sample introduction became an issue on account of the lower sample capacity, lower column flow rates and the need for narrower injection bands at the column inlet for open-​tubular columns compared with the more forgiving properties of packed columns. Microliter syringes to dispense sample volumes and silicone septa to seal the sample entry point largely remained the same. Microliter syringes facilitate handling solution volumes in the 0.1–​10 μL range but with poor volume precision. Gas-​tight syringes of larger volume are available for handling samples in the gas phase, but fixed-​loop injection valves offer higher volume and mass precision and are preferred for gas analysis. The main concern for syringe injection with hot vaporizing injectors for open-​ tubular columns is mass discrimination. This results from the distillation of the sample from the syringe needle after the plunger completes its movement to the base of the barrel.35 At the completion of the injection sequence a significant fraction of the sample volume remains trapped in the syringe needle. Sample components then distill from the needle according to their relative vapor pressure and exit the syringe needle over a longer time compared with the sample volume dispensed from the needle as droplets. Typically, sample components of lower volatility are denuded from the sample chromatogram compared with the actual sample composition. Silicone septa have remained indispensable as a durable seal for injectors. They allow multiple injections without backflushing of the sample vapors from the needle injection area. Improvements in the manufacturing process have minimized outgassing of volatile components that appear as contaminants or ghost peaks in the chromatogram. Various valve devices for septemless injection have been proposed and a few commercialized without proving popular. In hot vaporizing injectors, the sample is released by syringe into a glass or quartz liner (pre-​column volume) where it is vaporized and mixed with carrier gas. Numerous liner designs have been proposed, several of which are commercially available, but the most common type are straight glass tubes of various dimensions, in some cases supplemented with a plug of deactivated glass or quartz wool.36 Alternatively, the incorporation of baffles of various types instead of glass/​quartz wool is another possibility. The volume of the injection

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liner should be sufficient to hold the sample volume after evaporation. The glass/​quartz wool aids sample evaporation and traps involatile sample residues. It has a high surface area that favors rapid evaporation of the sample but at the same time may contribute to the activity of the liner resulting in the decomposition of labile sample components. When used with an autosampler the glass/​quartz wool plug serves to wipe the outside of the syringe needle during withdrawal minimizing the loss of sample at the septum seal.37 Autosamplers are no longer looked upon as a luxury item. As well as facilitating the unattended operation for multiple samples typically housed in a carousel at addressable positions, they also afford higher repeatability compared with manual injection and avoid some of the issues associated with discrimination using hot vaporizing injectors. Autosamplers are able to complete the syringe introduction, sample release and syringe withdrawal sequence in less than 100 ms, with each step performed with high precision. With an autosampler the repeatability of sample injection is typically better than 1–​2% RSD (relative standard deviation) while for manual injection < 2–​4% RSD is only achievable with difficulty.37 Autosamplers that allow scheduled automated exchange of contaminated injection liners are available.38 The injection liner is an important factor that influences the chromatography of labile and easily adsorbed compounds. Glass surfaces are generally active and the accumulation of involatile sample residues in the liner can increase its activity. Most liners are typically deactivated by silanization, but this is not usually adequate to eliminate all activity and the durability of the deactivation is also a concern once the liner is placed into service. An all-​too-​ common phenomena for polar compounds in complex matrices is matrix-​ induced response enhancement.39 Observed originally for hot vaporizing injectors with organophosphorus pesticides in food extracts, it is now established as common for many polar compounds and other matrices. For samples with a significant matrix burden, vaporization of matrix and analyte occur at the same time. The larger amount of matrix deactivates active surfaces in the injector (or reduces the thermal shock to labile compounds) allowing more analyte to be transferred to the column than for matrix-​free samples or standards. The problem is amplified when standards prepared in neat solvent are used for calibration of samples with a significant matrix burden. A practical solution to this problem is matrix-​matched calibration standards or addition of analyte protection agents to calibration standards and samples.39 For matrix-​matched calibration an analyte-​free matrix is used to prepare the calibration standards and is often the best approach. Typical analyte protection agents are 3-​ethoxy-​1,2-​propanediol for volatile pesticides, gulonolactone for pesticides of intermediate volatility and sorbitol for pesticides of low volatility.40 Analyte protection agents are used in a similar manner to analyte-​free matrix for standard preparation. Alternatively, improved sample clean-​up can minimize the matrix effect, but is not always practical. Retention gaps are used to facilitate the injection of samples with a heavy matrix burden.41,42 A retention gap is a length of uncoated (usually deactivated) capillary tubing placed between the injector and separation column

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with the required dimensions to accommodate the sample volume and injection technique. Separation columns can be purchased with a retention gap prepared as a column inlet, otherwise a separate pre-​column is attached to the injector and separation column by low-​dead-​volume fittings. Retention gaps are considered disposable and replaced periodically depending on sample properties. As well as acting as a guard column minimizing contamination of the separation column by involatile and particulate matter, it has a number of other functions, including facilitating cold trapping and solvent effects due to its reduced retention power compared with the separation column and as an essential component for large volume injections (> 5 μL). Wide-​bore retention gaps facilitate syringe alignment and large volume injection using an autosamplers with narrow-​bore separation columns. Most analysts would agree that a retention gap is an essential component for the analysis of food extracts, and in fact, for most samples. Before considering the common sample inlets for open-​tubular columns, a brief mention of largely automated systems that integrate the processes of sampling and separation into a single operation deserve mention. Among these static and dynamic headspace analyzers, thermal desorption devices, pyrolysis devices, and coupled extraction and separation devices, for example, liquid chromatography–​gas chromatography (LC-​GC) and supercritical fluid extraction–​gas chromatography (SFE-​GC) serve as examples.28,37 Subsequent chapters will provide details of specific applications for food analysis. No single sample inlet provides a suitable solution for all sample types. Important considerations are: the injected volume; analyte concentration, thermal stability, and volatility range; the concentration of low-​volatility matrix components; and the target method accuracy and precision. General reviews provide some insight into the selection of a particular sample inlet based on the problem at hand.35,37,41,44-​46

3.3.1 Hot Vaporizing Injectors The split/​splitless injector, Figure 3.2A, is the most common example of an isothermal hot vaporizing injector. It is, in fact, two injection techniques supported by a common injector body requiring only minor changes in configuration to switch from one technique to the other.28,35,37 The split injector was the first injector compatible with open-​tubular columns. It consists of an isothermal vaporizing chamber, in which the evaporated sample is mixed with carrier gas and divided between two streams of different flow rate, one entering the column (carrier gas flow) and the other vented to the outside (split flow and septum purge flow). The flow of gas to the injector inlet is controlled by a mass flow controller and a backpressure regulator in the split vent line controls the inlet pressure. This configuration provides tighter control of retention repeatability and split ratio linearity compared with forward pressure regulation still found on some older instruments. The purpose of the septum purge is to prevent septum bleed products and contaminants from entering the vaporizing chamber. Appropriate column loads are achieved by injecting sample volumes

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of 0.2–​2.0 μL by syringe with split ratios between 1 : 10 and 1 : 1 000 (typically 1 : 20 to 1 : 200). For mixtures with a wide volatility range split injection discriminates against the less-​volatile sample components due to selective evaporation from the syringe needle (minimized by fast injection using an autosampler) and from incomplete evaporation and inhomogeneous mixing of sample vapors with the carrier gas in the vaporization chamber. The residence time of the sample in the vaporization chamber is insufficient for the transfer of sufficient heat to the sample to complete vaporization and the sample arrives at the split point only partially evaporated as a mixture of vapor and liquid droplets of various sizes. This results in a variable split ratio and variation in the amount of individual sample components entering the column. This is the main cause of difficulties in quantitative analysis and control of all aspects of the injection process is important for acceptable repeatability. Autosamplers accomplish these tasks better than manual injection. Among the strengths of split injectors are that they allow the injection of mixtures virtually independent of the sample solvent and column temperature with minimal risk of distorted peaks due to solvent effects or band broadening in time, discussed later. They favor the formation of narrow injection bands desirable for column characterization and have a simple and rugged design. On the other hand, they are not well-​suited for trace analysis due to low sample utilization, discrimination of sample components occurs for mixtures of wide volatility, and quantification is more difficult than for other injectors and requires careful control of a large number of experimental parameters.28

A

B

Figure 3.2 Schematic diagrams of a hot split/​splitless injector (A) and a programmed-​temperature vaporizer injector (B) for sample introduction using open-​tubular columns.

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Splitless injection, using a split/​splitless injector, requires the installation of a suitable inlet liner of sufficient volume to hold the evaporated sample volume and software changes to set the flow of gas through the liner to the carrier gas flow rate and to time control the initiation of the split flow some time after sample introduction.28,35,37 Splitless injectors equipped with a programmable electronic pressure controller allow pressure-​pulsed splitless injection.47 In this technique a high flow rate through the inlet at the time of injection is followed by a rapid reduction of pressure to restore the desired carrier gas flow rate. The advantage of pressure-​pulsed splitless injection is that it allows a larger sample volume, typically ≈ 5 μL, to be injected into a standard 1-​mL injection liner compared with < 2 μL for normal splitless injection. For standard splitless injection the flow of gas through the vaporization chamber is the same as the carrier gas flow rate. Consequently, the transport of sample vapors to the column is relatively slow and the volume of the injection liner must be large enough to hold the entire volume of sample vapor at the column inlet pressure. The rapid evaporation of the sample causes an expansion of typically 100–​1 000 fold to vapor depending on the sample solvent (expansion volumes increase with the polarity of the sample solvent). Sample transfer to the column is slow (several seconds up to a few minutes) and for accurate quantification should ideally be complete. This is difficult to achieve, however, because the sample vapors are continually diluted with fresh carrier gas and the sample concentration at the column inlet is reduced over time. At some point the transfer of sample to the column becomes too slow to justify extending the time for sample introduction. At the end of the practical sample transfer period, a purge flow (split flow initiated) is set to remove the diluted vapors from the injection liner. If the purge flow is started too soon sample will be lost; if too late, the solvent front is distorted with a long tail that may overlap with early eluting peaks in the chromatogram. As a general guide, the purge flow should be started at about twice the time from the start of injection for the carrier gas to sweep out the volume of the injection liner. It is optimized by a small number of trial-​and-​error experiments observing the shape of the solvent front profile. The time for sample introduction in splitless injection is long compared to typical peak widths desired for separations and the injected sample is possibly distributed over a column length greater than desirable peak widths at the column outlet. These processes are referred to as band broadening in time and band broadening in space, respectively. If uncorrected, subsequent separations are of little practical use. Band broadening in time is characterized by a constant broadening of all peaks in the chromatogram, while band broadening in space is characterized by peak broadening that increases proportionately with retention and may be accompanied by peak splitting. Splitless injection is possible because of one of two available refocusing mechanisms that counteract these band broadening processes, and is facilitated in most cases by using a retention gap. Band broadening in time is counteracted by temporarily increasing the retention power of the column inlet during sample introduction. This can be

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achieved by lowering the column temperature, called cold trapping, or by a temporary increase of the retention power of the column inlet with the injection solvent acting as a temporary stationary phase, called solvent effects. Cold trapping is frequently used with temperature-​programmed separations under conditions unfavorable for solvent effects. In general, the temperature of the column inlet (oven temperature at the time of injection) must be about 15°C higher than the solvent boiling point at the column inlet pressure to avoid condensation of the sample solvent at the inlet (absence of solvent effects). In addition, the minimum temperature difference between the elution temperature during the separation and the column inlet temperature at injection must be at least ≈ 80°C (effective cold trapping). In essence, the effectiveness of cold trapping as a focusing mechanism depends on the ratio of the migration speed of the analytes at the injection and elution temperatures. It does not imply that the column inlet requires separate cooling to subambient temperatures, only that the migration velocity of the analytes at the injection temperature is effectively zero. For compounds of moderate and low volatility this is easily achieved at convenient column temperatures for temperature-​ programmed separations. Solvent effects depend on the recondensation of the sample solvent at the column inlet (or retention gap) to form a temporary film of sufficient retention power to delay migration of the sample. For this to occur, the sample solvent should have a high boiling point with respect to the column inlet temperature and a low boiling point with respect to the sample. The sample solvent must also wet the stationary phase (retention gap) and be a good solvent for the sample if it is to form a continuous film at the column inlet with enhanced retention power. The effectiveness of solvent effects as a refocusing mechanism depends on the speed of solvent evaporation from the temporary liquid film relative to the migration speed of the sample through the film. The solvent film evaporates from the column inlet end faster than from the deposited film further along the column as fresh carrier gas entering the column becomes saturated with solvent vapor and the film further along the column remains stable. The process is continuous with the film being stripped from the inlet end moving along the column and the sample dissolved in the film migrating ahead of the evaporating solvent film until the last drop of solvent is evaporated and the sample is refocused into a narrow band. Solvent trapping is an effective mechanism for volatile analytes with an elution temperature no more than 50°C above the column inlet temperature. Partial solvent trapping results in poor chromatography with peaks that may be distorted and unrecognizable as such. Optimization of the injection conditions generally requires greater effort than for cold trapping. Hot splitless injection is preferred to split injection for the analysis of trace components without pre-​concentration and for the analysis of dirty samples, such as food extracts, because the injector is easily dismantled for cleaning. The repeatability of retention times in splitless injection is not as good as for split injection because absolute retention times are influenced by the experimental variables that control the injection process. Precision is acceptable for

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quantitative analysis, but the technique is subject to numerous systematic errors that may affect accuracy. The long residence time of the sample vapors in the hot injector tends to magnify problems associated with thermally labile compounds and adsorption phenomena such as matrix-​induced response enhancement. Notwithstanding these problems, most official and regulatory methods for minor and trace components in complex matrices such as food extracts specify splitless injection for sample introduction. With suitable attention to optimization and control of experimental variables, injector performance is satisfactory.48 In recent times there has been a trend to favor the programmed temperature vaporizer over hot splitless injection for trace analysis. A hot split/​splitless injector can be converted to a direct injector by installing a special gooseneck liner with a direct connection between the column and the liner at the bottom end and turning off the split vent, or as a hot on-​column injector using a straight liner with a single restriction at the top where the column seal is made. During injection, the syringe needle partially constricts the restriction at the top of the liner, preventing sample vapors escaping backwards into the septum purge and carrier gas lines. Direct injection is possible with wide-​bore capillary columns and flow rates of 5–​15 mL min–​1. Injection volumes are typically 2–​8 μL and relatively high flow rates are necessary to sweep the sample onto the column in a reasonable time. Because it is used only with wide-​bore columns of comparatively low efficiency, poor injection conditions may go unnoticed, so it may seem there are fewer problems than for hot splitless injection. In reality, it is subject to similar limitations with respect to matrix-​induced response enhancement and syringe handling issues.

3.3.2 Cold On-​column Injectors Cold on-​column injection is suitable for the quantification of most sample types with a low-​matrix burden because the sample is released as liquid into the column or retention gap without prior vaporization.28,37,41 This requires that the temperature of the column inlet is maintained below the boiling point of the sample solvent. It is virtually free of discrimination for mixtures with a wide volatility range. Quantitative accuracy and repeatability (< 1% RSD) for automated injectors is excellent using an internal standards to correct for differences in volume delivery.49 Manual injection is more difficult requiring a syringe holder in the form of a scaffold to guide the fragile syringe needle into the column, for positioning the needle at the desired location within the column, and for sealing the needle passageway, or at least restricting the flow of carrier gas out through the syringe entry port. Forced air cooling or a separate temperature controlled injector oven within the column oven is used with some designs to maintain the temperature of the needle passageway below the solvent boiling point. Sample volumes of about 0.2–​1.5 μL are typically used for single-​shot injection. Much larger volumes are possible using techniques for large-​volume injection (see Section 3.3.4). On-​column injection is not as widely used today as it was a decade ago and has been replaced by the more flexible programmed-​temperature vaporizer for most applications

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while retaining similar accuracy. For the quantitative analysis of triglycerides in refined oils and fats it has remained competitive, but the general problem of column contamination and frequent maintenance resulting from dirty extracts detracts from its general use.51

3.3.3 Programmed-​temperature Vaporizer Injectors The programmed-​temperature vaporizer (PTV) can be used as a split/​splitless injector for normal sample volumes, as a large-​volume injector, as a device for automated liquid-​or vapor-​phase (headspace) sample introduction, and as an interface for a number of sample preparation devices. The PTV (Figure 3.2B) is as close as exists to a universal injector for open-​tubular col​umns.28,37,52,53 The injector body has a low thermal mass and a smaller vaporizing chamber (liner) compared with hot split/​splitless injectors. This allows its temperature to be rapidly changed by resistive heating or circulating hot air (10–​15°C s–​1) or cooled by circulating cool air, a Peltier element, or expanding carbon dioxide vapors. For split injection the sample is introduced into the liner at a temperature below the solvent boiling point with the split exit open. Shortly after, the syringe needle is withdrawn the injector is ballistically heated to a higher temperature to vaporize the sample. Discrimination effects are reduced compared with a hot split injector and the preset split ratio and the actual split ratio are nearly the same. For cold splitless injection the sample is introduced into the vaporizing chamber at a temperature close to the solvent boiling point with the split vent closed. Momentarily after the syringe needle is withdrawn the vaporizing chamber is ballistically heated to a higher temperature to complete the transfer of vapors into the column and solvent residues purged from the vaporizing chamber by opening the split vent. Similar to hot splitless injection, cold trapping and solvent effects are employed as refocusing mechanisms. The precision and accuracy of the PTV injector are generally superior to hot classical split/​splitless injectors and similar to cold on-​column injection.49–​53 Compared with cold on-​column injection it is better suited to the analysis of samples with a heavy matrix burden, such as traditional food solvent extracts. Food extracts prepared with polar solvents may contain minerals and polyfunctional compounds of low volatility or stability, and low-​polarity solvents, high-​mass oils and fats of low volatility that accumulate in the injection zone, eventually resulting in peak tailing and/​or adsorption of analytes using cold on-​column injection.

3.3.4 Large-​volume Injection Large-​volume injection is used to concentrate dilute samples by solvent evaporation or as an interface for liquid-​phase separation/​extraction techniques. The most common approach is solvent evaporation in the vaporizing chamber of a PTV injector or retention gap of a cold on-​column injector.41,54–​56 The PTV injector can be operated as a cold split injector with solvent elimination,

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as a splitless injector with vapor discharge, or as a hot splitless injector with vapor overflow for solvent elimination.54 For cold split injection the sample is introduced into the PTV injector at a temperature close to the solvent boiling point with the split vent open. The solvent is evaporated in the injector liner and swept out through the split vent. When solvent evaporation is complete, the split vent is closed and the injector ballistically heated to vaporize the sample and transfer it to the column. The injector liner is packed with glass wool to enhance the retention of analytes by cold trapping and solvent effects during solvent elimination. Cold split injection is restricted to analytes with a significantly higher boiling point than the sample solvent unless the liner is packed with an adsorbent of sufficient capacity to retain volatile analytes. The maximum sample volume that can be handled depends on the size of the liner and typically is about 20–​150 μL for a single-​shot injection or 1 mL by speed-​controlled injection. For splitless injection the sample is introduced into the vaporizing chamber at a temperature close to the pressure corrected solvent boiling point with the split vent closed. Most of the expanding solvent vapor and volatile sample components escape through the septum purge outlet while low-​volatility sample components are retained in the low-​temperature zone created by the evaporating solvent. Tenax or silanized glass wool can be used as packing material to increase the retention capability of the liner. After solvent elimination the temperature of the liner is rapidly increased to transfer the analytes to the column. Large-​volume injection using a cold on-​column injector requires a retention gap and preferably installation of an early solvent vapor exit before the separation column. The sample is introduced into the retention gap at a controlled speed, slightly higher than the solvent evaporation rate at a temperature below the pressure corrected solvent boiling point. This ensures the formation of a solvent film on the wall of the retention gap. Partial concurrent solvent evaporation allows a large fraction of the solvent to be evaporated during the injection. Analytes of low volatility are distributed over the length of the solvent film in the retention gap and are refocused by cold trapping at the column inlet (by solvent effects for volatile analytes) after completion of the solvent evaporation. Because many detectors are intolerant of large volumes of solvent vapors an early vapor exit is installed to divert them away from the separation column. Up to about 1 mL of sample can be transferred to the column with partial concurrent solvent evaporation with about three-​quarters of the solvent vapors eliminated through the early solvent vapor exit. A retaining pre-​column can be installed between the retention gap and separation column to reduce the loss of volatile analytes. Working conditions are established taking into account the speed of injection, the dimensions of the retention gap, injection temperature and the solvent evaporation rate.

3.4 Column Technology Open-​tubular columns have become consumable items and few scientists today have sufficient knowledge or skill to prepare self-​ made columns.

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Column manufacturers rarely disclose the details of incremental changes in column chemistry and improvements in the manufacturing process that occur over time, with the result that there are gaps in the knowledge of typical consumers about the products they rely upon. The following discussion is no different, and after a brief description of general column preparation procedures a more focused look at stationary phase properties and column selection for method development will follow. Although a variety of column types have been employed historically in gas chromatography (see Table 3.1), from a contemporary viewpoint only wall-​coated open-​tubular columns (WCOT) and porous-​layer open-​tubular columns (PLOT) need concern us here. The WCOT column is used ostensibly for gas–​liquid (partition) chromatography and the PLOT column for gas–​ solid (adsorption) chromatography. The most important and versatile are the WCOT columns.

3.4.1 Wall-​coated Open-​tubular Columns Wall-​coated open-​tubular columns are manufactured in a multistep process consisting of surface treatment, deactivation, coating, immobilization of the stationary phase and finally column conditioning.28,43,57–​59 An alternative process based on sol-​gel technology combines column coating, deactivation and immobilization into a single-​step procedure through chemical bonding of the stationary phase to an interfacial organic–​inorganic polymer layer that evolves on top of the original capillary surface.60 Sol-​gel coated columns have acceptable and similar performance to columns manufactured by the traditional multistep process, but remain a minor segment of the column market. For general laboratory applications WCOT columns are prepared from fused-​silica capillary tubing drawn in purpose-​built draw towers in a clean room environment.43 The thin-​wall columns are fragile and externally coated by a protective film of poly(imide) or aluminum during the drawing process, which acts as an atmospheric barrier. The coated columns are mechanically strong, flexible and inherently straight, allowing long lengths of tubing to be wound onto a metal framework for convenient installation in typical column ovens. The straight ends facilitate connection to the injector and detector with simple fittings usually containing a ferrule to make a leak-​tight seal. The interior wall of fused-​silica columns is relatively inert and has a well-​defined and (reasonably) reproducible surface chemistry. Various simple conditioning and hydrothermal treatments are used to remove acid impurities created during the drawing process and to re-​hydroxylate the surface of the capillary columns to provide a reproducible concentration of silanol groups important for column preparation. Because the columns are prepared from high-​purity silica there is almost negligible contamination with metal impurities (in the low ppm range), unlike other glasses. Clean and re-​hydroxylated fused-​silica surfaces coated with low-​polarity stationary phases exhibit undesirable properties for gas chromatography, such as tailing, adsorption or degradation of polar analytes, without

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prior deactivation. Deactivation is achieved using one of several high-​ temperature silylation reactions employing disilazanes, cyclosiloxanes, thermal degradation of a poly(siloxane), or condensation of a poly(hydrosiloxane).61 These reactions result in a decrease in the number of silanol groups on the deactivated surface and the formation of a protective film over the surface diminishing access to the remaining free silanol groups. The structure of the silylation reagents are often varied to promote adequate wetting of the deactivated surface by the stationary phase. Propriety polar deactivating agents are used to reduce undesirable interactions with basic compounds, such as amines.62 Stationary phases useful for the preparation of WCOT columns are characterized by high viscosity, high diffusivity, good solvent properties, low chemical reactivity, a wide temperature operating range, and facilitate crosslinking and other immobilization techniques.28,59,63 These properties tend to dictate the use of high-​purity and high-​molecular-​mass polymers prepared from siloxane or ethylene glycol monomers63 or ionic liquids.64 For a high separation efficiency it is essential that the stationary phase is deposited as a smooth, thin and homogeneous film that resists droplet formation as the column temperature is varied. A major development in column technology was the realization that crosslinking of gum phases to form a rubber provided a means to further stabilize poly(siloxane) and poly(ethylene glycol) stationary phases. Two general approaches are used to immobilize poly(siloxanes). Thermal immobilization of silanol-​or methoxy-​terminated low-​polarity stationary phases28,65 and radical-​initiated crosslinking of endcapped poly(siloxanes), poly(ethylene glycols) and some ionic liquids.28,66 Thermal immobilization results in the simultaneous bonding of the polymer to the column wall and the formation of crosslinks between polymer chains. Low-​polarity stationary phases of this type exhibit exceptional thermal stability and are used in high-​temperature gas chromatography. Immobilization by free-​radical crosslinking is achieved by the addition of a source of free radicals, such as peroxides or azo-​compounds, or less commonly by γ-​radiation. In these processes, crosslinking occurs by formation of carbon–​carbon bonds between neighboring polymer chains. Minimal crosslinking is sufficient for immobilization (0.1–​1.0%). The process is less efficient for poly(siloxanes) prepared with polar monomers and the incorporation of small amounts of vinyl, tolyl or octyl containing monomers is required. The term “stabilized” is often used to describe stationary phases that are incompletely immobilized. These columns have higher thermal stability than physically coated columns but are not as resistant to phase stripping or as durable as fully immobilized stationary phases. Immobilization allows the preparation of thick-​film columns with a more convenient range of retention factors for volatile compounds at moderate column temperatures. It also improves the resistance of the stationary phase to phase stripping facilitating large-​volume injection techniques. A new generation of low-​bleed stationary phases based on silarylene–​ siloxane copolymer chemistry was developed for use with bleed-​sensitive detectors, such as the mass spectrometer. The thermal stability of linear

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poly(siloxanes) is enhanced by inserting monomers containing stiffening groups (e.g., phenylene, carborane, diphenylene ether, etc.) into the siloxane backbone.28,63 The stiffening groups reduce the chain flexibility and inhibit attack by terminal silanol groups on a silicon atom in the same chain with release of small cyclic siloxanes and formation of a new silanol group to restart the process. This process is referred to as “backbiting” and is the most common cause of thermal degradation for linear poly(siloxanes). The thermal stability of poly(siloxanes) is also enhanced if the monomer groups alternate along the backbone. For example, a poly(methylphenylsiloxane) containing dimethylsiloxane and diphenylsiloxane monomers is more stable than a polymer containing solely methylphenylsiloxane monomers.

3.4.2 Stationary  Phases Different stationary phases are required to vary selectivity but the number of liquids with suitable properties to prepare durable and thermally stable pre-​ coated columns is limited to poly(siloxanes), poly(ethylene glycols) and ionic liquids. The selectivity of a stationary phase is determined by its relative capacity for specific intermolecular interactions, such as dispersion, induction, orientation and hydrogen-​bonding, as well as its cohesive energy. The transfer of a compound from the gas phase to a stationary phase occurs with the disruption of solvent–​solvent interactions (cavity formation) and the formation of solute–​solvent interactions. The cavity term and solute–​solvent interactions oppose each other and for retention the free energy of the solute-​solvent interactions must exceed the free energy required to disrupt solvent–​solvent interactions (cavity formation). The solvation parameter model provides a suitable method to characterize the retention properties of a stationary phase and is as set out below67–​69

log k = c + eE + sS + aA + bB + lL (3.7)

where k is the retention factor, the capital letters are descriptors that define the properties of the compounds and the lowercase letters in italics are system constants that define the complementary interactions of the stationary phase. The solute descriptors are defined in Table 3.4 with values available for several thousand compounds.70–​72 The system constants for a stationary phase are determined from experimental retention factors for varied compounds with known descriptor values by multiple linear regression analysis. The system constants contain the relevant information to characterize stationary phase selectivity. The e system constant is determined by the contributions from electron lone pair interactions (the additional dispersion forces that result from the polarizability of compounds with loosely bound electrons); the s system constant by the contribution from dipole-​type interactions (dipolarity and induction); the a system constant by the contribution from the hydrogen-​ bond basicity of the stationary phase; the b system constant by the contribution from the hydrogen-​bond acidity of the stationary phase; and the l system

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Table 3.4  Definition of solute descriptors used in the solvation parameter model. Descriptor

Description

E

Excess molar refraction For liquids calculated from the refractive index and the characteristic volume E = 10V[(η2 –​1) /​(η2 + 2)] –​2.832V + 0.526 η = refractive index at 20°C (sodium D-​line). For solids determined experimentally (preferred) or calculated from an estimated refractive index McGowan characteristic volume Calculated from structure using atom constants taking account of bond order Dipolarity/​polarizability Determined experimentally from (usually) chromatographic, liquid–​liquid partition or solubility measurements Hydrogen-​bond acidity Determined experimentally from (usually) chromatographic, liquid–​liquid partition or solubility measurements Hydrogen-​bond basicity Determined experimentally from (usually) chromatographic, liquid–​liquid partition or solubility measurements Gas–​liquid partition constant on n-​hexadecane at 298 K Back-​calculated from gas chromatographic retention factors on low-​polarity stationary phases at temperatures > 298 K

V S A B L

constant by the opposing contributions from dispersion interactions and cavity formation in the stationary phase. The sign of the system constant indicates whether the contribution increases retention (positive sign) or opposes retention (negative sign). The c term in eqn (3.7) is not a fundamental property of the stationary phase. When the retention factor is the dependent variable it is dominated by the column phase ratio, but it may also be partly determined by statistical contributions from the uncertainty in fitting the model to the experimental data. The c term is not required for the comparison of selectivity, but is necessary to model retention for individual columns that might have similar selectivity but different phase ratios. The poly(dimethylsiloxanes), PDMS, and poly(methyloctylsiloxane), PMOS, stationary phases are the only important example of poly(dialkylsiloxane) stationary phases (Figure 3.3). They are low-​ selectivity stationary phases with favorable dispersion interactions resulting in nearly non-​specific retention. Polar interactions are weak or absent (Table 3.5). These stationary phases can be taken as the baseline for comparing selectivity of WCOT columns. The poly(dimethyldiphenylsiloxane) stationary phases, with 5% diphenylsiloxane monomer PMPS-​5 (Figure 3.3 with R1 = R2 = CH3 and R3 = R4 = C6H5 and n = 95 and m = 5) is the most widely used general-​purpose stationary phase for gas chromatography. The dominant contribution to retention is dispersion interactions (l system constant) with weak dipole-​type and hydrogen-​bond base interactions (s and a system constants). Electron lone pair interactions are minor and the phase is

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Table 3.5  System constants at 140°C for common stationary-​phase chemistries.

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Stationary phase PMOS PDMS PMPS-​5 PMPS-​20 PMPS-​35 PMPS-​50 PMPS-​65 PMTS-​35 PMTS-​100 PCPM-​06 PCPM-​14 PCPM-​50 PCPM-​100 PCPS PEG IL60 IL-​61 IL-​76 IL-​100

System constants e

s

a

b

l

0.193 0.022 0.032 0.056 0.098 0.111 0.157 –​0.290 –​0.331 –​0.024 –​0.063 0.037 0 0.063 0.222 0 0.077 0.153 0.030

0.088 0.205 0.272 0.495 0.593 0.732 0.766 0.977 1.272 0.434 0.652 1.049 1.453 1.814 1.241 1.390 1.362 1.505 1.629

0 0.124 0.156 0.210 0.243 0.285 0.297 0.110 0.140 0.382 0.568 0.956 1.338 1.801 1.664 1.057 1.312 1.433 1.204

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.294 0.216 0.166 0.454

0.542 0.455 0.465 0.490 0.487 0.483 0.486 0.429 0.409 0.494 0.468 0.385 0.415 0.370 0.427 0.360 0.361 0.319 0.274

non-​hydrogen-​bond acidic. Selectivity differences are small compared with the poly(dialkylsiloxane) stationary phases, but with increasing incorporation of diphenylsiloxane monomer selectivity differences are more significant. Typical values for the system constants are given in Table 3.5 for the poly(dimethyldiphenylsiloxane) stationary phases containing 20% (PMPS-​ 20), 35% (PMPS-​35), 50% (PMPS-​50) and 65% (PMPS-​65) diphenylsiloxane monomer. These represent the stationary-​phase compositions commonly available as pre-​coated WCOT columns. The influence of increasing the diphenylsiloxane monomer composition is illustrated in Figure 3.4.69 The dominant feature is the orderly increase in contributions from dipole-​type interactions (s system constant) and hydrogen-​bond basicity (a system constant) at least up to 50% diphenylsiloxane monomer with an indication that at higher diphenylsiloxane monomer compositions selectivity differences decline. The ratio of the s/​a system constants also increases, with increasing amount of diphenylsiloxane monomer indicating a progressive change in selectivity for compounds with descriptor values influenced by these interactions. Phenyl-​containing phases based on silarylene–​siloxane copolymer chemistry exhibit small changes in selectivity compared with the nominally similar poly(dimethyldiphenylsiloxane) stationary phases.73 Poly(dimethylmethyltrifluoropropylsiloxane) stationary phases in which one of the R groups in Figure 3.3 is a 3,3,3-​trifluoropropyl group, and the ratio of monomer composition n/​m is varied, possess complementary selectivity to the poly(dimethyldiphenylsiloxane) stationary phases.74 The most common

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Figure 3.3 General structures of a poly(siloxane) stationary phase with different monomer units and a poly(siloxane) based on silphenylene–​siloxane copolymer chemistry. Typical R groups are methyl, octyl, phenyl, 3,3,3-​ trifluoropropyl and 3-​cyanopropyl.

s

0.8 System Constant

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0.6

l

0.4

a

0.2

e

0

0

20 40 60 Percent Polar Monomer

Figure 3.4 Plot of the system constants at 140°C for poly(dimethyldiphenylsiloxane) stationary phases containing different amounts of diphenylsiloxane monomer. Reproduced from Ref. 69 with permission from Elsevier, Copyright 2008.

compositions for pre-​ coated columns are 35% methyltrifluoropropylsiloxane monomer, PMTS-​35, and 100% methyltrifluoropropysiloxane monomer, PMTS-​100 (Table 3.5). The methyltrifluoropropylsiloxane monomer-​containing stationary phases are significantly more dipolar/​ polarizable and weaker hydrogen-​ bond bases than the poly(dimethyldiphenylsiloxane) stationary phases. In addition, electron lone pair interactions are important and repulsive. Common poly(siloxane) stationary phases containing a 3-​cyanopropyl group include poly(cyanopropylphenyldimethylsiloxanes) with 6% (PCPM-​06), 14% (PCPM-​14) and 50% (PCPM-​50) cyanopropylphenylsiloxane monomer; poly(cyanopropylmethylsiloxane) with 100% cyanopropylmethylsiloxane monomer (PCPM-​ 100); and poly(biscyanopropylsiloxane) with 100% bis(cyanopropylsiloxane) monomer (PCPS) (Table 3.5). A number of pre-​coated columns with 85–​100% bis(cyanopropylsiloxane) monomer are used for specific applications

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such as the separation of saturated and unsaturated fatty acid methyl esters.75 This group of stationary phases contains examples of the most polar stationary phases for gas chromatography. The poly(cyano­propylphenyldimethylsiloxane) stationary phases are characterized by their unique s/​a ratio. The poly(cyanopropylmethylsiloxane) and poly(biscyanopropylsiloxanes) phases are quite cohesive, strongly dipolar and the strongest hydrogen-​bond bases among the poly(siloxane) stationary phases. The small l system constants are an indication of the relative difficulty of cavity formation as well as the limited capability to separate alternate members in a homologous series. The poly(ethylene glycol) (PEG) stationary phases are polar stationary phases with similar cohesion to poly(cyanopropylmethylsiloxane) but are less dipolar/​polarizable and slightly more hydrogen-​bond basic.69,75 In addition, electron lone pair interactions, while weak, are more important. A small number of ionic liquid stationary phases are now available as pre-​coated columns but offer only a limited range of structural diversity.76 Representative examples are 1,12-​ di(tripropylphosphonium)dodecane bis(trifluoromethylsulfonyl)imide (IL60), 1,12-​ di(tripropylphosphonium) dodecane bis(trifluoromethylsulfonyl)imide trifluoromethanesulfonate (IL61), tri(tripropylphosphoniumhexanamido)triethylamine bis(trifluoro­ methylsulfonyl)imide (IL76) and 1,9-​di(3-​vinylimidazolium)nonane bis(trifluoromethylsulfonyl)imide (IL100) (Figure 3.5). The ionic liquids are cohesive solvents and strongly dipolar/​polarizable while slightly weaker hydrogen-​ bond bases than the poly(biscyanopropylsiloxanes) and poly(ethylene glycol) stationary phases (Table 3.5). Most notable, they are weak hydrogen-​bond acids. Common poly(siloxane) and poly(ethylene glycol) stationary phases are non-​hydrogen-​bond acids.77 Except for hydrocarbons, most organic compounds are hydrogen-​bond bases and the ionic liquids allow this interaction to be exploited to adjust selectivity. The exchange of a single anion in IL60 and IL61 on the hydrogen-​bonding interactions for ionic liquids offers insight into how variation of the ionic structure might be used to design stationary phases for specific applications. Because of the limited number of ions with favorable properties for gas chromatography identified so far, this remains more of a goal than reality. Principal component plots provide a useful tool to visualize how individual stationary phase chemistries occupy the selectivity space accessible for pre-​coated columns. For the stationary phase chemistries in Table 3.5, the principal component score plot is shown in Figure 3.6. The first two principal components explain 84.4% of the variance and represent the column selectivity space reasonably well. The points shown on the plot represent single stationary-​phase chemistries and not the dispersion within the selectivity space for columns of the same nominal stationary-​phase chemistry. The cluster of stationary phases at the lower right-​hand corner represent the poly(siloxane) stationary phases containing methyl and phenyl groups and the poly(cyanopropylphenyldimethylsiloxane) stationary phases with 14% of cyanopropylphenylsiloxane monomer. They fill this area of the selectivity space quite well, but cover only a small region of the total selectivity space.

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Figure 3.5 Structures of ionic liquid stationary phases available as pre-​ coated columns; 1,12-​di(tripropylphosphonium)dodecane bis(trifluoromethylsulfonyl)imide trifluoromethanesulfonate (IL61) (top), tri(tripropylphosphoniumhexanamido)triethylamine bis(trifluoromethylsulfonyl)imide (IL76) (middle), and 1,9-​di(3-​ vinylimidazolium)nonane bis(trifluoromethylsulfonyl)imide (IL100) (bottom).

The poly(methyloctylsiloxane) stationary phase is displaced from this group of moderately polar phases and has singular (low) selectivity. The poly(di­ methylmethyltrifluoropropylsiloxane) and poly(ethylene glycol) stationary phases occupy separate areas of the selectivity space to the moderately polar poly(siloxane) stationary phases and are a good choice for method development. The poly(cyanopropylmethylsiloxanes), poly(biscyanopropylsiloxanes) and ionic liquid stationary phases occupy the top portion of the selectivity space, with the ionic liquids providing access to the neighboring selectivity space left vacant by the cyanopropylsiloxane-​containing stationary phases.

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IL100

IL60

IL61

IL76

PCPS

PMTS PCPM

PDMS + PMPS

PEG

PMOS

Figure 3.6 Score plot from principal component factor analysis (varimax rotation) of the stationary-​phase chemistries summarized in Table 3.5.

What is striking, though, is how much of the selectivity space remains empty and stationary phases that could fill these regions would be useful. Selecting a column for a particular application can be considered both simple and difficult at the same time. The stationary phase should be selected first and then the physical dimensions of the column to provide the required efficiency and retention. For most applications a column of the PMPS-​5 type is a reasonable choice, and such stationary phases are the most widely used general purpose stationary phases in gas chromatography. Low-​bleed versions facilitate applications employing mass spectrometric detection. Typical food applications for PMPS-​5 columns include analysis of pesticides, environmental contaminants (polybrominated diphenyl ethers, polychlorinated biphenyl ethers, dioxins and furans, and polycyclic aromatic hydrocarbons), food contact materials (plasticizers, monomers, etc.), additives and preservatives, hormones, triglycerides, essential oils, and flavors and fragrances. More polar columns are required for specific separations or to provide identity confirmation. In food chemistry, for example, the separation of essential oils and flavors on PEG phases,19,78 organochlorine and organophosphorus pesticides using PMTS phases74 and the separation of fatty acid methyl esters differing in degree of unsaturation or as cis/​trans isomers using PCPS and ionic liquid stationary phases.75,79 A modern development in the marketing of WCOT columns is application-​ specific columns. These columns are generally certified to perform a specific separation often connected with regulatory requirements. Examples include the congener-​specific separations of polychlorinated biphenyls and dioxins,74 multiresidue analysis of organophosphorus and organochlorine pesticides,74 volatile organic compounds80 and fatty acid methyl esters,75 etc. Generally, these are columns prepared from the same poly(siloxane) stationary phases discussed previously, perhaps with the column length and phase ratio optimized for the specified separation. In a few cases the stationary phase has

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1.8

System Constants

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1.5 1.2 s

0.9

a

0.6 0.3 0

e 50

100

150 200 Temperature (°C)

250

l b 300

Figure 3.7 A system map for the ionic liquid stationary-​phase IL60.

been engineered for the specific separation by optimizing the monomer ratio compared with general purpose columns of the same type. Column details are typically withheld as proprietary information. System maps are a continuous plot of the system constants as a function of temperature for a stationary phase.43,62,68,69,81 They illustrate the change in selectivity with temperature. Intermolecular interactions are temperature-​ dependent and stationary-​phase-​specific, so it cannot be concluded that ranking of stationary phases at a particular temperature will hold true for other temperatures. A typical system map for the ionic liquid stationary-​phase IL60 is shown in Figure 3.7. The general trend is for interactions of a dipole-​type and hydrogen bonding to decline with temperature, and to do so with different slopes. Of note is that for polar stationary phases these interactions usually persist to the maximum column operating temperature and, as a consequence, selectivity differences remain over the full temperature range for each stationary phase. Cavity formation/​dispersion interactions (l system constant) decline with temperature, indicating that dispersion interactions are less influential at higher temperatures (cavity formation will be easier at higher temperatures and would result in an increase in l if this was the only change with temperature). Electron lone pair interactions are generally weak and for some phases negative (repulsive) at low temperatures becoming positive at high temperatures. At high temperatures their relative contribution becomes more important because the other system constants have smaller values.

3.4.3 Chiral Stationary Phases Enantiomers are stereoisomers that differ only in the spatial configuration of their substituent groups at an asymmetric center. In all other respects they have identical physico-​chemical properties and cannot be separated by conventional stationary phases, which lack the capability to recognize the

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difference in their shape. There are two general approaches for their separation: the formation of transient diastereomer association complexes with a stationary phase containing a chiral selector (direct approach); or after reaction with a single-​enantiomer derivatizing reagent to form covalently bonded diasteroemers which can be separated by conventional stationary phases (indirect method). Only the direct method is considered here. The main application of chiral stationary phases is the precise determination of the enantiomeric composition of volatile chemicals or natural products and their metabolites. There are numerous applications in food chemistry, for example, the analysis of fragrance and flavor compounds and increasingly food contaminants, such as pesticides, fungicides and herbicides to name just a few.82 Many bulk food constituents are stereoisomers and their enantiomeric composition can be used to identify adulteration or contamination. There are three types of pre-​coated WCOT columns available for the separation of enantiomers. Chirasil–​Val is a poly(methylsiloxane) stationary phase containing l-​valine-​t-​butylamide as a chiral selector. It is widely used for the separation of racemic amino acid derivatives, amino alcohols, amines, lactones and sulfoxides.83 Chirasil–​metal contains a metal complex (e.g., nickel) bis[3-​(heptafluorobutanyl)-​(1R)-​camphorate] as a chiral selector incorporated into a poly(methylsiloxane) stationary phase. For stationary phases of this type enantioseparation is based on chelation and is useful for the separation of low-​mass oxygen-​, nitrogen-​and sulfur-​containing ring compounds.84 Cyclodextrin-​containing stationary phases have largely replaced Chiralsil metal columns today in many of their applications. The development of derivatized cyclodextrins as chiral selectors is a major success story for enantioseparations by gas chromatography.28,82,84,85 A number of columns coated with a mid-​polarity poly(siloxane) stationary phases with the chiral selector dissolved in the stationary phase are commercially available, but these are increasingly being replaced by a smaller number of stationary phases in which the chiral selector is attached by a hydrocarbon linker arm to the silicon atom of a poly(methylsiloxane) polymer. Cyclodextrins are natural macrocyclic oligosaccharides consisting of six (α-), seven (β-​) or eight (γ-​) d-​glucose monomers arranged in the shape of a hollow truncated cone with a relatively hydrophobic cavity and a polar outer surface where the hydroxyl groups are located. The larger opening of the cone is surrounded by the secondary (C-​2 and C-​3) hydroxyl groups, while the primary (C-​6) hydroxyl groups are located at the smaller end of the cone. Derivatizing the outer rim hydroxyls with alkyl, trifluoroacetyl, t-​butyldimethylsilyl groups, etc. affords a simple method to adjust the enantioselectivity of the cyclodextrins. Commercially available immobilized stationary phases include heptakis(2,3,6-​tri-​O-​methyl)-​β-​cyclodextrin (Chirasil–​Dex), heptakis(3-​O-​ trifluoroacetyl-​2,6-​di-​O-​n-​pentyl)-​β-​ cyclodextrin, and octakis(3-​ O-​butryl-​2,6-​ di-​(2-​n-​pentyl)-​γ-​cyclodextrin (Chirasil–​γ-​Dex) as chiral selectors. Rationalizing the chiral recognition mechanism for cyclodextrin derivatives is difficult, as almost all classes of enantiomers are separated to some extent on several cyclodextrin derivatives, often without a logical dependence on shape, size

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or functionality. The high efficiency of WCOT columns allows small selectivity factors to be exploited to yield useful separations. This is certainly a contributing factor to the success of these phases. For method development a large database of successful applications, ChirBase GC, can be scanned for suitable stationary phases based on past experience with the same or similar compounds.86 Alternatively, a small number of columns can be screened experimentally to identify a suitable phase.

3.4.4 Porous-​layer Open-​tubular Columns Porous-​layer open-​tubular columns are used for gas–​solid chromatography in which the retention mechanism is dominated by adsorption and, in some cases, accompanied by size-​exclusion and partition. Pre-​coated PLOT columns are available with aluminum oxides, silica, molecular sieves, various forms of carbon and porous polymers prepared from styrene, divinylbenzene, vinyl pyridine and methacrylate monomers.59,87 Thin layers of adsorbent (5–​50 μm thick) are deposited and immobilized on the column wall by in situ polymerization or addition of a chemical binder to a suspension of particles subsequently bonded to the column wall. Porous-​layer open-​tubular columns are used for a narrow range of applications, roughly stated as those difficult to achieve by gas–​liquid chromatography at normal temperatures. Typical applications include the separation of fixed gases, short-​chain hydrocarbons, volatile halocarbons, Freons, solvents, isotopomers and sulfur gases with a boiling point below 200°C. The high adsorption energy and large surface areas of typical adsorbents result in excessive retention for polar compounds of moderate vapor pressure. Applications in food analysis are rare, except for headspace samples. The typical complex solvent extracts from foods result in column fouling and in the strong retention of matrix components.

3.5 Detection Options The common methods of detection in gas chromatography can be classified into three categories based on the detection principle: ionization detectors, bulk property detectors and optical detectors. Further division into universal, element-​selective, structure-​sensitive and mass-​or concentration-​dependent based on the detector response is possible. As well as these general detectors, more specialized options such as mass spectrometry (Chapter 4), olfactometry (Chapter 9), radiochemical and infrared absorption should be mentioned.

3.5.1 Ionization Detectors The ionization detectors are the most important of the general detectors. They are generally low-​cost, rugged and reliable with a fast and sensitive response. The overarching principle exploited in their design is that typical carrier gases are excellent insulators, allowing processes that produce only a few ions to be exploited for detection. The increased conductivity associated with these

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charged species is easily measured, providing the low sample detection limits characteristic of ionization detectors. The ionization detectors in common use include the flame ionization detector (FID), the thermionic ionization detector (TID), the photoionization detector (PID), the electron-​capture detector (ECD) and the helium-​ionization detector (HID).28,88,89 Each detector employs a different method for producing ions, but in all cases the quantitative basis of the detector operation is the fluctuation of an ion current generated when organic vapors diluted by the carrier gas flow into the detector.

3.5.1.1 Flame Ionization Detector The flame ionization detector is the most popular detector for gas chromatography. It provides a near-​universal response to organic compounds and is sometimes referred to as a carbon-​selective detector with a near-​constant response for carbon-​ containing compounds with only a weak structure dependence. This allows a single standard to be used for the calibration of mixtures and percent relative composition reports to be obtained directly from the sum of detector responses for all observed peaks without using multiple response factors. Only elemental and simple inorganic gases, single-​ atom carbon compounds bound to oxygen or sulfur, water, formic acid and formaldehyde provide a weak or insignificant detector response. A representative schematic diagram of an FID is shown in Figure 3.8A. The detector employs a small hydrogen–​air diffusion flame for combustion of organic compounds producing predominantly carbon dioxide and water

A

B

Figure 3.8 Schematic diagram of a flame ionization detector (A) and a co-​axial cylinder electron-​capture detector (B). Reproduced from Ref. 89 with permission from Elsevier, Copyright 2015.

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Table 3.6  T  ypical performance characteristics for the flame ionization and thermionic ionization detectors. Parameter

Response characteristics

Flame ionization detector Type Response mechanism Minimum detectable level Linear range

Carbon-​selective detector Mass-​dependent 2 × 10–​12 g C s–​1 106–​107

Thermionic ionization detector Type Response mechanism Minimum detectable level Linear range Selectivity

NP-​mode Element-​selective detector Mass-​dependent 5 × 10–​14–​5 × 10–​13 g N s–​1 1 × 10–​14–​2 × 10–​13 g P s–​1 105 Nitrogen and phosphorus 104–​105 g C g–​1 N 104–​105 g C g–​1 P 0.1–​0.5 g P g–​1 N

P-​mode

5 × 10–​14 g P s–​1 105 Phosphorus 106 g C g–​1 P

as well as a small number of ions by a minor reaction pathway(s). A cylindrical collector electrode located a short distance above the flame collects the charged particles by application of a small voltage between the flame jet and the collector electrode. The small ion currents are converted to a voltage and amplified by a precision electrometer. The negligible unswept internal volume of the FID combined with the fast transduction of the chemical signal ensures that the detector imposes minimal band broadening for fast separations. The detector has excellent sensitivity with mass detection limits in the picogram range (Table 3.6). The detector response mechanism is poorly understood, but it is generally inferred that ion formation results from chemical ionization of CHO* produced by reaction of O• and CH•.89 CH• + O• → CHO* → CHO+ + e_​ (3.8) The ionization process is expected to be first-​order, explaining the linear response of the FID. The detector response is reduced compared with hydrocarbons for carbon compounds containing oxygen, nitrogen, sulfur or halogen atoms. The reduced response is due to competition between hydrogenation of the carbon–​heteroatom bond and hydrogen abstraction with the formation of poorly ionized neutral species. The effective carbon number concept was developed to account for the lower detector response for compounds containing heteroatoms.89,90 The effective carbon number is defined as the number of equivalent alkane carbon atoms that would produce the same detector response as the compound of interest. In theory, if the combustion properties of the functional groups are independent of structure the effective

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Table 3.7  A  pproximate contributions of molecular fragments to the response of the FID determined as the effective carbon number. Atom

Type

Effective carbon number

C C C C C C C C O O O O N Cl Cl Cl Br I

Aliphatic Aromatic Olefinic Acetylenic Carbonyl Carboxyl Ester Nitrile Ether Primary alcohol Secondary alcohol Tertiary alcohol In amines On an olefinic C On an aliphatic C Two or more on aliphatic C On an aliphatic C On an aliphatic C

1.0 1.0 0.95 1.30 0 0 –​0.50 0.3 –​1.0 –​0.5 –​0.75 –​0.25 Similar to O in alcohols 0.05 –​0.14 –​0.2 per Cl –​0.25 –​0.14

carbon number could be estimated by summation of the various carbon and heteroatom contributions (Table 3.7), which in turn could be used to predict response factors. However, when accurate response factors are required they should be determined by experiment as the effective carbon number values are overall averages affected by both molecular structure and assumptions in their calculation.

3.5.1.2 Thermionic Ionization Detector The thermionic ionization detector, also known as a nitrogen–​phosphorus detector (NPD), evolved from studies of element-​selective detection using the alkali–​metal–​flame ionization detector. Its general structure is similar to the FID with some important differences. A hydrogen–​air plasma without flame is used as a thermal reservoir to fragment organic compounds producing radicals. The high-​temperature plasma is created by alteration of the hydrogen-​ to-​air ratio compared with the FID. The detector contains a thermionic source located just above the plasma jet with a cylindrical collector electrode either surrounding the thermionic source or located immediately above it. The thermionic source is a ceramic glass matrix doped with an alkali metal salt (usually rubidium or cesium), in the form of a bead molded onto an electrical heater wire. A current applied to the wire controls the temperature of the thermionic source, which is typically 400–​600°C. A potential of a few hundred volts set between the plasma jet and collector electrode collects (usually) negative ions formed in the plasma surrounding the thermionic source and the ion current is measured by an electrometer. All TIDs exhibit some loss of sensitivity

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and selectivity in use, necessitating periodic source replacement and routine checks on calibration. Chlorinated solvents and silanizing reagents should be avoided to maintain detector stability. The detector is typically operated with a hydrogen-​rich plasma to enhance selectivity to nitrogen and phosphorus (NP-​mode), and with an oxygen-​rich plasma to suppress selectivity toward nitrogen (P-​mode). Compounds entering the plasma are efficiently broken down into fragments. Those of high electronegativity, such as CN• and PO2•, are thought to be important in explaining the element-​selective response. These radicals are thought to act as precursor species for negative ion products, such as CN–​ and PO2–​ among others, formed by interaction with the thermionic source. However, the detailed reaction mechanism is unclear.91 Simple carbon compounds lack the internal energy required to overcome the work function of the thermionic source and are poorly ionized. Although the TID requires more attention than the FID, it is not particularly difficult to operate and with routine maintenance provides a stable response. Selectivity for phosphorus over nitrogen in the P-​mode is good, but for nitrogen-​containing compounds in the NP-​mode selectivity over phosphorus is poor (Table 3.6). In both the NP-​and P-​mode selectivity over other heteroatom-​containing compounds and carbon compounds is good. It is widely used in food analysis for the measurement of pesticide and pharmaceutical residues, and for natural nitrogen-​containing compounds.92–​94

3.5.1.3 Photoionization Detector When a neutral molecule absorbs a photon of energy close to its ionization potential, the molecule may be ionized. For typical organic compounds this requires photons with energies of 5–​20 eV depending on its structure. The absorption of photons with a narrow energy distribution and detection of the ions formed is the basis of the response of the photoionization detector and its structure selectivity. The detector consists of a photon source separated from an ionization chamber by a photon-​transparent window material.89,95 The photon source is a compact, low-​pressure gas-​discharge lamp emitting a line spectrum of specific energies that depend on the choice of fill gas and window material. Discharge sources of nominal energy 8.3, 9.5, 10.2, 10.9 and 11.7 eV provide for the possibility of selective ionization of organic compounds, with the 10.2 eV source used for general applications on account of its low selectivity. The column effluent passes through a heated ionization chamber and between two annular electrodes separated by an insulator. Typical ionization chamber volumes are 40 and 175 μL for normal-​and wide-​bore WCOT columns, respectively. An electric field of a few hundred volts is applied between the electrodes to collect ions and electrons and the current amplified by an electrometer. Due to the low efficiency of photon ionization (typically < 0.1%) and the neutralization of ions at the collector electrodes the detector can be classified as non-​destructive and used in tandem with destructive detectors for dual-​detection monitoring without flow splitting. Because it only requires

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carrier gas for its operation, it is frequently used in portable gas chromatographs. A pulsed helium discharge detector with an open source design provides a suitable alternative to the conventional PID.96 The PID is a structure-​selective and concentration-​dependent detector.97 Compound response factors cover a wide range. For easily ionized compounds it may be 5–​50-​fold more sensitive than the FID, while for other compounds it may not respond at all or only respond weakly. Selectivity is limited for specific applications by the discharge sources available. It has a linear response range of about 107.

3.5.1.4 Electron-​capture Detector The electron-​capture detector is a structure-​selective detector with a wide response range. Compounds with a strong response include those containing halogen atoms or nitro groups and certain conjugated compounds.98 Compounds with a low electron affinity (e.g., hydrocarbons, simple oxygenated compounds, amines, etc.) have only a weak response. If these compounds possess a reactive functional group they can be derivatized with a number of functional-​group-​specific reagents to introduce a suitable electrophore for detection.99 The ECD owes its popularity to its high sensitivity to biologically active compounds. Typical applications in food analysis include the determination of persistent organochlorine pesticides and polychlorinated biphenyl ethers, acrylamide, volatile haloalkanes, cyclamates and mycotoxins in vegetables and processed foods.100–​102 Detector response characteristics for favorable compounds are summarized in Table 3.8. The ECD is available in two general designs depending on the source of thermal electrons. The majority of radioisotope-​based detectors use nickel-​63 as a source on account of its high thermal stability (to 400°C) and its operational and practical convenience. The radioisotope source is platted on the inside wall of a flow-​through ionization chamber. Nickel-​63 decays with the emission of high energy β-​particles which undergo multiple collisions with carrier gas molecules forming a plasma of thermal electrons (0.02–​0.05 eV), radicals and positive ions within the ionization chamber. The co-​axial cylinder, Figure 3.8B, and asymmetric configuration dominate the design for ionization chambers with several variations.28,88,89,98,103,104 Locating the anode upstream from the ionized gas volume in the asymmetric configuration minimizes interference from the collection of long-​range β-​particles and facilitates a more compact design with an effective volume of about 0.3–​1.0 mL. These relatively large detector volumes are not ideal for fast separations using WCOT columns and the effective detector volume is reduced by adding makeup gas at the end of the column to preserve column efficiency at the expense of sample dilution and a lower response. Hydrogen or helium are typically used as the carrier gas, with argon containing 5–​10% methane or nitrogen as the makeup gas to minimize other interactions with the sample besides those occurring by electron capture. Application of a square-​wave potential to the ionization chamber allows collection of the thermal electrons and establishes a steady state

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Table 3.8 Typical performance characteristics for popular structure-​and element-​ selective detectors. Parameter

Response characteristics

(i) Electron-​capture detector Type Response mechanism Minimum detectable level Linear range Selectivity

Structure-​selective detector Concentration-​dependent 5 × 10–​14 g mL–​1 104–​105 Variable (see text)

(ii) Flame photometric detector Type Response mechanism Minimum detectable level Linear response range Selectivity (iii) Chemiluminescence detector Type Response mechanism Minimum detectable level Linear range Selectivity

P-​mode Element-​selective detector Mass-​dependent 5 × 10–​13–​1 × 10–​14 g P s–​1 105 5 × 105 g C g–​1 P

S-​mode

N-​mode Element selective detector Mass-​dependent 10–​12 g N s–​1 105 107 g C g–​1 N

S-​mode

10–​12–​10–​14 g S s–​1 non-​linear 104–​107 g C g–​1 S

10–​12 g S s–​1 105 107 g C g–​1 S

(or baseline) detector current. When an electron-​capturing compound enters the ionization chamber, thermal electrons are removed by the formation of negative ions. These negative ions do not reach the collector electrode during application of the pulse voltage owing to their relatively slow drift velocity and neutralization by positive ions. Thus, in the presence of an electron-​capturing compound the population of thermal electrons is reduced and the detector current is smaller compared with the steady-​state current in the absence of electron-​capturing compounds. In practice, most detectors are operated in a constant current mode, in which the pulse frequency of the applied voltage is varied throughout the separation to maintain the detector current at a reference value.105 The detector signal is a voltage proportional to the pulse frequency. This has the advantage of increasing the linear response range (≈ 104–​105) and reduces detector disturbance from column contamination. The pulsed-​helium discharge ECD generates thermal electrons by photoionization of an additive gas (typically methane or xenon) downstream of the discharge zone where a small bias potential allows isolation of the thermal electrons and their interaction with the column effluent.106,107 A variable dc voltage proportional to the analyte concentration entering the detector provides the detector signal. Both the radioisotope-​based and pulsed-​helium discharge ECD have similar performance characteristics. The radioisotope-​ based detector is subject to a higher level of regulatory requirements and routine leak testing.

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The ECD is a concentration-​dependent detector with each compound requiring calibration for quantification because response factors cover a wide range and change with detector contamination. The detector response is also temperature-​dependent. For compounds that undergo dissociative electron capture involving bond-​breaking (e.g., halogen-​containing compounds) the highest response is usually obtained at the maximum detector operating temperature. For compounds that undergo non-​dissociate capture involving electron attachment with the preservation of the bond order, the highest response usually corresponds to the lowest practical detector temperature dictated by the column operating conditions. Some compounds may show mixed behavior and the optimum detector temperature should then be determined by experiment.

3.5.2 Bulk Property Detectors Bulk property detectors respond to some difference in a physical property of the carrier gas due to the presence of an organic compound. Usually, a large signal for the characteristic carrier gas property is desirable to ensure a reasonable working range, but for low concentrations of analyte the detector response becomes noise-​limited. The sensitivity of bulk property detectors tends to be low compared with ionization detectors. The only bulk property detector commonly used today is the thermal conductivity detector (TCD). This detector is easily miniaturized and has advantages where low power consumption is important.

3.5.2.1 Thermal Conductivity Detector The thermal conductivity detector is a universal, non-​ destructive, concentration-​dependent detector that responds to the difference in thermal conductivity of the carrier gas and the carrier gas containing analyte. It is generally used to detect compounds with a poor response to the FID, which is otherwise the preferred detector for general laboratory use. Detection limits for the TCD typically fall into the range 10–​6–​10–​8 g per peak (about 102–​103-​fold higher than the FID) with a linear response range of four orders of magnitude (considerably shorter than the FID). Low sensitivity limits applications of the thermal conductivity detector in food analysis to compositional analysis and evolved gases. An emerging application is the determination of water in processed foods by headspace analysis using ionic liquid stationary phases for the separation of water from matrix components.108 There are numerous detector designs generally based on flow-​through, semi-​diffusion or diffusion cell principles in which the carrier gas flows through a heated thermostatted cavity containing a sensing element, either a heated metal wire or thermistor.88,109 With only carrier gas flowing through the cavity heat loss from the sensor depends only on the thermal conductivity of the carrier gas. In the presence of a mixture of carrier gas and analyte a change in thermal conductivity for the mixture is observed as a resultant

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change in temperature of the sensor. Detector cells with volumes of 1–​100 μL are easily fabricated for use with WCOT columns. An alternative design uses flow modulation to switch the carrier gas between two channels, one of which contains a single filament.88 Every 100 ms a switching valve fills the filament channel alternately with carrier gas and column effluent. Digital processing of the signal provides greater sensitivity and stability compared with conventional detector designs for wide-​bore columns.

3.5.3 Optical Detection Absorbance detectors are little used in gas chromatography. A recent exception is the vacuum ultraviolet detector.110 The detector uses a heated lightpipe-​type interface of low volume (≈ 80 μL), deuterium lamp source and holographic spectrometer with a charge-​coupled device detector to record transmission spectra in the wavelength range 120–​240 nm. It provides qualitative information in the form of a spectrum and quantitative information by integrating the transmission signal over a narrow wavelength range. Vacuum ultraviolet refers to the wavelength range and not the measurement, which is at near-​ atmospheric pressure (no vacuum system required). The detector is universal, mass-​sensitive, with detection limits falling into the low to mid picogram range depending on the compound absorption cross-​section. The vacuum ultraviolet spectra complement mass spectral information. A typical application is to distinguish isomers not easily identified from their mass spectra. It is less complex and has better detection limits than FTIR detectors, but vacuum ultraviolet spectra are less informative than infrared spectra. Some applications to food analysis include the identification of pesticides,110 fatty acid methyl esters111 and to distinguish anomeric carbohydrates.112 Infrared spectra provide information of the bond order and functional groups present in a molecule as a series of narrow absorption bands characteristic of the molecular structure. It affords complementary information to mass spectrometry useful for the identification of functional groups and particularly the possibility to distinguish positional and conformational isomers often not possible by mass spectrometry alone. However, as the use of techniques such as attenuated total reflectance and near-​infrared spectroscopy for the compositional analysis and determination of adulterants in whole foods has grown in recent years this has not been the case for gas chromatography combined with infrared spectroscopy.113,114 Contributing factors include the relatively broad absorption bands observed in vapor-​phase spectra, modest sensitivity and the limited availability of searchable vapor-​phase spectral libraries. The growth of gas chromatography–​infrared spectroscopy–​ mass spectrometry instruments with integrated data analysis had stopped by the turn of the century.115 Applications of gas chromatography–​infrared spectroscopy to food chemistry occur in the compositional analysis of essential oils, flavors and fragrances, vegetable oils and the alteration of the volatile composition of foods associated with processing steps.113,116,117 These

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applications are mainly of a research type and not utilized for routine analysis. A flow-​through, heated lightpipe and associated capillary transfer tubing provides a convenient interface for gas chromatography–​Fourier transform infrared spectroscopy (GC-​FTIR).113 Makeup gas is generally used to manage extracolumn band broadening and the volume of the lightpipe and sensitivity are a compromise between the speed and resolution of the chromatographic separation and the detection level for the separated compounds. Full-​spectra recording typically requires about 10–​25 ng per component for a strong absorber and greater than 100 ng for a weak absorber at a spectral resolution of about 8 cm–​1. More typical applications of optical detection for routine analysis are based on gas-​phase chemiluminescence for element-​selective detection and atomic emission detection. Of these, the flame photometric detector (FPD) for sulfur-​and phosphorus-​containing compounds and the chemiluminescence detector for sulfur-​and nitrogen-​containing compounds (SCD and NCD, respectively) are the most common. The atomic emission detector (AED) provides selectable multi-​element detection and expands the range of elements that can be detected to include many organometallic compounds that can be separated by gas chromatography. It is also considerably more complex than other element-​selective detectors and outside of the determination of organometalic compounds is not widely used.

3.5.3.1 Flame Photometric Detector The flame photometric detector is an element-​selective detector commonly used for the analysis of sulfur-​and phosphorus-​containing compounds by their chemiluminescence in a low-​temperature hydrogen-​diffusion flame. Combustion in the flame results in the formation of species such as S2* and HPO* in an excited state that relax to the ground state with emission of characteristic band spectra. This emission is isolated by a filter and monitored by a photomultiplier detector. In addition to the standard single-​flame design, other versions include dual flame and pulsed flame models.118–​120 Problems associated with hydrocarbon quenching and structure-​response variation for different sulfur-​and phosphorus-​containing compounds with the single flame design can be partially ameliorated using dual-​flame and pulsed-​flame configurations. The dual-​flame FPD employs two longitudinally separated flames. The lower flame is operated in a hydrogen-​rich mode and functions as a matrix-​normalization reactor, resulting in efficient fragmentation to highly reduced species. These combustion products are swept into the second flame where the desired optical emission is generated under optimized flame conditions. The pulsed-​flame detector utilizes flame gas flow rates insufficient to sustain a continuous flame. The combustion gases are combined in a small chamber and flow to a continuously heated wire igniter. The ignited flame then propagates back to the source of combustion gases and is self-​terminated once all of the combustible gas mixture is consumed. The continuous gas flow removes the combustion products and then restarts the combustion process

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in a periodic fashion. The pulsed-​flame emission enhances selectivity by time resolution of the various flame luminescent species using a time gated photomultiplier detector. Only a small fraction of the sulfur entering the flame is converted to S2*. whose relaxation results in broad band emission from 320 to 460 nm with a maximum around 394 nm. The detector response to sulfur is inherently non-​ linear and proportional to [S]‌n where n ≈ 2, but depends on both the compound and detector operating conditions. Suitable quantitative results are obtained using non-​linear calibration for each compound. For phosphorus the emitting species is HPO* and the detector response is linear with respect to the phosphorus concentration. Typical detector characteristics are summarized in Table 3.8. In food chemistry the FPD is commonly used for the determination of organophosphorus pesticides in commodities and beverages and organosulfur pesticides and volatile sulfur compounds (in particular odiferous compounds) in raw and processed foods.121–​123

3.5.3.2 Chemiluminescence Detector The reactions important for gas chromatography occur in the gas phase between ozone and specific fragments produced by the thermal or catalytic decomposition of nitrogen-​and sulfur-​containing compounds. Nitrogen-​ containing compounds are oxidized to nitric oxide, which is then reacted with ozone under vacuum conditions to form nitrogen dioxide in an excited state. The excited state nitrogen dioxide decays with photon emission in the near infrared, around 1 200 nm. Sulfur-​containing compounds are decomposed by thermal oxidation to sulfur monoxide, which is stabilized in a reducing environment and subsequently reacted with ozone to form sulfur dioxide in an excited state with photon emission centered at about 360 nm (range 280–​ 460 nm). A number of flow-​through furnaces have been used to generate the reactive species from organic compounds.122,123 More recent versions of this detector employ dual plasma burners and optimized gas flows for efficient generation of reactive NO or SO species. In contrast to the FPD, the sulfur chemiluminescence detector has a nearly equimolar response and linear response range to sulfur-​containing compounds. A single-​compound calibration curve suffices in most cases for the determination of mixtures of sulfur-​containing compounds. Typical detector characteristics are summarized in Table 3.8. Applications in food chemistry tend to parallel those of the FPD and TID detectors for which it provides an alternative approach for nitrogen compounds with higher selectivity and higher sensitivity and selectivity for sulfur compounds.124–​127

3.5.3.3 Atomic Emission Detector The atomic emission detector is a near-​universal multi-​element selective detector based on an atmospheric pressure helium plasma excitation source and a high-​resolution spectrometer for the partial simultaneous measurement

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of several element emissions. The separation column is fed from the gas chromatograph to the entrance of a microwave cavity through a heated transfer line. A gas-​flow system controls the flow of carrier gas, makeup gas and scavenger gases to the microwave cavity. The plasma is produced in a water-​cooled thin-​wall silica discharge tube, inductively coupled through a waveguide to a magnetron. The exit of the cavity is closed with a fused-​silica window which allows light to be collected from the plasma and focused onto a flat focal-​plane spectrometer with a movable photodiode array detector. The spectral resolution of the AED is about 0.25 nm (full width at half maximum). The photodiode array moves along the focal plane, which is nearly linear from 170 to 840 nm. The array range is about 25 nm, which determines which element combinations can be measured simultaneously. For simultaneous element detection, the elements must have emission lines which fall within the wavelength range spanned by the photodiode array and require the same scavenger gases. Typically, up to four elements can be detected simultaneously (but not necessarily the four elements most desired) and displayed as element-​specific chromatograms. Individual element detection limits range from 0.1 to 75 pg s–​1 with a linear range of about 103–​104. The AED can also be used to detect compounds labeled with stable isotopes. The AED is a complex detector and many factors can affect its response, in particular, any factor that affects the temperature, composition or sample residence time in the plasma is a potential source of poor reproducibility. Attempts to use the AED to establish empirical formulas for compounds have largely been unsuccessful.130,131 For many applications in food analysis mass spectrometric detection is generally preferred and the AED now sees limited use. A notable exception is the speciation of organometallic contaminants (e.g., organotin, organomercury, organolead compounds, etc.) in food extracts.132

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73. W. Kiridena, C. C. Patchett, W. W. Koziol, H. Ahmed and C. F. Poole, J. Sep. Sci., 2006, 29, 211. 74. W. Kiridena, C. DeKay, C. C. Patchett, W. W. Koziol, J. Qian and C. F. Poole, J. Chromatogr. A, 2006, 1128, 228. 75. W. Kiridena, J. Qian, W. W. Koziol and C. F. Poole, J. Sep. Sci., 2007, 30, 740. 76. C. F. Poole and N. Lenca, J. Chromatogr. A, 2014, 1357, 87. 77. S. D. Martin, C. F. Poole and M. H. Abraham, J. Chromatogr. A, 1998, 805, 217. 78. S. T. Chen and P. J. Marriott, Anal. Chim. Acta, 2015, 854, 1. 79. M. X. Zeng, S. T. Chun, Y. Nolvachai, C. Cuising, L. M. Sidisky and P. J. Marriott, Anal. Chim. Acta, 2013, 803, 166. 80. C. F. Poole, J. Qian, W. Kiridena, C. DeKay and W. W. Koziol, J. Chromatogr. A, 2006, 1134, 284. 81. S. N. Atapattu, K. Eggers, C. F. Poole, W. Kiridena and W. W. Koziol, J. Chromatogr. A, 2009, 1216, 1640. 82. C. Cagliero, B. Sgorbini, C. Cordero, E. Liberto, P. Rubiolo and C. Bicchi, Israel J. Chem., 2016, 56, 925. 83. V. Schurig, J. Chromatogr. B, 2011, 879, 3122. 84. V. Schurig, TrAC, Trends Anal. Chem., 2002, 21, 647. 85. V. Schurig, in Gas Chromatography, ed. C. F. Poole, Elsevier, Amsterdam, 2012, pp. 495–​517. 86. B. Koppenhoefer, R. Graf, H. Holzschuh, A. Nothdurft, U. Trettin, P. Piras, C. Roussel, J. Chromatogr. A, 1994, 668, 557. (ChirBase is available from ACD Labs as ChirBase for ACD/​Labs at [email protected]) 87. Z. Ji, R. E. Majors and E. J. Guthrie, J. Chromatogr. A, 1999, 842, 115. 88. M. S. Klee, in Gas Chromatography, ed. C. F. Poole, Elsevier, Amsterdam, 2012, pp. 307–​347. 89. C. F. Poole, J. Chromatogr. A, 2015, 1421, 137. 90. M. Kallai and J. Balla, Chromatographia, 2002, 56, 357. 91. H. Carlsson, G. Robertsson and A. Colmsjo, Anal. Chem., 2001, 73, 5698. 92. S. Corral, E. Leitner, B. Siegmund and M. Flores, Food Chem., 2016, 190, 657. 93. M. A. Gonzalez-​Curbela, S. Dionis-​Delgado, M. Asensio-​Ramos and J. Hernandez-​Borges, J. Sep. Sci., 2012, 35, 299. 94. E. G. Amvrazi and N. G. Tsiropoulos, J. Chromatogr. A, 2009, 1216, 2789. 95. J. N. Driscoll, J. Chromatogr. Sci., 1985, 23, 488. 96. W. E. Wentworth, S. V. Vasnin, S. D. Stearns, C. J. Meyer, Chromatographia, 1992, 34, 219. 97. A. N. Freedman, J. Chromatogr., 1982, 236, 11. 98. A. Zlatkis and C. F. Poole, ed., Electron Capture Theory and Practice in Chromatography, Elsevier, Amsterdam, 1981. 99. C. F. Poole, J. Chromatogr. A, 2013, 1296, 15. 100. H. Sun, X. Ge, Y. Lv and A. Wang, J. Chromatogr. A, 2012, 1237, 1. 101. S. E. K. Tekkeli, C. Onai and A. Onai, Food Anal. Methods, 2012, 5, 29.

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102. M. A. Martinez-​Uraz, M. Mezcua, N. B. Valles, A. R. Fernandez-​Alba, Anal. Bioanal. Chem., 2012, 402, 1365. 103. R. Simon and G. Wells, J. Chromatogr., 1984, 302, 221. 104. M. S. Klee, M. D. Williams, I. Chang and J. Murphy, J. High Resolut. Chromatogr., 1999, 22, 24. 105. R. J. Maggs, P. L. Joynes, A. J. Davies and J. E. Lovelock, Anal. Chem., 1971, 43, 1966. 106. H. M. Cai, S. D. Stearns and W. E. Wentworth, Anal. Chem., 1998, 70. 3770. 107. W. E. Wentworth, J. Huang, K. Sun, Y. Zhang, C. Kao, H. Cai and S. D. Stearns, J. Chromatogr. A, 1999, 842, 229. 108. L. A. Frink and D. W. Armstrong, Food Chem., 2016, 205, 23. 109. G. Wells and P. Simon, J. Chromatogr., 1983, 256, 1. 110. I. C. Santos and K. A. Schug, J. Sep. Sci., 2017, 40, 138. 111. H. Fan, J. Smuts, L. Bai, P. Walsh, D. W. Armstrong and K. A. Schrug, Food Chem., 2015, 194, 265. 112. J. Schenk, G. Nagy, N. L. B. Leghissa, A. Smuts and K. A. Schug, J. Chromatogr. A, 2017, 1513, 210. 113. C. Cordella, I. Moussa, A. C. Martel, N. Sbirrazzuoli, I. Lizzani-​Cuveller, J. Agric. Food Chem., 2002, 50, 1751. 114. P. Wang, J. B. Sun, I. T. Zhang, W. J. Liu, Appl. Spectros. Revs., 2016, 51, 763. 115. T. A. Sasaki, C. L. Wilkins, J. Chromatogr. A, 1999, 842, 341. 116. Q. Zhang, A. S. M. Saleh, Q. Shen, J. Am. Oil Chem. Soc., 2016, 93, 69. 117. C. Tyburczy, M. M. Mossoba, J. L. Rader, Anal. Bioanal. Chem., 2013, 405, 5759. 118. H. Jing and A. Amirav, J. Chromatogr. A, 1998, 805, 177. 119. J. P. Le Harle and B. Bellier, J. Chromatogr. A, 2005, 1087, 124. 120. A. G. Clark and K. B. Thurbide, J. Chromatogr. A, 2015, 1421, 154. 121. X. Yan, J. Sep. Sci., 2006, 20, 1931. 122. J. Luong, R. Gras, M. Hawryluk and R. Shearer, Anal. Methods, 2016, 8, 7014. 123. X. P. Liu, B. Mitrevski, D. E. Li, J. O. Li and P. J. Marriott, Microchem. J., 2013, 111, 25. 124. Y. W. Pan, J. Sep. Sci., 2006, 29, 1931. 125. A. Perez-​Parada, M. Colazzo, N. Besil, I. Geis-​Asteggiarre, F. Ray and H. Heinzon, J. Chromatogr. A, 2011, 1218, 5852. 126. P. G. Hill and R. M. Smith, J. Chromatogr. A, 2000, 872, 203. 127. A. I. Simplicio and I. V. Boas, J. Chromatogr. A, 1999, 833, 35. 128. C. Li, Z. Long, X. Jiang, P. Wu and X. Hou, TrAC, Trends Anal. Chem., 2016, 77, 139. 129. R. G. Wuilloud and J. C. Altamirano, Current Anal. Chem., 2006, 2, 353. 130. J. T. Andersson, Anal. Bioanal. Chem., 2002, 373, 344. 131. G. Becker, A. Colmsjo and C. Ostman, Anal. Chim. Acta, 1997, 340, 181. 132. N. Campillo, P. Vinas, R. Penalver, J. I. Cacho, M. Hernandez-​Cordoba, J. Food Composition. Anal., 2012, 25, 66.

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Conventional Gas Chromatography: Mass Spectrometry Hyphenation and Applications in Food Analysis HANS-​GERD JANSSENa,b*, ALAN GARCÍA CICOURELb AND PETER Q. TRANCHIDAc a

Unilever Research and Development Vlaardingen, P.O. Box 114, 3130 AC Vlaardingen, the Netherlands;  bAnalytical-​Chemistry Group, Van ’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands;  cDipartimento di Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali, University of Messina, Polo Annunziata, 98168 Messina, Italy *Email:  Hans-​[email protected]

4.1 Gas Chromatography–​Mass Spectrometry: Introduction Currently, the most popular and powerful detector for gas chromatography is the mass spectrometer. Since the introduction of bench-​top devices from the mid 1980s, gas chromatography-mass spectrometry (GC-MS) has become an indispensable tool not only for food analysis, but for (organic)

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chemical analysis in general. Gas chromatography and mass spectrometry offer an excellent complementarity: GC, using a conventional 30 m × 0.25 mm ID × 0.25 df capillary column, is a powerful separation tool, prior to the MS identification and quantification process. Unlike the coupling of high-​performance liquid chromatography to MS, the combination of GC with MS is straightforward, because typical gas flows used in open-​tubular column (OTC) GC (1–​2 mL min–​1) are easily handled by modern-​day vacuum systems. This hyphenated approach provides not only information on compound identity, but also a further separation dimension [ions are separated on the basis of their mass-​to-​charge ratio (m/​z) in the mass analyzer]. In such a respect, GC-​MS is a two-​dimensional (2D) technology, being capable of providing two different types of analyte-​specific information, viz., retention time and a mass spectrum (ideally comprising the molecular ion). As will be seen, a non-​sufficient GC separation, leading to cases of co-​elution, can be resolved by exploiting the MS step, for example through deconvolution, extracted ions, MSMS processes, etc. Hence, MS can greatly enhance specificity. Moreover, depending on the type of MS instrumentation used, a high level of sensitivity can be achieved in food analysis, with limits of quantification (LoQs) often down to the low parts-​per-​billion (ppb) level, or even less.1 It is obvious that method sensitivity is also tightly related to sample preparation (see Chapters 1 and 2), a process which is also important to avoid matrix contamination of the GC column and of the ion source. Other MS instrumental features, such as range of linearity, maximum spectral production frequency, duty cycle, mass resolution and accuracy, are all related to the type of system used. The combination of GC with single-​analyzer MS can potentially provide three levels (or points) of identification: (I) GC retention time; (II) the entire mass spectral fingerprint; and (III) molecular mass. For the scope of identification it is convenient to use a relative form of retention, such as the linear retention index (LRI; see Section 3.2.1). With regard to levels I and II, these are always present, while level III depends on the presence of the molecular ion (MI). The latter occurrence is often hindered by the rather harsh classical 70-​eV electron ionization (EI) process.2 The column peak capacity (nc) corresponds to the number of peaks that can potentially be stacked side-​by-​side in a one-​dimensional GC separation space (not considering the void time), at a desired level of resolution (e.g., 1). A conventional low-​polarity 30 m × 0.25 mm ID × 0.25 df column will normally generate an nc value in the range 400–​600. However, GC peaks usually elute in a random manner (unless homologous series of compounds are subjected to analysis), often leading to overcrowded parts of the chromatogram, along with empty zones. The main consequence of random peak elution is that the GC peak capacity must be much higher than the number of sample volatiles if satisfactory analyte-​to-​analyte resolution is desired. Theory dictates that such an excess must reach a factor of 100 to achieve the separation of 98% of the sample constituents.3 So, if one desires to fully separate a food sample composed of 100 volatiles, then an nc value exceeding 10 000 would

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be required. In such an instance, a GC-​FID system would be overwhelmed by such a challenge; on the other hand, as will be seen, GC-​MS can meet such high peak capacity requirements. The use of a multidimensional GC system is a further effective way to both separation power and specificity (Chapters 6 and 7).

4.2 Gas Chromatography–​Mass Spectrometry: Principles and Instrumentation One of the first descriptions of GC-​MS was published in 1959 by Gohlke, who reported the hyphenation of packed-​column GC with low-​resolution time-​of-​ flight (LR ToF) MS.4 The author highlighted the 2D potential of the technology by stating that “single chromatographic peaks containing two or three components can usually be successfully resolved by a careful examination of several mass spectra obtained at various times during the development of the chromatographic peak.” This was, in itself, the first case of MS deconvolution of compounds overlapping at the GC outlet. Since then, MS instrumentation has evolved greatly; the basic MS principles, and some current-​day forms of mass spectrometry, are herein described. In a GC-​MS instrument, the function of the mass spectrometer is to generate ions, mainly from organic compounds, at the outlet of the column; ionization, using a variety of methods, occurs within the ion source. The generated ions are separated on the basis of their m/​z values in the mass analyzer, and are then detected in both qualitative (specific m/​z value) and quantitative terms. The GC process has been described in Chapter 3. Mass spectrometers are operated under high vacuum (< 10−5 Pa), a condition necessary to avoid collisions between the ions and other gaseous molecules, a negative occurrence leading to trajectory deviations and unwanted reactions. Adequate MS vacuum conditions are guaranteed by the combined action of pumps (e.g., mechanical and turbomolecular). As already mentioned, OTC columns are typically operated at low gas flows, which are easily handled by current-​day pumping systems, thus generally leading to no difficulties in GC-​ MS hyphenation. Wide-​bore OTC columns are usually operated at gas flows in the range 5–​10 mL min–​1 (Chapter 3), volumes still within the capability of advanced vacuum systems. Packed columns, on the other hand, are operated at much higher gas flows (up to 50 mL min–​1) and require dedicated interfaces (e.g., jet separator) to selectively remove the carrier gas molecules. However, the use of packed-​column GC-​MS has practically disappeared from the food analysis scene. The GC column is usually directly coupled to the mass spectrometer after passing through a heated metal transfer line. The column is joined to the transfer line in a vacuum-​sealed manner. The temperature of the transfer line must be high enough to avoid analyte condensation, and is often set at a value equal to or just slightly below the maximum GC analysis temperature. The low-​pressure conditions, present in the end portion of the column, contribute to the complete and rapid transfer of the analytes into the ion source.5,6

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4.2.1 Ionization Methods Analytes exiting the GC column are subjected to an ionization process, prior to their entrance into the mass analyzer. The ionization process may consist in the addition or loss of an electron, in protonation or deprotonation, etc. and is followed by fragmentation. The extent to which fragmentation occurs depends on the amount of energy transferred to the analytes, and by its physico-​chemical properties. Ionization and fragmentation are processes specific to each molecule, and are made visible in the mass spectrum. The latter can be considered as an analyte fingerprint or identity card. Mass spectra for compounds with different chemical structures (e.g., different pesticides) will usually be characterized by different ion profiles; on the other hand, analytes with similar chemistries (e.g., terpene classes) will also possess similar mass spectral profiles, with differentiation, under such circumstances, strongly depending on the GC retention information. In the present section, focus will be devoted to ionization methods most used in the food analysis field, along with others with a potential wide future diffusion.

4.2.1.1 Electron Ionization Electron ionization is by far the most common GC-​MS ionization method. The formation of ions, from neutral gas-​phase analytes, is enabled by a beam of electrons emitted by a metal (tungsten or rhenium) filament, resistively heated by a current to its operating temperature. The electrons undergo acceleration, usually through an applied potential of 70 V, to enable the following process:

M( g ) + e − → M(+g•) + 2e −

where M(g) represents the neutral molecule in the gas phase. The ionization energy (IE) is the minimum energy required to ionize a neutral compound, and is in the range 7–​15 eV for most organic molecules. Electron removal can occur at a σ-​or π-​bond, or from a lone pair, with the latter being the most favored position, followed by the π-​bond. The IEs of naphthalene and decane, for example, are 8.1 and 9.7 eV, respectively. However, the probability that this minimum amount of energy will be transferred quantitatively to a molecule, leading to the formation of an ion, is low. More specifically, below an EI energy of 50 eV the ionization efficiency falls very rapidly, while it reaches a maximum at around 70 eV for most organic compounds. The advantages of EI are well-​known, these being the generation of repeatable, compound-​specific mass spectra and the fact that the majority of commercial MS databases contain 70-​eV EI spectra. The main drawbacks consist in excessive molecule fragmentation (with the formation of many low-​mass, non-​specific ions) and in the low abundance, or absence, of the MI.5,6 The application of an electron energy < 70 eV is an option to reduce MI fragmentation and to increase the relative abundance of the molecular ion.

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Such conditions, however, will lead to a reduced ionization and can cause a reduction in sensitivity. The use of a reduced electron energy was exploited by Shimma et al., who analyzed polychlorinated biphenyls (PCBs) through the combination of GC with miniaturized high-​resolution time-​of-​flight mass spectrometry (GC-​HR ToFMS).7 A series of ionization conditions were evaluated, and an optimum was found at 18 eV because it enabled an enhanced intensity of the quantifier ion for heptachlorobiphenyl. The definition “variable energy EI” relates to an ion source characterized by the capability to ionize molecules at a low and high EI energy (e.g., 14 and 70 eV), in an alternate and rapid manner throughout the same analysis.8 Apart from this unique feature, a further emphasized strongpoint is that sensitivity is maintained at a satisfactory level, even when using low EI energies. Supersonic molecular beam (SMB) EI is a soft-​ionization methodology, also defined as “cold EI”, because ionization takes place on vibrationally cooled compounds in a fly-​through ion source. Evident MIs are generated for compounds composed of 15 or more atoms, with such an event often highlighted in hydrocarbon ionization. Other emphasized advantages of SMB EI relate to the possibility of high gas flow operation and to the limitation of source-​ induced peak tailing.9

4.2.1.2 Chemical Ionization Chemical ionization (CI) is considered a soft ionization method with respect to conventional EI; in fact, neutral molecules receive a lower amount of energy: ions are formed through ion–​molecule (bimolecular process) reactions, rather than electron–​molecule (unimolecular process) ones. Current-​ day EI sources can also be operated in the CI mode, with the main difference between the two approaches being the presence of an excess (by a factor of 1 000–​10 000) of reagent gas (i.e., methane, ammonia, isobutene) over the analytes. Such intra-​source conditions enable the ionization of the reagent gas molecules, which in turn interact with the neutral analyte molecules. A variety of positive MIs can be formed, such as through protonation [M + H]+ and electrophilic addition (e.g., [M + NH4]+). Acidic compounds can form both protonated and deprotonated [M − H]− ions, with the latter process exploited for negative chemical ionization (NCI). A CI process involving the formation of a protonated MI, using methane as reagent gas, follows:

CH 4 + e − → CH 4+ • + 2e −



CH 4+ • + CH 4 → CH5+ + CH3•



CH5+ + M → CH 4 + [ M + H ] +

While practically all neutral compounds are able to yield positive ions [positive chemical ionization (PCI)], the production of negative ions requires the presence of electronegative elements, or acidic groups. Compounds with such

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features can be selectively detected (e.g., halogenated pesticides) in the NCI mode. Negative ions can be generated through reactions between analytes and ions present in the reagent plasma, or by capture of thermal electrons. Sensitivity under CI conditions, is generally lower compared to EI, and is dependent on a series of variables, such as primary electron energy, ion source temperature, type and pressure of the reagent gas, etc.6,10

4.2.1.3 Atmospheric Chemical Ionization Atmospheric pressure chemical ionization (APCI) has recently gained a certain popularity in the GC-​MS field, even though its use is by no means novel.11 In current-​day GC-​APCI MS experiments, a corona needle discharge creates a plasma, this being exploited for analyte ionization. If nitrogen is used as make-​up gas, then the formed species N2+• and N4+• induce the generation of an intense MI, accompanied by reduced fragmentation. Furthermore, the presence of water vapor in the source, even in traces, will promote the formation of protonated MIs.12 Even though the presence of the [M + H]+ species can enhance the information content of a mass spectrum, it also is noteworthy that it can also complicate the evaluation of isotopic clusters. In fact, there is only a 4.5 mDa mass difference between the 13C isotope of an MI, and the (monoisotopic) [M + H]+ species.

4.2.1.4 Single Photon Ionization Single photon ionization (SPI) is enabled by a pulsing laser, which generates vacuum ultraviolet (VUV) photons, with sufficient energy to induce soft and universal ionization. Eschner et al. developed a GC-​MS instrument, capable of alternate generation of SPI and EI data, through rapid switching between both ionization approaches.13 More specifically, SPI (9.8 eV, 126 nm wavelength) was applied continuously, while EI was activated, and then deactivated, every 100 ms. So, alternate SPI and EI + SPI processes were performed. The authors affirmed that organic compounds can be universally ionized using photon energies of approximately 10 eV. The SPI GC-​MS result for a diesel fuel was visualized in three dimensions (first dimension: tR; second dimension: m/​z values; third dimension: signal intensity); the responses for the aromatic compounds, compared to the saturated ones, were higher. Furthermore, the EI + SPI total ion current (TIC) result was higher by approximately three orders of magnitude with respect to that generated by SPI.

4.2.2 Mass Analyzers There are a wide variety of single and coupled mass analyzers available to the current-​day GC-​MS food analyst; it is obvious that the selection of an MS system depends on different factors, with price and ease-​of-​use being key aspects next to the technical differences. The most commonly used instrument in the food-​analysis field (and not only there) corresponds to the

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simplest to operate and least-​expensive one, namely the single quadrupole (Q) mass spectrometer. Important technical MS characteristics to be considered are specificity and sensitivity; specificity depends on various factors such as mass resolution, mass accuracy and specific operational modes, such as selected-​ion-​monitoring (SIM), multiple reaction monitoring (MRM), etc. Sensitivity depends on the duty cycle (% of ions which reach the detector) and is also related to specificity, inasmuch that the background noise is greatly reduced under certain MS operational conditions (e.g., MRM, extraction of accurate-​mass ion chromatograms, etc.). The mass spectral production frequency must guarantee a minimum of some 10 data points per peak for reliable quantification, with no problems in such a respect usually encountered in conventional GC-​MS analyses where peak widths at the base are normally > 5 s. The mass range must cover the molecular masses of the sample analytes, with again no problems met in GC-​MS applications (ions are rarely monitored above m/z 600). A series of basic MS concepts and definitions, mainly related to the mass analyzer, follow. The mass spectrometer generates native mass spectra, characterized by peaks with a width and height (profile data), each representing a specific m/​z value. Mass spectra are nearly always represented by lines located at a specific m/​z value (peak centroid data). The most intense signal in the mass spectrum corresponds to the base peak, and is assigned a relative abundance of 100%. Obviously, all the other ions are characterized by an abundance lower than 100%. Absolute ion intensities must also be considered, in particular when performing analyte quantification. Mass resolution is the minimum m/​z difference (Δm/​z) that can be distinguished at a specific m/​z value, and is calculated by dividing a specific mass value by the peak width (usually at half height, fwhm). A mass resolution of 1 000 essentially means that the mass spectrometer is capable of separating m/​z values of 1 000 and 1 001. Mass accuracy can be expressed in absolute terms (measured mass –​theoretical mass), or in relative terms (ppm) as the mass error (absolute mass accuracy /​theoretical mass × 106). Mass analyzers can be classified as either scanning or non-​scanning; the former systems (i.e., QMS) monitor m/​z values one at a time, while non-​ scanning devices receive packets of ions across the applied mass range (i.e., time-​of-​flight MS). In the present section, focus will be devoted to the mass analyzers most used in the food analysis field, along with other less-​common but powerful devices. A brief description is also given on ion-​mobility spectrometry, due to its points in common with mass spectrometry. A description of the main MS detectors will not be herein given. The reader is directed to the literature for information on the most widely used detectors in mass spectrometry.14,15

4.2.2.1 Single-​quadrupole  MS A linear single-​quadrupole mass analyzer consists of four cylindrical or hyperbolically shaped rods, which act as a mass filter. On the basis of the applied

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voltages, the mass analyzer transmits packets of ions to the detector, at one specific m/​z value at a time, across a specific mass range. In fact, only ions with a stable trajectory reach the detector, while all others are eliminated. Single-​ quadrupole MS systems are, therefore, scanning devices, and are characterized by unit-​mass resolution and by a low duty cycle (≈0.1%). The operational modes for untargeted and targeted analysis, are scan and SIM, respectively. Targeted determinations, performed in the SIM mode, involve a specific number of previously defined analytes (e.g., pesticides in a vegetable), quantified through the selection of a characteristic ion, named as the quantifier. Quantifier ions should be as unique as possible to each target compound, with higher masses usually preferred over lower ones. One or two other characteristic ions are selected as “qualifiers”, with these enabling analyte positive identification if specific quantifier/​qualifier ion ratios remain within a pre-​ specified tolerance range. Gas chromatography retention time data (e.g., LRI values) are also used for identification purposes (again, tolerance ranges can be applied). Quantifier and qualifier ion information, as well as retention time data, are best attained through the preliminary injection of pure standard compounds, although reliable information is also available in MS spectral and LRI databases. Obviously, solutions of pure standard compounds are also used to generate calibrations functions and obtain method validation data (some examples will be given later in this chapter). Quantification can also be performed through extracted-​ion chromatograms (EICs), again by using quantifier and qualifier ions. The monitoring of a few specific ions per compound is a more sensitive option compared to the extraction of specific masses from the scan data. At the same time, however, the SIM mode can only be used in targeted determinations, because it lacks full-​scan spectral information. As mentioned previously, QMS instruments are the most commonly used in the GC-​MS field; apart from their rather limited economical costs and their structural robustness, an important additional advantage is the fact that the majority of commercial MS databases contain QMS spectra. In the presence of rapidly eluting peaks (usually not the case in conventional GC-​MS analyses), such as those generated in high-​speed GC and comprehensive 2D GC applications, mass spectral consistency can be low. Such a phenomenon is defined skewing and is caused by the rapid variation of analyte concentration within the ion source during the (slower) scanning process.15,16 In recent years, QMS technology has evolved greatly, to the extent that such instrumentation is now capable of generating 50 spectra s–​1, under commonly applied mass range conditions (i.e., m/​z 40–​330). Additionally, it is now possible to switch back and forth between the full-​scan and SIM modes, producing both untargeted and targeted information during the same analysis.17

4.2.2.2 Time-​of-​flight  MS The concept of ToFMS is rather straightforward: ions of different masses are separated in space within a field-​free flight tube. As long as the packets of ions across a specific mass range begin their “flight” at the same time, higher-​mass

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ions will take more time to reach the detector with respect to lighter ones. Depending on the length and geometry of the flight path, there are two types of ToFMS systems, namely low-​resolution and high-​resolution: LR devices are usually considered as unit-​mass resolution devices, while resolution values of 60 000 (fwhm) are currently achievable with specific HR instrumentation.18 Today, both types of instrumentation are usually characterized by pulsed orthogonal acceleration of the ion beam exiting the ion source (reducing the dispersion of ions with the same m/​z value in space). The reflectron is a further important part of modern-​day ToFMS, because it acts as an ion mirror, focusing ions with the same m/​z value, but with a slightly different kinetic energy. The reflectron generates a decelerating electric field, and is positioned at the end of the field-​free flight region. Ions penetrate the reflectron until they reach a kinetic energy of zero and are then launched backwards from the reflectron. Among ions with the same m/​z value, those with a slightly higher kinetic energy will arrive first to the reflectron, but will penetrate further therein; the opposite occurs for ions with a slightly lower kinetic energy. Time-​of-​flight MS systems are characterized by very high spectral generation rates (e.g., 500 Hz for LR systems) and by optimum deconvolution capabilities. The former feature has made such instrumentation a prime choice in high-​speed GC-​MS and comprehensive 2D GC-​MS applications (see Chapters 5 and 7). The good ability to resolve co-​eluting peaks at the GC column outlet (deconvolution) is a consequence of the high consistency of mass spectral profiles during compound elution (no MS skewing) and is performed (mathematically) by dedicated deconvolution software. Furthermore, duty cycles (and sensitivities) can be higher with respect to scanning devices, because the mass analyzer receives packets of ions across the entire applied mass range. Finally, QMS and ToFMS spectra, attained under the same ionization conditions, are altogether very similar, albeit not fully equal. Targeted quantification can be performed through EICs with no loss in sensitivity. It is noteworthy that LR ToFMS EICs are usually derived from nominal masses in an altogether similar manner with respect to QMS. The situation is entirely different in the case of HR ToFMS, inasmuch as EICs are derived through calculated exact masses, using a tolerance range, often expressed in ppm (e.g., ± 5 ppm). The extraction of exact mass values enables a great increase in specificity and consequently in sensitivity compared to the extraction of nominal masses. Finally, if the full-​spectrum HR ToFMS data are stored, then they can be investigated (if required) at a later stage to pinpoint previously unsearched compounds, again by extracting exact masses (post-​targeted analysis).15,16

4.2.2.3 Triple-​quadrupole  MS The use of two mass analyzers in sequence can greatly increase both specificity and sensitivity. Additionally, tandem MS (MSMS) processes can also be exploited for reliable peak identification. The most popular form of MSMS instrumentation used in the GC-​MS field is triple-​quadrupole (QqQ) MS, with

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the resulting hyphenated technology most often used in targeted applications. In QqQMS systems, MSMS processes are performed in space. Current QqQMS systems are composed of two quadrupole analyzers (for ease, defined here as Q1 and Q3), separated by a collision cell (normally a hexapole or octapole). Within the cell, collision-​induced dissociation (CID) processes can occur, with these promoted by the presence of an inert gas, such as Ar. There are various possible QqQMS operational modes with the most common being multiple reaction monitoring (MRM). In MRM analyses, Q1 and Q3 are operated both in the SIM mode; the first analyzer enables the isolation of one or a few precursor ions sequentially, with each of these subjected to CID. The second analyzer enables the isolation of at least two product ions, a qualifier and a quantifier ion. The use of MRM enables the reduction, or the elimination, of interfering mass spectral signals related to, for example, the matrix and column bleed. The selection of higher-​mass precursor ions will usually guarantee a greater specificity, compared to lower masses. Other MSMS modes are: (1) precursor ion scan, in which Q3 monitors a single ion, derived from CID of the ions transmitted by Q1 (Q1 scan–​Q3 SIM). Such a determination will contain a record of compounds characterized by a specific functional group (e.g., phenyl); (2) product ion scan, during which the ions derived from the CID of the precursor ion are monitored (Q1 SIM–​ Q3 scan). Such experiments will enable the selection of the most appropriate product ions for specific analytes in a pre-​targeted analysis. This mode is also used for identification of specific compounds in complex matrices; and (3) neutral-​loss scan, in which the two quadrupoles are synchronized in such a manner that the difference in mass of ions passing through the mass analyzers is constant (Q1 scan–​Q3 scan). Neutral-​loss scan is exploited to detect compounds that contain a specific substituent (e.g., Cl).19,20

4.2.2.4 Quadrupole-​time-​of-​flight  MS The combined use of a quadrupole analyzer, with a high-​resolution ToF one (QToF), generates a flexible and powerful hybrid instrument, with the ability to perform as a single-​or double-​analyzer. This hybrid MS approach is also very attractive due to the simplicity of use of the quadrupole, and the enhanced performance of the ToF analyzer. The QToFMS approach can be described in the simplest manner by considering it as a QqQMS system, with the Q3 analyzer replaced by a high-​resolution ToF one (as for QqQMS systems, MSMS processes are again performed in space). A scheme of an orthogonally accelerated QToFMS instrument is reported in Figure 4.1. The curved blue line represents the flight path, comprising the interaction with the reflectron, positioned behind the flight tube. The quadrupole can be operated in the “radio-​frequency-​only mode”, acting as a field-​free region, when single-​ stage MS processes are desired. In such a manner, high-​resolution spectra across the applied mass range can be attained. In the MSMS mode, the quadrupole is used to isolate and accelerate precursor ions to the collision

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Figure 4.1 A scheme of an orthogonally accelerated QToFMS system. Reproduced from Ref. 54 with permission from John Wiley and Sons, Copyright © 2014 Wiley Periodicals, Inc.

cell, within which CID occurs. The result of a QToF MSMS process is a product ion spectrum, characterized by high levels of both resolution and mass accuracy. Product ion spectra can be highly useful for structural elucidation; additionally, highly specific exact-​mass EICs can be used for the scope of quantification.15,19,20

4.2.2.5 Other Analyzers In Orbitrap, MS ions are trapped in an electrostatic field, between an inner spindle-​shaped electrode and an outer barrel-​shaped one, with the latter divided into two equal parts. The ions orbit around the inner electrode, moving backwards and forwards along it (axial oscillation). The image current generated by the oscillating ions is subjected to measurement, and then translated into a frequency domain signal through Fourier transform (FT), leading to high mass-​accuracy information. The frequency of the axial oscillation is inversely proportional to the square root of the m/​z value. The resolving power and mass accuracy of Orbitrap MS instrumentation is extremely high, being

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comparable to that of FT ion cyclotron resonance MS. The development of a GC-​Orbitrap MS system was reported in 2014.21 The discrimination of isotopes during plant biosynthesis can be exploited in studies focused on the evaluation of genuineness and/​or geographical origin of plant-​related food products. The isotopic ratios of specific elements contained in volatile food constituents can be measured with great precision by using GC combined with isotope-​ratio mass spectrometry (IRMS).22 More specifically, GC-​IRMS can measure deviations of specific isotope ratios (i.e., 13 C/​12C), from a defined standard, at the parts-​per-​thousand level (expressed by the dimensionless value δ). A characteristic of IRMS is that the monitored element must be transformed into a gas (e.g., C → CO2) prior to the ionization process. Ion separation occurs within a single magnetic sector, while Faraday cups are employed for detection. The most commonly monitored element is C, even though H, O, N and S can be analyzed also.23 As reported in Chapter 6 (Section 6.5), the use of multidimensional GC combined with IRMS is a prime choice to establish reliable isotope ratios when analyzing food samples. The reason for such a preference is related to the fact that the various IRMS instrumental parts and connections (in particular the combustion chamber), located between the column outlet and the ion source, can contribute significantly to band broadening. Such a reduction in resolving power will lead to an increase in the probability of peak overlapping using a single GC column, and hence in the production of incorrect isotope ratio results. Ion-​mobility spectrometry (IMS) has some common points with MS, inasmuch that the generation, separation and detection of ions are factors that occur in both technologies. In a traditional GC-​IMS system (drift time IMS), neutral compounds are introduced into an ionization region, and are usually converted into monomers, sometimes dimers, and even trimer ions. The formed ions are introduced into a drift region through an ion gate, for a specific brief time interval (50–​200 μs). The ions are exposed to a weak electric field, and travel within the drift tube where they interact with a counter flow of drift gas (i.e., N2); specifically, the energy provided to the ions by the electric field is reduced by the collisions with the drift gas molecules. Such a characteristic is attractive because ions with the same mass, although with different structure, can be characterized by a different mobility and, thus, be resolved very rapidly (ms time scale) and with high efficiency. Apart from drift time IMS, other instrumental forms of IMS have been used (i.e., traveling-​wave IMS, open loop IMS, differential mobility analyser, etc.).24 Ion-​mobility spectrometry is complementary to mass spectrometry, inasmuch that IMS separations depend on the molecule shape, size and charge. Hence, the coupling of IMS with MS spectrometry forms a two-​dimensional system, with an enhanced separation and identification power. Furthermore, if a GC system is combined with an IMS-​MS one, then a powerful three-​ dimensional instrument is attained.24 The use of GC-​IMS-​MS in the food analysis field is rarely described.

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4.3.1 Single Quadrupole MS Applications Headspace solid-​phase microextraction (HS SPME), combined with GC-​ QMS, was used by Mondello et al. to investigate a highly complex type of sample, namely coffee bean headspace.25 Coffee, together with tea, is the most popular beverage in the world. Robusta and Arabica are the two types of coffee subjected to industrial treatment, with the latter being the most appreciated by consumers. Coffee roasting is a necessary step to trigger the Maillard reaction, leading to the generation of an extremely high number of volatile compounds.26 Mondello et al. used a 30 m × 0.25 mm ID × 0.25 μm df polyethylene glycol column to investigate coffee beans characterized by: (1) different roasting degrees (Arabica); (2) the same roasting process, but of different type (Arabica and Robusta); (3) three geographical origins, for both Arabica and Robusta roasted samples; and (4) a different initial treatment (dry and wet, Robusta). Total-​ion-​current (TIC) HS SPME-​GC-​QMS chromatograms for samples of green and roasted Arabica coffees are reported in Figure 4.2. Tentative peak identification was carried out by applying two filters during the mass spectral matching process: (1) minimum accepted similarity value: 90%; and (2) LRI tolerance range: ± 10 units (a possible database match with a similarity ≥ 90%, but with an LRI value outside the tolerance range was eliminated). Such a dual-​filtered search process simplifies the identification process, increases its reliability and is particularly useful in the GC-​MS analysis of samples composed of chemically similar compounds (e.g., essential oil terpenes). For example, for peaks 29 (2,5-​dimethylpyrazine, LRI: 1 324), 30 (2,6-​dimethylpyrazine, LRI: 1 330) and 33 (2,3-​dimethylpyrazine, LRI: 1 348) in the roasted coffee chromatogram (lower trace), compounds with a very similar MS behavior, the use of retention data was fundamental for correct peak assignment. Overall, 27 compounds were tentatively identified in green coffee and 57 in the roasted sample. As will be illustrated in Chapter 7 (Section 7.3), the volatile fraction of roasted Arabica coffee was far from being fully separated. Current trends are to include a high number of compounds in multiresidue food investigations because sample preparation usually involves the use of organic extraction solvents and because current MS devices should guarantee high levels of specificity and sensitivity. With this in mind, the performances of GC-​QMS and GC-​HR ToFMS have been compared in a multiresidue study involving 170 organohalogen and organophosphorus analytes in dried ginseng roots.27 Powdered ginseng contained in dietary supplements is derived from the roots of Panax quinquefolius (American ginseng) or Panax ginseng (Asian ginseng), with its consumption related to a series of health benefits. As for any other vegetable, ginseng is exposed to the use of agents for pest control, which can potentially lead to the contamination of the final product. In the comparative GC-​MS study, 2.5 g of dry powdered ginseng were subjected to ethyl acetate extraction, leading to a final 5-​mL solution.

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Figure 4.2 Headspace SPME-​GC-​QMS chromatograms of a green (upper chromatogram) and roasted sample of Arabica coffee. Refer to Ref. 25 for peak identification. Reproduced from Ref. 25 with permission from John Wiley and Sons, Copyright © 2005 WILEY-​VCH Verlag GmbH & Co. KGaA, Weinheim.

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Further clean-​up was performed by using gel-​permeation chromatography (a glass column was packed with styrene divinylbenzene beads): the highest molecular weight analyte, acrinathrin, was the first to elute at just over 8.5 min. A mobile phase (70 : 30, ethyl acetate : cyclohexane) volume of 47 mL was collected and reduced to approximately 1 mL. The final clean-​up step was carried out by using a tandem-​bed SPE cartridge: graphitized carbon black at the top, followed by primary secondary amine (PSA). The former sorbent can efficiently remove vegetable pigments, while PSA is used to eliminate polar matrix interferences such as fatty acids, sugars, etc. A sample volume of 1 μL was injected in the splitless mode in both GC-​MS systems. However, an initial injector pressure pulse of 2 min was used only in the GC-​QMS applications. In principle, the specificity and sensitivity of HR ToFMS in targeted applications is higher compared to QMS in the SIM mode, due to the possibility to extract chromatograms using accurate-​mass ions. However, similar (geometric mean) LoQs of 3 and 4 ng g–​1 were reported for the HR ToFMS and QMS approaches, respectively (matrix-​matched calibration was used). It is noteworthy that such values were rather near the (European Union) imposed maximum residue limit (MRL) of 10 ng g–​1 for the majority of pesticides in vegetable food products. The reasons for such a between-​method LoQ similarity were probably related to the only moderately high resolution capabilities of the HR ToFMS used (reported to be > 6 000) and to the multistep clean-​up process, enabling the efficient removal of matrix interferences. European Union (EU) MRLs were considered by Banerjee et al. during the development and validation of a pre-​targeted GC-​QMS method for the quantification of 47 pesticides in grapes.28 A QuEChERS method was used for sample preparation: 10 mL of ethyl acetate and 10 g of anhydrous sodium sulfate were added to 10 g of homogenized grapes, with the mixture subjected to homogenization and then centrifugation. A volume of 1 mL of the supernatant was subjected to dispersive-​SPE by using PSA for the selective removal of polar interferences. After a final centrifugation step, 0.8 mL of supernatant was transferred to an autosampler vial, to which 0.1% of formic acid was added. In fact, it was found that the stability of the pesticides over time was better in an acidified solution. A rather large sample volume (8 μL) was introduced into a PTV injector, with this heated from 70°C (0.07 min) to 87°C (0.1 min) at 50°C min–​1, and to 280°C at 400°C min–​1. The solvent was vented for 0.17 min, prior to closure of the vent valve (4 min). Calibration solutions were prepared at the concentration levels of 0.01, 0.02, 0.04, 0.05, 0.10 and 0.25 μg mL–​1. Matrix-​ matched calibration was performed by spiking extracts of organically grown grapes at the same concentration levels. Quantification and identification were performed in the SIM mode, using one quantifier and two qualifier ions. The authors used a ± 15% tolerance range, considering the relative intensity of the qualifier ion with respect to that of the quantifier ion. Linearity was satisfactory across the calibration range, while LoQs were above 10 ppb only for six analytes, although in all cases below or equal to their EU MRLs. Matrix effects were also evaluated. This was done by considering the peak area ratios derived by analyzing the matrix-​matched and solvent standard solutions (the

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values were expressed as a %) and were found to be limited, with a maximum enhancement value of 33%, and suppression values always lower than 20%. Recovery experiments were carried out by fortifying untreated grapes at the 10 and 20 ppb levels, and were found to be within the range 67–​120%, with relative standard deviations (RSDs) always below 19%.

4.3.2 Time-​of-​flight  MS Applications As mentioned previously, in recent years the utility of LR ToFMS has been highlighted mainly in GC applications characterized by rapid peak elution. For such a reason it will be treated in more detail in Chapters 5 and 7. However, the use of conventional GC combined with LR ToFMS has been described in a range of food applications. For example, de Koning et al. used GC-​LR ToFMS for the determination of 13 pesticides in pineapple and grapes.29 Rather than the (low) number of pesticides, more noteworthy was the sample introduction technique, namely automated difficult matrix introduction (DMI). The approach consisted in the use of a micro-​vial containing a small volume of sample extract, placed in a wider liner (80 mm × 3.4 mm ID) for thermal desorption. The majority of the sample solvent (ethyl acetate) was eliminated in 120 s at an injector gas flow of 150 mL min–​1, and at a temperature of 50°C. After solvent elimination, the injector was rapidly heated to 280°C to thermally desorb the pesticides. The LoDs (limits of detection) were found to be in the range 1–​10 ng g–​1. The mass spectrometer was operated at a spectral acquisition rate of 20 Hz, and it was affirmed that a retention time difference of only 0.1 s was sufficient for effective deconvolution. The DMI process proposed by the authors was certainly both rapid and simple, with the main disadvantage being the potential introduction onto the GC column of many matrix interferences. As mentioned previously, mass spectrometry can be considered, in its own right, as an independent analytical (separation) dimension. It is noteworthy, however, that the mass-​analyzer separation capability is often hampered in 70-​eV EI processes by the generation of a high number of mostly low-​mass ions per analyte. On the contrary, if a softer ionization process is used (e.g., SMB EI, PI, APCI, etc.), inducing reduced MI fragmentation, then the separation potential of MS can become evident. In such a respect, Hejazi et al. exalted the 2D nature of GC-​ToFMS by using field ionization (FI) in applications involving vegetable and fish oils.30 Field ionization was the first form of SI, it being introduced in the 1950s. Low amounts of energy are involved in FI processes, having the beneficial effect of reduced MI fragmentation, unfortunately at the expense of a lower ionization efficiency. In historical terms, FI has been most often exploited in the petrochemical field.31 Hejazi et al. applied the method in food analysis. These authors performed a transesterification process of edible oil –​triacylglycerols (TAGs) to fatty acid methyl esters (FAMEs) –​by using methanol containing 1% of sulfuric acid. The GC analyses were performed on a highly polar (70% cyanopropyl polysilphenylene-​siloxane) 60 m × 0.25 mm ID × 0.25 μm df column, with

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an enhanced selectivity for (cis/​trans) FAME separation. With regard to the ToFMS instrument, this was characterized by a moderately high resolution (7 000 fwhm). It is noteworthy, however, that because FAMEs usually differ by a minimum of two mass units (presence of 0, 1, 2, etc. double bonds), the use of HR MS in this case was superfluous. In the first analytical dimension, the GC, the FAMEs were resolved on the basis of both mass and polarity, whereas they were obviously resolved only on the basis of mass in the second analytical MS dimension. The GC-​FI HR ToFMS data were visualized in a 3D format: the y-​axis was defined by retention times, while the x-​axis was defined by m/​z values. The GC-​FI HR ToFMS result for a fish oil is shown in Figure 4.3, with it being very similar to comprehensive 2D GC FAME results (Chapter 7). Such a highly organized elution pattern was attained through the manipulation of the retention times, so that the saturated FAMEs were aligned horizontally. Retention times relative to the saturated FAMEs were then extrapolated for the other methyl esters. The only case for which HR MS proved its usefulness was for the identification of the antioxidant butylated hydroxytoluene, at an m/​z value of 220.1807 (mass error: 9.02 ppm). In a food-​related research, Salivo et al. used GC-​HR ToFMS for the qualitative analysis of the unsaponifiable lipid fraction of human plasma, with particular focus on the sterols. The lipids contained in human plasma, or in food products such as vegetable oils, are classified as either saponifiable or

Figure 4.3 The TIC GC-​FI HR ToFMS result for fish oil. Reproduced from Ref. 30 with permission from American Chemical Society, Copyright 2009.

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unsaponifiable constituents. The former major class comprises compounds with a hydrolyzable fatty acid, such as TAGs, sterol esters, phospholipids, etc.; on the other hand, the unsaponifiable fraction forms a group of minor compounds, containing squalene, free sterols, lipid-​soluble vitamins, etc. With regards to vegetable oils, the GC analysis of these lipid constituents can reveal the presence of low amounts of a cheaper oil added to a more expensive one.33 The plasma lipid profile, on the other hand, does reflect the nutritional habits of each individual.34 For example, cholesterol oxidation products (COPs) can have negative effects on health, and can be formed through endogenous processes or derive directly from food.35 Salivo et al. extracted the lipid fraction from plasma by using a 2 : 1 chloroform : methanol mixture; the plasma lipids were subjected to cold saponification, with the use of 2,6-​di-​tert-​butyl-​4-​methylphenol as antioxidant. The isolated unsaponifiable fraction was subjected to derivatization –​compounds with hydroxyl groups (e.g., sterols) were transformed to trimethyl silyl (TMS) ethers. The plasma lipid constituents were then separated on a low-​polarity 15-​m micro-​ bore (0.1 mm ID) column, prior to mass spectrometry. The HR ToFMS system used was characterized by a compact mass analyzer (multireflectron), and by two mass resolution operational modes –​high (≥ 25 000 fwhm) and ultra-​high (≥ 50 000 fwhm). A unique feature of the mass spectrometer was its capability to generate up to 200 spectra s–​1, under both resolution modes, with mass accuracies often below 1 ppm. A spectral generation frequency of 4 Hz was used, being sufficient for deconvolution purposes (approximately 20–​30 data points per peak are required), under high-​resolution conditions. Attention was directed, in particular, to the sterol fraction: the deconvolution algorithm generated 20 distinct mass spectra. An example highlighting the separation potential of the MS dimension can be found in the GC-​HR ToF MS chromatogram expansion shown in Figure 4.4: five peaks overlapping at the GC outlet were resolved by the combination of mass spectrometry and deconvolution. The mass spectrum attained for TMS-​derivatized 7β-​hydroxycholesterol (C33H62O2Si2; molecular weight = 546.42884 u), a COP, along with its molecular structure, are illustrated in Figure 4.5. As can be seen, the mass spectrum is rather “noisy” even though a distinct ion at m/​z 456.37849 is present, being generated by the molecular ion losing a TMS-​OH group (70 eV EI was used). Pintado-​Herrera et al. used GC-​HR ToFMS, with APCI, for the simultaneous determination of 102 regulated and emerging contaminants in different types of water samples (wastewater, sea water, river water and water from a well).36 Due to the vicinity of such a type of application to food analysis (e.g., well water analysis), it is described herein. The authors first added acetic anhydride to the water samples to derivatize the hydroxyl groups (sodium carbonate was used to maintain pH = 7). Next, they used stir bar sorptive extraction (SBSE), using polydimethylsiloxane (PDMS) with a 0.5-​mm film thickness, for target analyte extraction (see Chapter 1) from 100-​mL water samples. The ionic strength of the sample was increased by adding NaCl to reach a concentration of 100 g L−1. Salting out decreased the solubility of weakly polar analytes [compounds with lower (< 4) octanol–​water partition coefficients – ​log Kow],

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4 -hydroxycholesterol

lathosterol

7-dehydrocholesterol

cholesterol 7 -hydroxycholesterol

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Figure 4.4 A GC-​HR ToFMS chromatogram expansion, generated by extracting accurate-​mass ions, showing five (TMS-​derivatized) sterols identified in a sample of human plasma. Reproduced from Ref. 55 with permission from Elsevier, Copyright 2019.

Figure 4.5 Deconvoluted mass spectrum of derivatized 7β-​hydroxycholesterol and its molecular structure.

while increasing their concentration in the extraction phase. The extraction process was performed through stirring and lasted 5 h. Afterwards, the absorbed analytes were desorbed through ethyl acetate extraction and then subjected to GC-​APCI HR ToFMS analysis. Calibration was performed by using spiked pure water solutions, while a series of deuterated compounds were also used to compensate for variations during extraction and the final

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analysis. Method detection limits were very low, namely below 1 ng L−1 for more than 70% of the analytes. Matrix effects were found to be limited, reaching 30% in the dirtier water samples. The general high sensitivity reported by the authors was due to the combined effects of using SBSE, extracted accurate-​ mass chromatograms and APCI. The use of APCI enabled the selection of intense higher molecular-​weight (often protonated MIs) quantifier ions, compared to classical EI. In a similar GC-​HR ToFMS research (this time with EI), Hernández et al. used in-​sample SPME for the targeted analysis of 60 water contaminants, including octyl/​ nonyl phenols, pentachlorobenzene, pesticides and (poly) aromatic hydrocarbons (PAHs).37 Apart from well water, surface water, wastewater and urban solid waste leachates were subjected to analysis (a total of 10 samples). In-​sample SPME was performed by using a Carbowax/​divinylbenzene fiber, exposed for 45 min at ambient temperature to 4 mL of sample, containing 10% NaCl. Several deuterated internal standards (ISs) were used, namely:  p,p′-​hexachlorobenzene (HCB)-​13C6 for pentachlorobenzene, trifluraline and HCB; lindane-​d6 for lindane; p,p′-​dichlorodiphenyldichloroethylene (DDE)-​d8 for the polychlorinated biphenyls and remaining chlorinated pesticides; terbuthylazine-​d5 for the herbicides, octyl/​nonyl phenols and organophosphorus pesticides; benzo(a)anthracene-​d12 for the PAHs. Injector thermal desorption was carried out at 250°C for 5 min, while the target analytes were separated on a conventional low-​polarity column, and finally subjected to moderately high-​resolution MS (7 000 fwhm). Seven-​point calibration was carried out across the concentration range 0.01–​5 μg L−1; the LoQ was considered the lowest calibration concentration level at which at least one Q/​q value (Q: quantifier ion; q: qualifier ion) fell within a predefined tolerance window. Such a procedure was used to extrapolate LoQ values because often it can be difficult to measure noise widths when accurate-​mass ions are extracted (the noise is practically equal to zero in several cases) –​in this case a ± 10 mDa tolerance window was applied. For all but seven contaminants LoQs were ≤ 0.05 μg L−1. For the purpose of identification, four qualifier ions were selected for the majority of the target analytes. The Q/​q tolerance ranges applied were those reported in European legislation (2002/​657/​EC). Six target compounds –​four herbicides (simazine, terbuthylazine, terbumeton and terbutryn), chlorpyrifos (an insecticide) and 4-​t-​octylphenol (a detergent derivative) –​were quantified across the concentration range 0.01–​1.5 μg L−1; simazine, terbuthylazine (the most frequently detected contaminant), terbumeton and terbutryn were found in more than one sample. The EICs for terbuthylazine (0.2 μg L–​1), chlorpyrifos (0.4 μg L–​1) and terbutryn (0.1 μg L–​1), pinpointed in a sample of surface water, are shown in Figure 4.6. In all cases, the Q/​q values matched well with those attained using the calibration solutions. Following the targeted process, post-​targeted analysis was carried out for six fungicides and 11 polybrominated diphenyl ethers (PBDEs). Three fungicides were detected (diphenylamine, thiabendazole and imazalil) and quantified in the concentration range 0.1–​1 μg L−1 after a new calibration process.

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Figure 4.6 Accurate-​mass extracted ion chromatograms for three target contaminants contained in a sample of surface water. Abbreviations: St: standard compound; W: water sample; percentages values relate to the difference of the experimental Q/​q values, from the calibration solution ones. Reproduced from Ref. 37 with permission from the American Chemical Society, Copyright 2007.

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Figure 4.7 Diazinon peaks reconstructed through four accurate-​mass ions (A), the MS database spectrum of diazinon (B), the deconvoluted spectrum of diazinon (C), the forward match, the (accurate) masses of five representative ions, and mass errors in mDa (in brackets) (D). Reproduced from Ref. 37 with permission from the American Chemical Society, Copyright 2007.

Finally, the presence of full-​spectrum information enabled the investigation of untargeted contaminants; among several additional pollutants, the organophosphorus insecticide diazinon (or dimpylate) was found in several samples. Figure 4.7 reports four different accurate-​mass traces for diazinon (A), its database (B) and deconvoluted (C) mass spectrum, and the forward match, the accurate masses of five fragment ions and mass errors (values in brackets) expressed in mDa (D), with a maximum deviation of –​1.3 mDa (approximately 9.5 ppm). It is noteworthy that the diazinon peaks in Figure 4.7 are characterized by triangle-​like shapes; such a non-​satisfactory peak reconstruction was due to the low spectral acquisition rate applied (1 Hz), which appears to be excessively low for the scopes of reliable deconvolution and quantification.

4.3.3 Triple Quadrupole MS Applications The use of GC-​QqQMS as an alternative to magnetic sector HR MS has been recently proposed by the EU (2014) to confirm the compliance (or not) of the amounts of dioxins in food and feed.38,39 Apart from the type of GC-​MS methodology used, the sample preparation process is of the highest importance in such an applicational field. Polychlorinated dibenzo-​ p-​ dioxins and furans (PCDD/​ Fs) –​defined as dioxins –​and dioxin-​like polychlorinated biphenyls (DL-​PCBs) –​mono-​ortho and non-​ortho substituted congeners –​are well-​known groups of persistent organic pollutants (POPs). These highly toxic compounds are included in the

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Stockholm Convention on POPs, which entered into force in May 2004, with the aim of protecting both human health and the environment. García-​Bermejo et al. developed and validated GC-​EI QqQMS methods for the determination of 17 PCDD/​Fs and 12 DL-​PCBs in food (milk powder) and feedstuff (samples typically characterized by low dioxin concentrations), sewage slush (intermediate degree of dioxin contamination) and fly ash (high level of dioxin contamination).40 The GC-​EI QqQMS results were compared side by side with those attained by using the standard method GC-​EI magnetic sector MS. The milk powder, a certified reference material, was spiked with known amounts of surrogate standards (15 deuterated PCDD/​Fs and 12 deuterated DL-​PCBs). A 20-g amount of the powder was subjected to an extraction process using a 1 : 1 mixture of acetone and hexane. Afterwards, sample clean-​up was performed using a sequence of columns packed with different adsorbants, enabling the isolation of a DL-​PCB fraction and a PCDD/​F one, with both subjected to a sample pre-​concentration step. It is noteworthy that recoveries exceeded 65% in all instances. Prior to GC-​MS analysis, the extracts were spiked with known amounts of further deuterated PCDD/​Fs and DL-​PCBs to monitor the injection process. A standard solution, containing target analytes at low concentration levels, was injected in a cyclic manner to monitor the instrumental response. In the GC-​QqQMS applications, a PTV was used to transfer the solutes onto a low-​polarity 30 m × 0.25 mm ID × 0.25 μm df column; in the magnetic sector MS analyses, on the other hand, a split/​splitless injector was used (in the splitless mode) for sample introduction onto a low-​polarity 60 m × 0.25 mm ID × 0.25 μm df column. Different temperature programs were also applied, even though in both cases a sample volume of 1 μL was used. With regard to the EI QqQMS conditions, an ionization energy of 40 eV was selected, presumably to generate less fragmentation compared to 70 eV EI. The MRM mode was used for target analyte determination: the two most intense ions in the scan and in the product-​ion spectra were exploited as precursor and product ions, respectively. Figure 4.8 shows the scan spectrum of PCB 77, a non-​ortho substituted PCB, in which the isotopic cluster of the monoisotopic MI at m/​z 290 is well evident. The product ion spectra, derived from CID (Ar was used as collision gas) of the precursor ions at m/​z 290 and 292 (for the main part PCB 77 containing a single 37Cl isotope), are also illustrated in Figure 4.8. The product ions monitored, specifically m/​z 220 and 222, were generated from the loss of two Cl atoms. The low-​resolution nature of the mass analyzer used is readily visible from the profile data present in Figure 4.8. The magnetic sector MS was operated in the SIM mode, using an ionization energy of 32 eV, with a reported mass resolution of 10 000 (10% valley definition). Solutions of PCDD/​Fs across the concentration range 0.1–​400 pg μL–​1, containing the surrogate labeled standards at the 100 pg μL–​1 level (except for one which was at 200 pg μL–​1), were prepared to evaluate linearity and

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Figure 4.8 From left to right: PCB 77 scan spectrum; product-​ion-​scan spectra for the precursor ions at m/​z 292 (above) and 290. Reproduced from Ref. 40 with permission from Elsevier, Copyright 2015.

measure instrumental LoDs and LoQs. For the same scope, solutions of DL-​ PCBs across the concentration range 0.1–​200 pg μL–​1, containing the labeled standards at the 50 pg μL–​1 level, were prepared. The response of each target compound, relative to its labeled counterpart, was found to be linear in the following ranges: 0.1–​40 pg μL–​1 for tetraCDD/​Fs, 0.5–​200 pg μL–​1 for pentaCDD/​Fs, hexaCDD/​Fs and heptaCDD/​Fs, 1–​400 pg μL–​1 for octaCDD/​ Fs, and 0.1–​200 pg μL–​1 for DL-​PCBs. The instrumental LoDs and LoQs were extrapolated from the lowest calibration point: six consecutive analyses were performed, with the standard deviation relative to the peak areas of each target compound multiplied by 3 and 10, respectively. The measurement of signal-​to-​noise ratios was not performed because noise is often complicated to evaluate in MRM analyses, as well as in HR MS applications, it being practically nullified. With regard to the GC-​QqQMS method, the LoD ranges for the PCDD/​Fs and the DL-​PCBs were 0.07–​0.75 pg μL–​1 and 0.05–​0.63 pg μL–​1, respectively; passing to the GC-​magnetic sector MS method, the LoD ranges for the PCDD/​Fs and the DL-​PCBs were 0.007–​0.026 pg μL–​1 and 0.004–​0.007 pg μL–​1, respectively. As can be seen, the GC-​QqQMS approach was less sensitive, even though the reported instrumental LoQs (0.16–​2.5 pg μL–​1) were sufficiently low for food contaminant investigations. Moreover, both methods provided similar quantification results for the PCDD/​Fs present in the milk powder. It is noteworthy that GC-​QqQMS is more user-​friendly, and is

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both less expensive and maintenance-​demanding compared to GC-​magnetic sector MS. Brominated flame retardants (BFRs) are a further group of halogenated compounds of growing concern; as indicated by their name, BFRs are exploited to decrease the flammability of a variety of industrial products, i.e., furniture, electronics, cars, etc. These ubiquitous, persistent and toxic compounds can end up in great amounts in the environment, and then find their way into the food chain. In the BFR applicational field, various sensitive forms of GC-​MS have been used, such as GC combined with: EI SIM MS, EI MSMS, and electron-​ capture negative ionization (equivalent to NCI) SIM MS (in all cases a QqQMS system was employed) for drinking water,41 as well as magnetic sector EI SIM MS (along with QqQMS) in applications involving fish,42 and EI ion trap MSMS in analyses focused on palm oil, trout and chicken.43 As for dioxin analysis, the sample preparation method holds the same importance as the GC-​MS one. The EU recommends the use of methods for the quantification of BFR food contamination with LoQs ≤ 0.01 ng g–​1.44 With the scope of achieving such a high level of sensitivity, Portolés et al. developed a GC-​APCI QqQMS method for the targeted determination of 14 well-​known BFRs, containing 3–​10 Br atoms (polybrominated diphenyl ethers, PBDEs), and two novel BFRs, specifically 1,2-​bis(2,4,6-​tribromophenoxy)ethane (BTBPE) and decabromodiphenyl ethane (DBDPE, C14H4Br10).45 The sample preparation process was laborious: freeze-​dried marine samples (fish, prawn and squid) were subjected to homogenization, with a silica gel : anhydrous Na sulfate mixture (4 : 1 w/​w). Three labeled (13C12) PBDEs were used as surrogate standards, being added to the samples before the extraction process. The mixture was loaded onto a column, and the PBDEs were extracted with a 1 : 1 mixture of n-​hexane and acetone (matrix solid-​phase dispersion, MSPD). Additional cleanup and lipid removal were achieved by using multilayer columns, containing neutral, acid-​and base-​treated silica gel. Elution was performed by using n-​hexane. If required, the final extract could be subjected to an SPE process on graphitized non-​porous carbon to separate ortho substituted PCBs and PBDEs from non-​ ortho substituted PCBs and PCDD/​Fs.46 The scan APCI spectra of all the PBDEs were characterized by the presence of isotopic clusters related to the M+• and [M + H]+ ions, with one of these being the base peak. Both M+• and [M + H]+ ions were present in the BTBPE spectrum, but not as base peak; the spectrum for DBDPE was an exception, with neither the MI nor the protonated molecule being present, and the ion C8H4Br5+• forming the base peak. The scan APCI spectrum for 2,2-​, 3,3-​, 4,4-​, 5,5-​, 6,6-​deca-​BDE (BDE 209) is reported in Figure 4.9, highlighting the isotopic clusters of both the M+• and [M + H]+ ions. The scan EI spectrum is also shown, it being characterized by a low signal for the MI isotopic cluster. The presence of an open vial located in the source, containing water, increased the abundance of the [M + H]+ ion over the M+• one (proton-​transfer conditions). Again, the only exception was DBDPE, with no significant change in the fragmentation observed.

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Figure 4.9 Scan spectra for BDE 209, using APCI (top) and EI. Reproduced from Ref. 45 with permission from the American Chemical Society, Copyright 2015.

Sensitivity was excellent for both normal and proton-​transfer APCI processes, with instrumental LoDs (signal-​to-​noise ratio = 3) in the range 1–​10 fg (on-​column) using the latter approach and 1–​25 fg using the former. Furthermore, it was observed that the degree of in-​source fragmentation was lower under proton-​transfer APCI conditions. In general, the use of APCI provided enhanced sensitivity compared to EI; an example was given highlighting the GC-​APCI QqQMS quantification of BDE 209 in fish at the 27 pg g–​1 level [quantification transition m/​z 959 → 799 (–​2Br)]. The same contaminant was not detected using GC-​EI QqQMS. The determination of phytosanitary contamination in vegetable products, at trace-​amount levels, is a typical GC-​QqQMS application. In such a respect, Martínez Vidal et al. developed an EI GC-​QqQMS methodology with focus on 130 pesticides in cucumber.47 Sample preparation was simple: liquid–​solid extraction (5 g of cucumber and 10 mL ethyl acetate), followed by drying with anhydrous sodium sulfate and solvent evaporation. The residue was dissolved in 1 mL of cyclohexane, containing 0.5 μg mL​–1 of internal standard (caffeine).

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Large-​volume injection (10 μL) was performed in a slow manner (10 μL min–​1), to enable solvent elimination, prior to heating of the PTV injector. A porous carbon plug (Carbofrit) in the liner and a 2 m × 0.25 mm ID guard column were used to prevent losses of volatile species from the injector as well as to avoid potential matrix contamination of the analytical column. With regard to the MS process, it is worth noting that both PCI and NCI were also evaluated, with utility found only for a restricted number of contaminants. On the other hand, EI was found to be more useful, being more universal and certainly more advisable in studies involving a wide range of contaminants with different chemical characteristics. Calibration was performed by spiking the cucumber extracts at four concentration levels, between 10 and 200 μg kg–​1. Limits of quantification, determined by considering a signal-​to-​noise ratio of 10, were ≤ 9.6 μg kg–​1. Recovery values were between 69% and 125%, and were determined by spiking uncontaminated cucumber samples at three different concentration levels (25, 200 and 500 μg kg–​1). The specificity of QqQMS was emphasized by the authors, inasmuch that it was reported that the quantification of 28 co-​eluting target contaminants was possible, a factor which could not be achieved through ion-​trap MS. As for APCI, the specificity of MSMS processes can be enhanced through the use of a mild ionization process, such as SMB EI. For instance, it was found that the molecular ion of diazinon (m/​z 304), an organophosphorus pesticide, was about an order of magnitude more intense when using GC-​SMB EI QqQMS compared to GC-​EI QqQMS.48

4.3.4 Hybrid MS Applications Cherta et al. evaluated both GC-​EI HR ToFMS and GC-​APCI QToFMS for the untargeted analysis of undesirable compounds migrating from food-​contact materials to food.49 Migrants derived from such materials are certainly a potential danger to human health, with many regulated by specific migration limits.50 However, for other non-​regulated ones –​generically defined as NIAS (non-​intentionally added substances) –​the concern is higher. Such compounds may consist of impurities present in starting materials, may be formed during industrial production stages, or may be introduced through recycling processes. Cherta et al. investigated multilayer trays made of polypropylene/​ethylene vinyl alcohol/​polypropylene (PP/​EVOH/​PP) and a film composed of PP/​ Al foil/​PP. Isooctane was exploited as an oil-​type food simulant, whereas Tenax was used as a dry food one. After contact with the materials, isooctane was directly injected into the GC system, while Tenax was subjected to an extraction process with diethyl ether. An initial screening was carried out using GC-​EI HR ToF MS: only database matches in excess of 700 were taken into consideration. Then, the five most intense ions for each candidate were subjected to an “elemental composition calculator” process to confirm (or not) peak identity. Using such a strategy, 18 peaks were detected with a total number of 63 candidates. At this point, the specificity of GC-​APCI QToFMS

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was exploited by extracting accurate-​mass chromatograms, with a ± 0.01 Da window, for the candidate MIs and protonated MIs. Moreover, an MSMS process which provided low energy (LE) and high energy (HE) CID during the same analysis was used to investigate the MIs/​protonated MIs and the fragment ions. In such a manner, the number of potential candidates was reduced to 36; even so, an average of two candidates per peak remained, at least in part also due to isomerism. Finally, 21 pure standard compounds were subjected to GC-​APCI QToFMS, to observe their retention, ionization and fragmentation behavior, leading to the positive identification of only eight compounds. For example, among the four candidate isomers 2,4-​, 3,5-, 2,5-​, 2,6-​di-​tert-​butyl-​phenol (C14H22O), the presence of the 2,4-​isomer was confirmed. The product ion spectra for 2,4-​di-​tert-​butyl-​phenol (2,4-​ DTB), attained under LE and HE conditions, in the isooctane sample and as a pure standard compound, are shown in Figure 4.10. The presence of the MI at m/​z 206.1667 in the sample LE spectrum (mass error = 0.0002 Da) is well evident. The authors found 2,4-​DBT in all of the samples analyzed, and confirmed it as being a NIAS derived from the degradation of the antioxidant Irgafos 168. In the study described by Cherta et al., the difficulties of mass spectrometry, even of highly specific forms, when encountering isomers became apparent. In such instances, observation of the GC retention behavior, and availability of LRI data, are absolutely necessary. Zhang et al. used GC-​EI QToFMS and a multistep identification process for the screening of 165 phytosanitary compounds in apple, spinach and scallion.51 Initially, the deconvoluted accurate-​mass spectra were subjected to an MS database search, with the support of LRI information. The presence of a suspected contaminant was then confirmed (or not) by: (1) evaluating the mass accuracy and intensity ratios of two representative ions in the mass spectrum; and (2) performing a product ion scan analysis. Using such an approach, the authors reported that, at concentrations ≤ 10 ng mL–​1, the presence of at least 88% of the contaminants could be confirmed. On the other hand, the presence of at least 98% of the contaminants could be confirmed at the ≤ 100 ng mL–​1 concentration. Such an analytical performance should obviously be evaluated in relation to specific MRL values. As mentioned before, the Orbitrap mass spectrometer is characterized by very high resolution, with its first GC-​hyphenation system recently described.21 It is noteworthy that the Orbitrap analyzer is preceded by a single quadrupole one, and so is technically a hybrid device. The linear quadrupole directed the ions to a curved quadrupole ion trap, defined as the C-​trap, where these were stored briefly before sending them in packets to the Orbitrap mass analyser section. The Q-​Orbitrap mass spectrometer was also equipped with a collision cell, for MSMS analysis if required. Mol et al. evaluated the Q-​Orbitrap MS instrument in applications involving the GC-​based analysis of 54 pesticides in leek, tomato and orange, extracted by using QuEChERS.52 The Q analyzer was used in the scan mode, across the

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Figure 4.10 Product ion spectra for 2,4-​di-​tert-​butyl-​phenol, attained under LE and HE conditions, in the isooctane sample and as a pure standard compound. Reproduced from Ref. 49 with permission from Elsevier, Copyright 2015.

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Figure 4.11 EI Orbitrap (above) and NIST spectra relative to pirimicarb. Reproduced from Ref. 52 with permission from Elsevier, Copyright 2016.

mass range m/​z 50–​500, and acted as a wide-​pass mass filter. Mass spectral consistency and mass accuracy (at a resolution of 60 000 fwhm) were evaluated by analyzing different on-​column amounts (0.1–​250 000 pg) of HCB. Spectra were consistent across the entire HCB amount range because of the capability of the C-​trap to prevent overloading, while mass accuracies were always better than 1 ppm. The detector responses were linear across five orders of magnitude, up to an on-​column amount of 25 000 pg. It is noteworthy that detector saturation, leading to distorted spectra, poor mass accuracy and non-​linear response, occurs at much lower on-​column quantities when using HR ToFMS.53

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Figure 4.12 Mass spectral zoom-​ins for chlorpropham at four different mass resolution values. Reproduced from Ref. 52 with permission from Elsevier, Copyright 2016.

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The similarity between EI Q-​Orbitrap MS and NIST spectra was investigated by injecting standard solutions and performing a database search. It was found that the HR and LR spectra were similar, albeit with some differences. For instance, low ion abundancy was observed below m/​z 90, as is evident for the case of pirimicarb shown in Figure 4.11. Such a discrimination was explained by the reduced trapping efficiency of the C-​trap toward lower-​mass  ions. The Q-​Orbitrap MS instrument was capable of operation using mass resolution conditions of 15 000, 30 000, 60 000 and 120 000 (fwhm at m/​z 200). A characteristic of such instrumentation is that the resolving power is inversely proportional to the square root of the m/​z value, leading to lower resolution at higher masses. A sample of leek was spiked at the 10-​ppb level with chlorpropham and subjected to analysis under the four resolution operational modes. Under the highest resolution conditions, an ion characteristic of chlorpropham (C6H6ClN+) was present at an m/​z value of 127.01846, and was well-​separated from another with a mass difference of just under 0.003 Da (Figure 4.12). The two ions remained resolved using a mass resolution of 60 000. On the other hand, at the two lower resolving powers the two ions formed a single peak, with a mass error higher than 20 ppm. In general, it was found that mass accuracies were always better than 5 ppm when using the two higher-​resolution  modes. It is noteworthy that the time required to produce an Orbitrap full spectrum increased with an increase in resolving power, reaching a maximum value of 250 ms (4 spectra s–​1). Consequently, even when using the highest resolution mode, the system was capable of providing a sufficient number of spectra (10) for peaks as narrow as 2.5 s at the base. In general, the GC-​Q-​Orbitrap MS instrument proved its suitability for the determination of 54 pesticides, at a concentration level down to 10 ppb, in the vegetable products subjected to attention. The authors affirmed at the end of the paper that the instrumental approach “provides an alternative to GC-​triple quadrupole MS, with the advantage that the measurement is more straightforward, and that besides quantitative determination an additional screening can be performed for other analytes.” Although such a statement could be true, the economical requirement for the purchase of a GC-​Q-​Orbitrap MS device should also be considered.

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30. L. Hejazi, D. Ebrahimi, M. Guilhaus and D. B. Hibbert, Anal. Chem., 2009, 81, 1450. 31. M. Anbar and W. H. Aberth, Anal. Chem., 1974, 46, 59A-​64A. 32. S. Salivo, M. Beccaria, G. Sullini, P. Q. Tranchida, P. Dugo and L. Mondello, J. Sep. Sci., 2015, 38, 267. 33. H.-​D. Belitz, W. Grosch and P. Schieberle, Food Chemistry, Springer, Berlin, 2004, pp. 157–​244. 34. E. P. de Oliveira, R. M. Manda, G. A. Torezan, J. E. Corrente and R. C. Burini, Cholesterol, 2011, 2011, 851750. 35. A. J. Brown and W. Jessup, Mol. Aspects Med., 2009, 30, 111. 36. M. G. Pintado-​Herrera, E. González-​Mazo and P. A. Lara-​Martín, Anal. Chim. Acta, 2014, 851, 1. 37. F. Hernández, T. Portolés, E. Pitarch and F. J. López, Anal. Chem., 2007, 79, 9494. 38. Commission Regulation (EU) No. 589/​2014 of 2 June 2014 laying down methods of sampling and analysis for the control of levels of dioxins, dioxin-​like PCBs and non-​dioxin-​like PCBs in certain foodstuffs and repealing Regulation (EU) No 252/​2012, OJEU, L164/​18–​40. 39. Commission Regulation (EU) No. 709/​2014 of 20 June 2014 amending Regulation (EC) No 152/​2009 as regards the determination of the levels of dioxins and polychlorinated biphenyls, OJEU, L188/​1–​18. 40. A. García-​Bermejo, M. Ábalos, J. Sauló, E. Abad, M. J. González and B. Gómara, Anal. Chim. Acta, 2015, 889, 156. 41. J. Cristale, J. Quintana, R. Chaler, F. Ventura and S. Lacorte, J. Chromatogr. A, 2012, 1241, 1. 42. S. A. Mackintosh, A. Pérez-​Fuentetaja, L. R. Zimmermann, G. Pacepavicius, M. Clapsadl, M. Alaee and D. S. Aga, Anal. Chim. Acta, 2012, 747, 67. 43. B. Gómara, L. Herrero, L. R. Bordajandi and M. J. Gonzáles, Rapid Commun. Mass Spectrom., 2006, 20, 69. 44. Commission Recommendation of 3 March 2014 on the monitoring of traces of brominated flame retardants in food, OJEU, L65/​39–​40. 45. T. Portolés, C. Sales, B. Gómara, J. V. Sancho, J. Beltrán, L. Herrero, M. J. Gonzáles and F. Hernández, Anal. Chem., 2015, 87, 9892. 46. M. Concejero, L. Ramos, B. Jiménez, B. Gómara, E. Abad, J. Rivera and M. J. González, J. Chromatogr. A, 2001, 917, 227. 47. J. L. Martínez Vidal, F. J. Arrebola Liébanas, M. J. González Rodríguez, A. Garrido Frenich and J. L. Fernández Moreno, Rapid Commun. Mass Spectrom., 2006, 20, 365. 48. A. B. Fialkov, U. Steiner, L. Jones and A. Amirav, Int. J. Mass Spectrom., 2006, 251, 47. 49. L. Cherta, T. Portolés, E. Pitarch, J. Beltran, F. J. López, C. Calatayud, B. Company and F. Hernández, Food Chem., 2015, 188, 301. 50. Commission Regulation (EU) No. 10/​2011 of 14 January 2011 on plastic materials and articles intended to come into contact with food, OJEU, L12/​1–​89.

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51. F. Zhang, H. Wang, L. Zhang, J. Zhang, R. Fan, C. Yu, W. Wang and Y. Guo, Talanta, 2014, 128, 156. 52. H. G. J. Mol, M. Tienstra and P. Zomer, Anal. Chim. Acta, 2016, 935, 161. 53. N. Belmonte Valles, S. Uclés, N. Besil, M. Mezcua and A. R. Fernández-​ Alba, Anal. Methods, 2015, 7, 2162. 54. S.-​T. Chin, Y. Nolvachai and P. J. Marriott, Chirality, 2014, 26, 747. 55. P. Q. Tranchida, Gas Chromatography | Multidimensional in Encyclopedia of Analytical Science, ed. P. Worsfold, A. Townshend, C. Poole and M. Miró, Elsevier, 3rd edn, 2019, pp. 202–​216.

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CHAPTER 5

High-​speed Gas Chromatography: Basic Theory, General Principles, Practical Aspects and Food Analysis PETER Q. TRANCHIDAa* AND LUIGI MONDELLOa,b,c a

Dipartimento di Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali, University of Messina, Polo Annunziata, 98168 Messina, Italy;  bChromaleont s.r.l., c/​ o Dipartimento di Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali, University of Messina, Polo Annunziata, 98168 Messina, Italy;  cUnit of Food Science and Nutrition, Department of Medicine, University Campus Bio-​Medico of Rome, 00128 Rome, Italy *E​mail:  [email protected]

5.1 Introduction James and Martin published the first scientific paper on gas chromatography in 1952:1 volatile fatty acids were separated within a packed glass column, on a liquid stationary phase. The chromatograms illustrated were characterized by retention times ranging from 30 to 500 min. Evolution occurred rapidly: in 1958, Golay demonstrated that open-​tubular (OT) capillary columns, with a liquid-​phase coating, were characterized by a much-​higher resolving power with respect to packed columns.2 Such an increased performance was related

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to the higher column permeability, and hence to the possibility to use much longer columns. After efficiency, the need for faster GC separations started to be addressed at the beginning of the 1960s.3,4 To date, and even though the surrounding world generally works and consumes at high velocity, GC analyses are usually characterized by time requirements between 30 and 90 min. Present-​day GC applications are most often performed by using 30 m × 0.25 mm ID × 0.25 μm df columns, with a theoretical plate number (N) of about 120 000, under ideal operational conditions. Furthermore, under “normal” separation conditions (e.g., temperature program: 50–​300°C at 3°C min–​1; He average linear velocity: 30 cm s–1) a peak capacity (nc –​the number of peaks that can be potentially situated side-​by-​ side in a one-​dimensional GC separation space) in the range 400–​600 can be expected. So, in the case of a 50-​min analysis time (the void time is not considered) and a 500 nc value, a total of 10 peaks min–​1 can be separated at the baseline, in theory. With such a peak capacity value, one would expect to separate, with ease, a sample formed of 100 compounds. However, peaks elute in a disorderly manner, and so there must be a great excess of peak capacity, compared to the number of sample analytes, if a 100% level of separation is desired. Giddings discussed such a factor in theoretical terms, reaching the conclusion that 98% of a sample can be resolved if the peak capacity exceeds its number of constituents by a factor of 100.5 As a consequence, a hypothetical column with a 10 000 nc value would enable the separation of 98 of 100 compounds. It is clear that even though such observations must only be considered as indications, they do give a good idea on the separation power of a single GC column. Bearing such concepts in mind –​time and peak capacity –​these must be related to one of the main objectives in any GC-​based experiment, namely to reach the analytical scope in the minimum time. With analytical scope one can intend the clear separation of the analytes of interest, from interfering compounds. Obviously, mass spectrometry (MS) can tackle non-​sufficient chromatography resolution (e.g., by using selected-​ion monitoring, extracted-​ion chromatograms, tandem MS, deconvolution, etc.), and can greatly contribute to the reduction of analysis times. However, and for the sake of simplicity, the GC step is mainly considered herein. As for conventional GC methods, high-​speed GC separations must guarantee a sufficient level of separation power. Several approaches have been developed and applied to achieve faster separations, among others the use of columns with a reduced ID and stationary-​phase thickness (micro-​bore columns),6 multicapillary columns,7 packed capillary columns,7 turbulent flow,8 vacuum-​outlet conditions,9 rapid (resistive) column heating,10 and short capillary columns.11 Some high-​speed approaches have been exploited much more than others; focus herein is directed to the more commonly used methodologies, namely micro-​bore (IDs ≤ 0.1 mm) capillary columns, vacuum-​ outlet conditions, rapid (resistive) column heating (> 100°C min–​1), and short capillary columns (with conventional GC ovens). Obviously, it is possible to combine two high-​speed approaches in a single approach, such as the rapid

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heating of a micro-​bore column (see Section 5.2.3). High-​speed GC methods should be accompanied by both rapid sample preparation and data processing procedures. Otherwise, the benefits of a reduced chromatography time will be inevitably reduced, or even nullified.

5.2 Basic Theory, General Principles, Practical Aspects and Food Analysis For the sake of classification, it is convenient to group high-​speed GC methods on the basis of the analysis time. In an arbitrary manner, fast and very-​fast GC applications can be considered as those with a duration between 3–​15 and 1–​3 min, respectively; gas chromatography experiments with durations between 1 and 60 s and < 1 s can be considered as hyper-​fast and ultra-​fast techniques, respectively. Gas chromatography separations with a duration of ≤ 60 s are rarely seen, are of low practical utility and are usually published in proof-​of-​ principle studies.12 With regard to capillary columns, those with IDs ≤ 0.1, ≤ 0.32 (and > 0.1) and > 0.32 mm are herein defined as micro-​bore, narrow-​ bore, and mega-​bore, respectively. Any GC method applied to the analysis of food volatiles can be used for one (or more) of the following scopes: general qualitative profiling (untargeted analysis), pre-​targeted and post-​targeted analysis. The latter refers to the investigation of a data file, at a later stage with respect to the date of analysis, to search for compounds not previously subjected to attention (e.g., a specific pesticide). In general, foods are analyzed to elucidate their composition, and to determine the presence, or not, of contaminants. Food composition refers to natively occuring constituents (e.g., fatty acids in a vegetable oil), or to those derived from transformation processes which may occur naturally (e.g., light-​ induced fatty acid oxidation), or industrially (e.g., the Maillard process). Food contaminants are often present at trace amount levels, derive from external sources, being anthropogenic or much more rarely natural. For example, the presence in foods of pesticide residues, dioxins, mineral oils, etc. all relate to a form of human activity, while the natural contamination of vegetables can occur if these are produced near an active volcano. The acquisition of in-​ depth knowledge on the composition of a food can be exploited to confirm (or not) genuineness, safety, or a geographical indication and traditional speciality (e.g., protected designation of origin –​PDO), as well as to highlight the presence of molecules with a possible beneficial effect on human health (e.g., omega-​3 polyunsaturated fatty acids, phytosterols, etc.). The determination of contaminants in food is always carried out to confirm (or not) safety.

5.2.1 Micro-​bore Columns The use of micro-​bore columns is the most popular high-​speed GC approach, because such tools can enable the full reproduction of conventional GC

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profiles in a much shorter time period. To date, the micro-​bore column of dimensions 10 m × 0.10 mm ID × 0.10 μm df has been, by far, the most popular choice in the fast GC field. If one observes the Golay equation,2 modified for GC experiments with a high pressure drop [eqn (5.1)],13 then it can be readily seen that the resistance to mass transfer in the mobile phase factor [CM,o –​the second additive factor in eqn (5.1), without considering the gas velocity at the column outlet (uo) and the pressure correction factor f1] increases with the squared value of the column ID (or dc). So, a reduction in column ID will lead to a significant reduction in band broadening (H –​plate height). Moreover, for columns with a high phase ratio (β), the contribution of the stationary phase [resistance to mass transfer in the stationary phase factor –​the third additive factor in eqn (5.1)] on the H value is low and can be neglected. The β value of a 10 m × 0.10 mm ID × 0.10 μm df column equals 249.5, and is considered high.



 2 DM ,o 1 + 6k + 11k 2 dc2  d 2f 2k H= + uo  f1 + uo f2 (5.1) 2 2 96 (1 + k ) DM ,o  3 (1 + k ) DS  uo

By neglecting the contribution of the stationary phase on band broadening [CS –​the third additive factor in eqn (5.1), without considering uo and the pressure correction factor f2], and then by differentiating the Golay equation with respect to the gas velocity, and setting the result equal to zero, a greatly simplified equation can be attained [eqn (5.2)],14 enabling the direct calculation of the lowest y-​axis value of the van Deemter curve, namely the minimum theoretical plate height (Hmin). For example, for an analyte with a retention factor (k) value of 5, and separated on a 10 m × 0.10 mm ID × 0.10 μm df column, Hmin will be equal to 0.095 mm, while at a k value of 10, Hmin will increase to 0.122 mm. In short, and as a rule-​of-​the-​thumb in GC, for capillary columns characterized by a high β value and substiantial pressure drop, Hmin can considered as equivalent to the column ID, with good approximation.



H min =

9 1 + 6k + 11k 2 dc (5.2) 2 16 3 (1 + k )

An additional simplified function can be attained by again neglecting the contribution of the stationary phase on band broadening [in eqn (5.1)], and this time by differentiating with respect to the plate height, and setting the result equal to zero [eqn (5.3)]:



uo,opt = 8

DM , o dc

3 (1 + k ) (5.3) 1 + 6k + 11k 2 2

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It can be readily seen in eqn (5.3) that the optimum gas velocity at the column outlet (uo,opt) increases proportionally with an increase in the diffusion coefficient (DM,o), and with a reduction of the column ID. So, micro-​bore columns are characterized by a higher optimum linear velocity compared to narrow-​ bore ones, while the use of H2 (a gas which contributes to a high DM,o value) is preferable in high-​speed GC experiments. The experimental van Deemter curves for a C14 alkane, analyzed on a 10 m × 0.10 mm ID × 0.10 μm df low-​polarity column (130°C), using H2 and He as carrier gases, are shown in Figure 5.1. As can be observed, the Hmin values using both types of carrier gas are similar, even though optimum conditions were reached at a higher gas velocity (approximately 60 cm s–​1) using hydrogen. Furthermore, the H2 van Deemter curve rises more gradually at higher-​than-​ideal gas velocities, which can be considered as an additional advantage. Considering the basic theory reported up to now, it can be concluded that the efficiencies at Hmin of a 10 m × 0.10 mm ID × 0.10 μm df column (Hmin ≈ 0.10 mm) and a 25 m × 0.25 mm ID × 0.25 μm df one (Hmin ≈ 0.25 mm), in terms of plate number, are equivalent (100 000 N). For such a reason, it is possible to fully translate a conventional GC method to a fast GC one, under properly optimized operational conditions. Within such a context, an important parameter to consider is the column hold-​up time: a 25-​m narrow-​bore column with an average gas velocity of 30 cm s–​1, will be characterized by a hold-​up time of about 83 s; on the other hand, a 10-​m micro-​bore column with an average gas velocity of 50 cm s–​1 will be characterized by a hold-​up time of 20 s. If such gas velocity conditions are used for the separation of an analyte, at a specific temperature, then elution will occur on the micro-​bore column approximately four

Figure 5.1 Experimental van Deemter curves, relative to a 10 m × 0.10 mm ID × 0.10 μm df column, using H2 (black curve) and He as carrier gases (C14 alkane/​ 130°C).

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times faster. In the much more common case of a temperature program, the oven heating rate must be related to the hold-​up time: for instance, a narrow-​ bore column (with a hold-​up time again of 83 s) heated at 5°C min–​1, across a 50–​250°C temperature range, will lead to an analysis time of 40 min. The translation of such a method to a micro-​bore one (with a hold-​up time again of 20 s) will result in a heating rate of approximately 20°C min–​1 across the same temperature range, leading to an analysis time of 10 min (four times faster). It has been postulated that the optimum heating rate, considering the relationship between speed and resolution, is 10°C per hold-​up time;15 hold-​up times of 83 and 20 s would lead to heating rates of 7.2 and 30°C min–​1, respectively. Klee and Blumberg have treated in detail the concept of method translation in high-​speed GC analysis. Gas chromatography parameters were classified into two groups, namely translatable and non-​translatable ones. Column length, carrier gas type, gas flow, the column heating gradient and the duration of isothermal periods are all translatable; on the other hand, the initial analysis temperature, the isothermal temperature, the stationary phase and the phase ratio are non-​translatable. Translated methods are characterized by the same order of peak elution; furthermore, the retention-​time ratio of the same peaks in the conventional and high-​speed GC chromatograms, respectively, should be equal (or very similar) to the ratio of the hold-​up times. The latter is defined as G, and represents the speed gain.15 As mentioned, band broadening on micro-​bore columns is greatly reduced, leading to the generation of peaks that are narrow, both in space and time. Although a variety of opinions exists, it is generally accepted that 10 data points per peak are required for reliable peak re-​construction, and hence quantification.16 As a consequence, detection systems must be characterized by rapid rise times, high acquisition frequencies, and no or limited sources of extracolumn band broadening. Specific forms of MS, such as single quadrupole mass spectrometry (QMS), ion trap mass spectrometry (ITMS), etc. may be unsuitable for some forms of high-​speed GC analysis. Such a factor will also depend on the specific model used (there has been great evolution in the MS field over the last 10 years) and on the widths of the peaks at the column outlet. For example, a GC peak with a 6-​σ width of 200 ms will require an acquisition frequency of at least 50 Hz for the scope of reliable quantification. The most immediate choice in such challenging analytical conditions has been low-​resolution time-​of-​flight mass spectrometry (LR ToFMS).12 In fact, LR ToFMS instrumentation is characterized by very-​high spectral production frequencies (e.g., ≥ 500 Hz), consistent spectral profiles (absence of skewing) and deconvolution capabilities. A further potential source of extracolumn band broadening, apart from the detection system, is represented by sample introduction. Split/​splitless injectors are commonly used in micro-​bore column applications, typically operated at high split ratios (e.g., ≥ 100 : 1). There are further options to create a narrow analyte band at the head of the analyte column, such as the use of switching valves or cold traps.17,18 One of the main disadvantages directly related to the reduction of the column ID and/​or stationary-​phase film is a decrease of the sample capacity. The

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latter can be defined as the maximum amount of an analyte that can be tolerated by a column before peak shapes become distorted and efficiency is compromised. For example, Mondello et al. evaluated the sample capacity of a 10 m × 0.10 mm ID × 0.10 μm df and a 30 m × 0.25 mm ID × 0.25 μm df column [poly(ethylene glycol) phase], finding that efficiency was greatly reduced using on-​column analyte (palmitic acid methyl ester) quantities higher than 1 and 50 ng, respectively.19 As a consequence, great attention must be directed to sample quantities to avoid column overloading in micro-​bore column applications. Such a necessity will lead to an inevitable reduction in method sensitivity, compared to the use of wider-​bore columns; however, such a loss is usually limited because high-​speed micro-​bore column GC experiments generate narrower peaks. The benefits of using a micro-​bore column, for fast headspace solid-​phase microextraction (HS SPME)–​GC-​flame ionization detector (FID) analysis, were highlighted by Tranchida et al. in an untargeted application.20 Specifically, a PDMS [poly(dimethylsiloxane)] SPME fiber, with a reduced thickness/​volume of absorbing phase (7 μm/​0.043  mm3), was used for the extraction of volatiles from the headspace of a sample of cold-​pressed bergamot essential oil. The latter is a highly valued type of essential oil attained from the rind of the fruit (Citrus bergamia), used in perfumery, and as a flavoring (e.g., in tea), among others.21 The volatile constituents (mainly monoterpene and sesquiterpene hydrocarbons, and oxygenated derivatives) form 93–​96% of the entire essential oil, and are responsible for its delicate aroma. In GC terms, bergamot oil can be considered as a sample of medium complexity, inasmuch that approximately 100 volatile compounds have been reported in the literature (considering one-​dimensional GC studies).22 Tranchida et al. performed the SPME process rapidly, in 15 min (30°C). Compounds with higher volatility (e.g., monoterpene hydrocarbons) reached fiber equilibrium in 2–​5 min, while the same occurred for the less-​volatile constituents (e.g., sesquiterpene hydrocarbons) in 10–​15 min. The fast GC separation was performed on a low-​polarity 10 m × 0.10 mm ID × 0.10 μm df column, using H2 as carrier gas at a constant gas linear velocity of 55 cm s–​1 (dead time: ≈ 18 s). The temperature program –​ 40°C (1 min) to 250°C at 12°C min–​1, then to 280°C at 70°C min–1 –​was characterized by a duration of 19 min, even though the last analyte of interest eluted well with 10 min (corresponding to a temperature of approximately 150°C). It is noteworthy that a CO2 cryogenic trap was installed at the head of the micro-​ bore column, and activated for 1 min at the beginning of the analysis, to focus the more volatile compounds. Without such a technological solution, excessive band broadening was observed in the initial part of the chromatogram. For convenience, considering a total GC separation time of 10 min (fast GC method), which combined with the HS SPME process, led to a total analysis time of 25 min (Figure 5.2). The same PDMS fiber and extraction conditions were used in an HS SPME-​ GC-​FID application, using a narrow-​bore low-​polarity 25 m × 0.25 mm ID × 0.25 μm df column. Even though splitless conditions were applied (as for the fast GC analysis), the general peak intensity was much lower than that observed

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uV(x10,000) Chromatogram 2.75 2.50 2.25 1

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Figure 5.2 Chromatograms showing the fast (upper trace) and conventional HS SPME GC-​FID separation of bergamot essential oil (refer to the source for the identity of the numbered compounds). Reproduced from Ref. 20 with permission from Elsevier, Copyright 2006.

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in Figure 5.2. As a consequence, a PDMS SPME fiber with a higher thickness/​ volume of absorbing phase (30 μm/​0.18  mm3) was used. As expected, the time required for satisfactory analyte extraction was longer, reaching 50 min. Hydrogen was again used as mobile phase, at a constant gas linear velocity of 40 cm s–​1. The dead time was 62.5 s, namely about 3.5 times longer than that calculated for the fast GC application. The temperature program –​40°C to 250°C at 3°C min–​1, then to 280°C at 15°C min–​1 –​was characterized by a duration of 72 min, even though the last analyte of interest eluted well with 40 min (corresponding to a temperature of 160°C). The use of the cryogenic trap was found to be unnecessary, because the combination of the initial analysis temperature and the thicker column stationary phase enabled effective general focusing. Again, for convenience, let us consider a total GC separation time of 40 min (conventional GC method), which combined with the HS SPME process led to a total analysis time of 90 min (Figure 5.2). As can be observed in Figure 5.2, the two chromatograms are characterized by a similar resolution, even though slightly better in the conventional GC method. The probable reason for such an occurrence can be related to the different ratios (conventional GC : fast GC) of the dead times (≈ 3.5) and the separation times (≈ 4.4). The latter is herein calculated considering specific GC separation times of 36 and 8.2 min (elution time of the last compound of interest) in the conventional and fast (the 1-​min focusing time is excluded) GC methods, respectively. The use of a slightly slower temperature increase (≈ 10°C s–​1) in the fast GC application would have enabled equal dead time and separation time ratios (G), and most probably equivalent general analyte resolution. In 2002, Sandra and David reported on the development of a (pre-​targeted) fast method for the determination of polychlorinated biphenyls (PCBs) in food (chicken and pork fat, eggs) and feed samples.23 Such a method proved to be of great usefulness during the Belgium dioxin crisis, because it enabled a greatly enhanced sample throughput compared to the official procedure. A brief description of this severe case of food contamination follows. In January 1999, farmers observed a great increase of nervous disorders and death among chicks. Soon after, samples of chicken fat and animal feed were subjected to analysis and were found to contain high levels (low ppb levels) of polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Such contaminants, along with dioxin-​like PCBs, can be referred to with the generic term “dioxins”. The consumption of foods with a high quantity of fat is considered a major exposure route.24 Sandra and David postulated that PCDDs and PCDFs could not contaminate food at such levels without the presence of PCBs at much higher concentrations. Very soon, the authors quantified PCBs at ppm levels, in feed, chicken fat and eggs, and concluded that used PCB transformer oil had been added to the feed. Finding the cause, at that point the development of a high-​throughput method became necessary: the authors directed their attention to both sample preparation and GC separation, with the first being performed using ultrasonic extraction with petroleum ether, followed by matrix solid-​phase dispersion. Fast GC was

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carried out by using a low-​polarity 10 m × 0.10 mm ID × 0.10 μm df column, using H2 as carrier gas at a gas linear velocity of 67.2 cm s–​1, and an electron-​ capture detector. For positive samples, further confirmation using mass spectrometry was performed. A conventional GC method was fully translated using dedicated software, with a predicted speed-​gain factor of 4.4, being in good agreement with the experimentally observed value of 4.5. As mentioned, the use of a long sample preparation procedure, combined with a fast GC separation, makes little sense; the foundations of such a concept are even stronger in the case of very-​fast GC separations. In such a respect, Mondello et al. performed a rapid derivatization process on a sample of cod liver oil, using μL volumes of reagent and solvents.25 Cod liver oil is formed, nearly completely, of triacylglycerols (TAGs); the latter, on their behalf, are esters derived from the combination of glycerol and three fatty acids (FA). The fatty acid composition of cod liver oil is one of the most complex known, with high levels of ω-​3 polyunsaturated fatty acids such as eicosapentaenoic (EPA) and docosaesaenoic acid (DHA), and with the resulting number of potential TAGs outstandingly high. It is possible to analyze intact TAGs by using GC columns of high thermal stability [e.g., poly(50%dimethyl50%diphenylsiloxane)]; however, it is much more common to analyze the FAs, following a derivatization process, mainly as methyl esters (FAMEs). Mondello et al. performed the methylation process in about 35 min: 20 μL of cod oil were subjected to transesterification by using 200 μL of boron trifluoride–​methanol (20% BF3), and heating at 100°C for 30 min. After cooling, 800 μL of distilled water and 200 μL of hexane were added to the mixture, which was then subjected to manual agitation (1 min) and to centrifugation for 2 min. Finally, about 100 μL of the hexane layer were transferred to a vial. The FAMEs were separated on a poly(ethylene glycol) 10 m × 0.10 mm ID × 0.10 μm df column. The temperature program –​180°C to 270°C(0.5 min) at 40°C min–​1 –​had a duration of 135 s (excluding the isothermal period), while H2 was used as mobile phase, at a constant linear velocity of 100 cm s–​1 (dead-​time:  ≈ 10 s) and an FID was used for detection. The very-​ fast GC-​FID chromatogram of cod liver oil FAMEs is shown in Figure 5.3 (upper trace), with peaks 29 and 33 corresponding to EPA and DHA, respectively. It was affirmed that approximately 45 min were required to analyze six samples (7.5 min per sample), considering a simultaneous process of derivatization and GC separation of samples previously subjected to preparation. With respect to a conventional GC-​FID analysis on the same sample, carried out by using a 30 m × 0.25 mm ID × 0.25 μm df column, there was a loss in resolution, as can be seen in the chromatogram illustrated in Figure 5.3. For example, peaks 13 (C18:1ω9) and 14 (C18:1ω7), and peaks 22 (C20:1ω9) and 23 (C20:1ω7), passed from baseline resolution to near-​complete co-​elution. The conventional application was performed by using H2 at a constant linear velocity of 35 cm s–​1 (dead time: ≈ 86 s), and a temperature program –​180°C to 270°C (5 min) at 3°C min–​1 –​with a duration of 30 min (excluding the isothermal period). Considering the dead times, the temperature gradient should have been 26°C min–​1 in the very-​fast GC analysis, to more-​or-​less reproduce

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Figure 5.3 Very fast and conventional GC-​FID chromatograms of a cod liver oil (refer to the source for the identity of the numbered compounds). Reproduced from Ref. 25 with permission from Elsevier, Copyright 2006.

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the conventional GC profile [under optimized conditions the conventional GC column was, in any case, more efficient (≈ 120 000 vs. 100 000 N)]. Very steep temperature gradients lead to an excessive reduction of k values, and thus to a non-​sufficient interaction with the column stationary phase (especially in the end part of the column). Under such conditions, the inevitable price to pay is a loss in resolution. Mass spectrometry is the most important form of detection in the GC field, and must now be included in the discussion. In truth, the mass spectrometer fits extremely tightly within the definition “detector” as it can be used as a stand-​ alone analytical technique. Gas chromatography combined with mass spectrometry (GC-​MS) forms a very powerful two-​dimensional analytical technique; ideally, both the GC and MS steps should be pushed to their full potential. There are several types of MS devices, the technical characteristics of which must be considered in relation to the high-​speed GC method of choice (apart from the specific scope of the analysis). As already mentioned, LR ToFMS is the most immediate choice for its intrinsic features; however, other MS devices (e.g., single quadrupole, triple quadrupole, high-​resolution ToF) can perform in a satisfactory manner in high-​speed GC investigations. Triple quadrupole (QqQ) mass spectrometry is the most popular form of multiple-​analyzer device, used in GC-​based investigations. The use of QqQMS is mainly reported in pre-​targeted applications, because it can greatly enhance both specificity and sensitivity. Additionally, and if required, MSMS processes can be very helpful in providing molecular structure information. A high-​speed QqQMS system was evaluated by Tranchida et al. under fast GC conditions.26 The mass spectrometer was capable of scan mode operation, at a scan speed of 20 000 amu s–​1, and multiple reaction monitoring (MRM) with a minimum dwell time of 0.01 s. Furthermore, the MS device could switch back and forth between the two operational modes during the same analysis and in a very rapid manner. An alternating scan/​MRM fast HS SPME-​GC-​ QqQMS method was developed for both the untargeted analysis of brewed tea volatiles and the pre-​targeted determination of 30 phytosanitary contaminants. Tea is one of the most popular beverages in the world, with great areas of land devoted to tea plantations. Tea plants are commonly treated with insecticides, in the field and sometimes during storage. Residues present on tea leaves can potentially end up in tea infusions, and thus represent a hazard especially for those populations which consume high quantities of tea.27 Apart from contaminants, which relate to food safety and must be quantified at potentially low concentration levels, knowledge on the overall composition of the brewed tea headspace is fundamental in relation to its aromatic characteristics. Tranchida et al. performed the fast GC separation by using a low-​polarity 15 m × 0.10 mm ID × 0.10 μm df column, with the last analyte eluting within 15 min, as can be seen in the scan and MRM chromatograms shown in Figure 5.4. A constant average He velocity of 45 cm s–​1 was used, accompanied by a temperature gradient of 18°C min–​1, which was equivalent to 10°C per void time. The MS system was operated at a scan/​MRM spectral production frequency of 10 Hz (loop time: 0.1 s). The HS SPME process was

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Figure 5.4 Fast scan and MRM HS SPME-​GC-​QqQMS traces, relative to the analysis of spiked brewed tea (refer to the source for peak identity). Reproduced from Ref. 26 with permission from John Wiley and Sons, Copyright © 2013 WILEY-​VCH Verlag GmbH & Co. KGaA, Weinheim.

carried out by using a 100 μm PDMS fiber at 60°C, in a very rapid manner: 5 min equilibration + 5 min extraction. It is obvious that, under such conditions, full equilibrium between the sample, headspace and the fiber, was not reached; however, the method limits of quantification (LoQs) satisfied legislation requirements for the investigated contaminants, them ranging between 14 ppt and 16 ppb. With regard to the scan information, 28 compounds were tentatively identified by using a spectral database equipped with linear retention index (LRI) information (experimental and database values were matched). Matches with a similarity value lower than 80%, and/​or outside a ± 15 LRI window, were automatically deleted from the “hit list”. A maximum deviation of 8 LRI units was observed. It is noteworthy that LRI values calculated in fast GC applications are usually in good agreement with those derived from conventional GC analyses; however, deviations can grow considerably in very-​fast GC investigations. A good example illustrating the potential and utility of the alternating operational mode can be observed in Figure 5.5: co-​elution occurred between a pesticide, disulfoton sulfoxide, and a natural tea constituent, undecanal. The former was quantified at the 45 ppb level (near the maximum residue limit of 50 ppb), while the latter was tentatively identified with a 94% spectral similarity, and an experimental vs. database LRI difference of –​2 units. Furthermore, the qualifier/​quantifier ratio equaled 65%, just 3% lower than the average value extrapolated from the analysis of standard solutions.

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Undecanal

2.00 1.75 1.50 1.25 1.00 0.75

Disulfoton sulfoxide

0.50 0.25 0.00 6.575

6.600

6.625

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min

Figure 5.5 Fast scan and MRM HS SPME-​GC-​QqQ MS chromatogram expansions, highlighting the co-​elution between undecanal and disulfoton sulfoxide, relative to the analysis of a contaminated brewed tea. Reproduced from Ref. 26 with permission from John Wiley and Sons, Copyright © 2013 WILEY-​VCH Verlag GmbH & Co. KGaA, Weinheim.

As aforementioned, the use of the 10 m × 0.10 mm ID × 0.10 μm df column is the most well-​established among the possible micro-​bore capillary options. Some of the reasons for such a trend are dependent on the non-​ excessive inlet pressure and split-​flow requirements, and on the now well-​ established industrial capability to deposit a stable and uniform layer of stationary phase with a 0.10 μm thickness. The inlet pressure, split flow, and industrial aspects can become a hindrance when reducing the ID and film thickness below values of 0.10 mm and 0.10 μm, respectively, because: the resistance to flow is inversely proportional to the fourth power of the column radius (Poiseuille’s law); the further reduction in sample capacity will require higher split flows; and there is an increased technological difficulty in the deposition of a uniform layer of stationary phase. On the other hand, the advantages of reducing further the column ID and film thickness are an increase in the separation power [eqn (5.1)]; and higher optimum gas linear velocities [(eqn (5.3)]. Considering column efficiency, the maximum theoretical plate number of a 5 m × 0.05 mm ID × 0.05 μm df column is equivalent to that of a 10 m × 0.10 mm ID × 0.10 μm df  column.

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120 100 80 60 40 20 0

20

40

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80

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Figure 5.6 Hydrogen van Deemter curve for C13 alkane using a 5 m × 0.05 mm ID × 0.05 μm df column (130°C). Reproduced from Ref. 28 with permission from John Wiley and Sons, Copyright © 2004 WILEY-​VCH Verlag GmbH & Co. KGaA, Weinheim.

The use of a low-​polarity 5 m × 0.05 mm ID × 0.05 μm df column has been reported for the very-​fast GC-​FID analysis of lime essential oil.28 An experimental van Deemter curve was constructed by analyzing a C13 alkane (130°C), using H2 as carrier gas. The lowest part of the curve corresponded to a gas velocity of approximately 70 cm s–​1, and a plate height of 0.052 mm (≈ 96 200 N), very close to theoretical dictations. As can be seen in Figure 5.6, the ascending side of the curve rises very gradually, enabling the use of a high gas velocity with a limited loss in resolving power. The lime oil was analyzed by using a three-​step temperature program –​50°C to 150°C at 80°C min–​1, 150°C to 200°C at 70°C min–​1, 200°C to 250°C at 55°C min–​1 –​and a constant inlet pressure of 880 kPa. The initial average gas velocity was 120 cm s–​1, with it decreasing throughout the analysis. The lime essential oil was diluted 1 : 100 (v/​v) in hexane; afterwards, 1 μL of the solution was subjected to GC injection at a split ratio of 750 : 1. The lime oil was separated in just over 90 s, as can be seen in Figure 5.7. The three peaks indicated in the initial, middle and final part of the chromatogram demonstrate the high column efficiency. The Trennzahl number, a parameter that gives an indication of peak capacity, was calculated across the C8–​C30 alkane range, and reached a value of 468. The C30 alkane eluted at a retention time of 192 s.

5.2.2 Vacuum Outlet Conditions Obviously, the most common manner to generate vacuum conditions at the column oulet is by using mass spectrometry. If one observes eqn (5.3), then it can easily be concluded that the value of optimum gas linear velocity

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Figure 5.7 Very-​fast GC-​FID analysis of lime essential oil by using a 5 m × 0.05 mm ID × 0.05 μm df column (refer to the source for the identity of the numbered compounds). Reproduced from Ref. 28 with permission from John Wiley and Sons, Copyright © 2004 WILEY-​VCH Verlag GmbH & Co. KGaA, Weinheim.

increases considerably with an enhancement of the factor DM,o. Such a coefficient, which is described by the equation introduced by Fuller, Schettler, and Giddings,29 undergoes an increase when GC operations are performed under low-​pressure (LP) conditions, and when using a “lighter” gas such as hydrogen as mobile phase. Mass spectrometry is usually (even though not always) performed using vacuum conditions at the column outlet (it being located within the source), while He is preferred over H2, for safety reasons, in the field of GC-​MS. The vacuum outlet conditions are better exploited if mega-​bore columns are used, because LP conditions are generated along great part of the analytical column. Typically, 10 m columns with a 0.53 mm ID are used; however, and in such a case, precautions have to be taken to avoid sub-​ambient pressure conditions reaching the injector. For instance, van Deursen et al. evaluated the use of a 0.6 m × 0.10 mm ID uncoated pre-​column, a supercritical fluid chromatography restriction at the column inlet, and a micro-​injection valve (a 15 m × 0.25 mm ID uncoated column connected the valve to the pressure regulator).9 The authors reported an optimum gas velocity of 200 cm s–​1 and an efficiency of 20 000 N, for a 10 m × 0.53 mm ID × 0.25 μm df column. Such results highlight the main disadvantage of using mega-​bore columns, namely the reduced separation power. For such a reason, the food analyst must bear in mind the analytical scope and the sample complexity before using such separation tools for high-​speed GC analysis. In particular, LP GC-​MS finds more

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use in targeted applications. An additional drawback relates to the generation of high gas flows (typically 5–​10 mL min–​1), which may exceed the pumping capacity of the MS instrumentation used. An important benefit is represented by the high sample capacity (large sample volumes can be used), and in a way, by the enhanced band broadening compared to micro-​bore columns. In fact, the generation of wider peaks (e.g., 0.5–​1 s) will lead to a reduced necessity of very-​high spectral production frequencies. In most instances, the production of 20 spectra s–​1 can be considered sufficient. In the present section, LP GC experiments involving four different forms of MS will be described, namely QMS, high-​and low-​resolution ToFMS, and QqQMS. In a relatively early study (2001), Maštovská et al. reported on the optimization and evalution of LP GC-​QMS in SIM (selected-​ion-​monitoring) applications, involving the presence of pesticides in carrots.30 The authors used a low-​polarity 10 m × 0.53 mm ID × 1 μm df column, preceded by an uncoated 3 m × 0.15 mm ID restrictor. Initially, the effect of gas flow on peak height was studied for the most retained pesticide, namely deltamethrin. The gas inlet pressure was increased from 10 to 60 p.s.i.g., with maximum peak height attained at a pressure of 20 p.s.i.g. The results observed were due to two opposing factors: as the gas velocity increases, peaks become narrower, while MS sensitivity is reduced (mass spectrometers are concentration-​ sensitive detectors). A side-​by-​side comparison was made with a low-​polarity 30 m × 0.25 mm ID × 0.25 μm df GC column: fast operational conditions were generated using a temperature gradient of 60°C min–​1 (run time: 6.8 min) at the optimized constant He pressure and applied to the analysis of a 5 ppm standard solution containing 20 pesticides (injection volume: 1 μL, splitless time: 30 s). The conventional column was heated rapidly, even though not in a linear manner, at a constant flow of 1 mL min–​1. The injection conditions were the same in both analyses. As can be seen in Figure 5.8A,B, resolution is visibily reduced when using the mega-​bore column approach (especially from peak 5 onwards), with this being performed about three times faster compared to the narrow-​bore column. Peak responses, on the other hand, are more-​or-​less similar. It must be noted that a better comparison could have been made if a linear temperature gradient was applied in both cases. Additionally, having to choose between an 18-​min analysis with a satisfactory degree of resolution, and one three times faster with a less-​than-​sufficient separation performance, then the preference would be directed to the former approach. In a later (pre-​targeted) study (2008), Cajka et al. used high-​resolution (HR) ToFMS to create LP conditions within a low-​polarity 10 m × 0.53 mm ID × 0.5 μm df column, linked to an uncoated 3 m × 0.15 mm ID restrictor.31 In general, HR ToFMS is very useful for both untargeted and pre-​targeted analysis. With regard to the former, the experimental mass spectra can be used in conventional MS database searches, while the accurate mass ions can give valuable information on the molecular composition. Pre-​targeted determinations are performed by generating highly specific exact-​mass chromatograms. A further characteristic of HR ToFMS is the possibilty to investigate the stored

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Figure 5.8 Low-​pressure (A) and conventional (B) GC-​QMS analysis of a mixture of standard pesticides. Reproduced from Ref. 30 with permission from Elsevier, Copyright 2001.

full-​spectrum data, at a later stage, to highlight the presence of previously unsearched compounds (post-​targeted analysis). Going back to the research of Cajka et al., the authors focused their attention toward the determination of 100 pesticides in apple baby foods, at concentration levels ≤ 10 μg kg–​1 (EU maximum residue limit). Considering the importance of multiresidue food analysis, with the necessity to perform a high number of analytical controls to

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guarantee food safety, the availability of fast and reliable methods is certainly desirable. Two sample preparation methods, classical ethyl acetate extraction (followed by gel permeation chromatography) and QuEChERS (Quick, Easy, Cheap, Effective. Rapid and Safe), were evaluated. A sample of a volume of 2 μL was delivered to a programmed temperature vaporizor (PTV): a vent time of 15 s, with a flow of 20 mL min–​1, were applied at a temperature of 70°C, to eliminate the solvent; afterwards, the PTV was switched to the splitless mode (1 min), and was rapidly heated to enable the release of the analytes (plus part of the matrix) onto the GC column. The latter was heated linearly from 90°C (1 min) to 280°C (2.83 min) at 60°C min–​1 (7 min run time). Mass resolution was generally more than 7 000 (fwhm), while 4 spectra s–​1 were acquired (the MS system used was capable of generating up to 10 spectra s–​1). One of the strengths of HR ToFMS is related its high specificity in pre-​ targeted analyses. As an example, Cajka et al. reported the exact-​mass chromatograms of phosalone (at the 10 μg kg–​1 level), extracted at an m/​z value of 182.001 (a molecule fragment), with mass windows of 1, 0.1 and 0.02 Da (Figure 5.9A–​C). As can be readily seen, the specifity increases greatly when the applied mass window is reduced. It was found that, for both sample preparation processes and for almost all the analytes, the mean recoveries (between 70% and 120%) and lowest calibration levels (≤ 10 μg kg–​1) were acceptable. Although QuEChERS sample preparation was found to be both faster and cheaper, it was also characterized by the presence of a greater amount of co-​ extracted matrix interferences. The authors affirmed that 18 samples could be subjected to analysis in 4 h by using the QuEChERS approach, followed by LP  GC-​MS. Koesukwiwat et al. (2010) used LP GC-​LR ToFMS for the quantification of 150 pesticides in tomato, strawberry, potato, orange and lettuce.32 A comparison between sample preparation methods was also performed: acetate-​buffered and unbuffered QuEChERS, each with dispersive solid-​phase extraction (d-​ SPE) and disposable pipette extraction (DPX) for clean-​up. The specificity and sensitivity of LR ToFMS is obviously much lower compared to HR ToFMS (and QqQMS). The authors used a low-​polarity 10 m × 0.53 mm ID × 1 μm df column, connected to an uncoated 3 m × 0.15 mm ID restrictor. To gain sensitivity, a large sample volume of 10 μL was injected into a PTV: a vent time of 15 s, with a flow of 50 mL min–​1, were applied at a temperature of 75°C to eliminate the solvent; then, the PTV was switched to the splitless mode (2 min), and was rapidly heated to enable the release of the analytes (plus part of the matrix) onto the GC column. The latter was heated from 90°C (1 min) to 180°C at 80°C min–​1, then to 250°C at 40°C min–​1, and then to 290°C at 70°C min–​1 (4 min), for a total run time of 9.45 min. The MS system generated 10 spectra s–​1, in a mass range of m/​z 70–​600. The authors affirmed that even though the MS production of 50–​100 spectra s–​1 would have enabled a better performance of the deconvolution algorithm (approximately 30 data points/​peak are advisable), the use of a lower acquisition frequency was necessary to guarantee acceptable sensitivity. Quantification was performed for most pesticides (98) at a concentration of 10 μg kg–​1 in all matrices, while for others (43) it was possible

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Figure 5.9 Exact-​mass chromatograms (target ion = m/​z 182.001) of phospalone, at the 10 ppb level in apple baby food extract (prepared by using the QuEChERS approach), generated by using mass windows of (A) 1 Da, (B) 0.1 Da and (C) 0.02 Da. Reproduced from Ref. 31 with permission from Elsevier, Copyright 2008.

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at the 10–​25 μg kg levels, depending on the QuEChERS method and matrix. The quantification of the remaining pesticides, at an acceptable level of sensitivity, was problematic. Even though all the QuEChERS approaches provided acceptable results, the preference of the authors was for the buffered version with d-​SPE. A throughput of approximately 36 samples in 9 h was reported; satisfactory instrumental stability was achieved by changing the liner every 96 samples, along with the removal of the first 5 cm of the restrictor. Shortly after the LP GC-​LR ToFMS investigation, Koesukwiwat et al. used LP GC-​QqQMS with for the quantification of 150 pesticides in broccoli, cantaloupe, lemon and sweet potato.33 The previously optimized buffered QuEChERS approach was used, while the sample volume injected into the PTV was reduced by 50% (5 μL).32 The GC column, and experimental conditions, were altogether similar to those reported in Ref. 32; hence, the main difference consisted in the type of mass spectrometry. Two transitions were monitored for each analyte, with a dwell time of 2.5 ms (interdwell delay: 1 ms). Sixty transitions per time segment were used, leading to a cycle time of 210 ms. Under such MRM conditions, a peak with a 3-​s width would be reconstructed with about 14 data points. No significant differences in peak heights and areas were observed when using dwell times across the range 1–​10 ms. On the other hand, considerable differences in peak heights were observed when using different gas flows. The graph in Figure 5.10 reports peak heights and retention time values of deltamethrin (the most retained analyte) at constant gas flows of 1, 2, 2.5, 3, 4, 5, 7.5 and 10 mL min–​1. As can be seen, the most intense signal was attained at a gas flow of 2 mL min–​1, it being used in all applications. Sensitivity fell to non-​acceptable levels at gas flows ≥ 5 mL min–​1. In general, it was found that compared to the LP GC-​LR ToFMS results,32 LoQs were reduced by 50%, even though half the sample volume was injected.

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–​1

5.2.3 Resistive Column Heating As mentioned, the application of a temperature gradient of 10°C per hold-​up time has been suggested to achieve an optimum compromise between speed and resolution.15 So, if a 30 m × 0.25 mm ID column is operated at an average gas velocity of 30 cm s–​1, then a temperature gradient of ≈ 6°C min–​1 should be used (or 10°C every 100 s). If, on the other hand, a 5 m × 0.25 mm ID column is used at the same average gas velocity, then a temperature gradient of ≈ 36°C min–​1 should be used (or ≈ 10°C every 17 s). Additionally, the required applied pressure to generate a specific flow decreases with column length, as can be derived from the Poiseulle equation [eqn (5.4); Fo(c) = outlet flow], where η is the gas dynamic viscosity at a specific temperature, r and L are the column radius and length, respectively, pi and po are the absolute pressures at the column inlet and outlet, respectively, and Tref and T are the reference (typically 25°C) and oven temperatures, respectively. It is obvious that the reduced inlet pressure requirements of a shorter column will lead to an increase of factor DM,o and, consequently, of the optimum gas linear velocity [eqn (5.3)]. If, for example, the optimum average gas velocity of a 5 m × 0.25 mm ID column

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Figure 5.10 Peak heights/​retention times for deltamethrin at different gas flows. Reproduced from Ref. 33 with permission from Elsevier, Copyright 2001.

corresponds to 100 cm s–​1, then a temperature gradient of 120°C min–​1 should be used, a value that exceeds the limit (approximately 80–​100°C min–​1) of conventional GC (air bath) ovens.34 The restricted heating and cooling capabilities of conventional GC ovens are related to a high thermal mass, and can correspond to limiting steps in specific forms (e.g., hyper-​fast) of high-​speed GC separations.



Fo(c ) =

60πr 4 pi2 − po2 Tref po T (5.4) 16 ηL

Resistive column heating is achieved through the use of heating materials, with these in direct or close contact with the column. Such materials are heated electrically, and can produce extremely steep positive and negative temperature gradients. Furthermore, the non-​use of a conventional GC oven simplifies greatly the construction of field-​portable gas chromatographs.34 The use of a highly accelerated temperature program has only sense with a short GC column, because k values should not reach zero (meaning no interaction of the analyte with the stationary phase) before the analytes reach the detector. Bicchi et al. used different GC columns and analytical conditions to separate a variety of essential oils.35 For example, sage essential oil was analyzed using a 25 m × 0.25 mm ID × 0.3 μm df and a 5 m × 0.1 mm ID × 0.1 μm df column, with a low polarity. The former was operated by using a temperature program of 50°C (1 min)–​250°C at 3°C min–​1, and a constant flow of 1.5 mL min–​1. The micro-​bore column was heated from 50°C (0.1 min) to 250°C at 150°C min–​1, with a constant flow of 0.5 mL min–​1. The resulting GC-​FID chromatograms

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Figure 5.11 Analysis of sage essential oil under conventional (upper chromatogram) and resistively heated GC conditions. Reproduced from Ref. 35 with permission from Elsevier, Copyright 2004.

are illustrated in Figure 5.11. As can be seen, even though the analysis time is drastically reduced in the very fast GC application (peak 14, scareol, elutes approximately 33 times faster), the general decrease in resolution is limited (e.g., see peaks 8–​13 elution range). The resistive-​heating approach used by Bicchi et al. was based on a model introduced by Overton and co-​authors36 and is capable of generating heating ramps of 20°C s–​1. A recent resistive-​heating technology, defined as low thermal mass gas chromatography (LTMGC), is worthy of note.37 Such a methodology is characterized by a power consumption of about 1% with respect to a conventional GC oven. The LTMGC heating/​cooling unit is constructed by binding the column, heating wire and resistive heating detector together with ceramic fibers before coiling. The coil, covered with aluminum foil, is located in a metal tray; the heating gradient can be extremely steep, up to 30°C s–​1 (1 800°C min–​1), while rapid cooling is achieved by a fan positioned on the bottom of the metal tray. As an example, three hyper-​fast separations of C14–​C16 n-​alkanes, carried out on a low-​polarity 2 m × 0.10 mm ID × 0.12 μm df column using heating rates of 1 200, 1 500 and 1 800°C min–​1 (from 50 to 300°C), are reported in

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Figure 5.12 Analysis of C14 (peak 1), C15 (peak 2), C16 (peak 3) n-​alkanes, using LTMGC temperature gradients of 1 200, 1 500, and 1 800°C min–​1. Reproduced from Ref. 37 with permission from Oxford University Press, Copyright 2006.

Figure 5.12. The mobile phase was H2, used at an average linear velocity of 100 cm s–​1. As a consequence, the void time was 2 s, with an optimum heating rate of 300°C min–​1. Considering the analytical conditions, the peaks appear to be generally excessively wide (≈ 600 ms), and curiously wider in the fastest of the applications. Such a factor can be related to the manual injection conditions, certainly unsuitable in such applications. More than the heating capability of LTMGC, the most attractive features of the methodology are the limited dimensions and power consumption, as well as the high-​speed cooling. With regard to the latter advantage, the authors reported that it took only 25 s to cool the 2 m column from 250 to 50°C.

5.2.4 Short Capillary Columns The reason why a shorter column will enable faster GC analysis is obvious, as well as the fact that the resolving power will be reduced. More specifically, general resolution will undergo a 50% decrease if the column length is reduced by a factor of four. Apart from the faster analyses, further advantages of using short column segments are related to the lower elution temperatures of compounds with a high boiling point and/​or limited thermal stability. For

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example, such an approach can be conveniently used in the high-​speed analysis of thermally labile pesticides.11 Bicchi et al. investigated the use of short segments of narrow-​bore and micro-​bore columns, in enantioselective (Es) GC-​QMS applications.38 Prior to the description of the fast Es GC experiments, it is important to highlight the role of such a methodology in food analysis. As an example, essential oils contain a wide variety of chiral compounds, with many of these characterized by specific enantiomer ratios (ERs). The deviation of such ERs, outside an established narrow tolerance range, can be related to adulteration, a specific geographical origin, or production period, depending on the type of essential oil and the enantiomers subjected to investigation. Flavors are also characterized by the presence of chiral constituents, with specific ERs related to the natural or synthetic origin. Dextrorotary and levorotary enantiomers can also possess entirely different olfactometric characteristics.39 The GC separation of enantiomers is required to determine ER values; however, conventional phases do not separate compounds with the same physico-​ chemical properties, also being characterized by identical MS behavior. The resolution of underivatized chiral analytes can be achieved by using shape-​ selective stationary phases. The most commonly used enantioselective phases are derivatized cyclodextrins (CDs). The latter consist of six (α-​), seven (β-​) or eight (γ-​) molecules of glucose, and are spatially shaped in the form of a truncated conic cavity. Derivatization of the hydroxyls, with methyl, trifluoroacetyl, etc. groups, enables the generation of different enantioselectivites.40 Bicchi et al. used short narrow-​bore and micro-​bore CD columns in the analysis of compounds found in food products and fragrances, avoiding the application of accelerated temperature programs, with these having a negative effect on enantiomer separation. For example, 25 and 5 m columns with a 0.25 mm ID, and coated with 30% of 2,3-​di-​O-​ethyl-​6-​O-​tert-​butyldimethylsilyl-​β-​cyclodextrin, diluted in a methylphenyl–​polysiloxane phase (PS-​086), were subjected to investigation. In all applications, the He gas flow was 1 mL min–​1 while the temperature gradient was 2°C min–​1. A mixture of standard compounds (terpenes, lactones, organochlorine compounds) was subjected to analysis on three columns with dimensions: (1) 25 m × 0.25 mm ID × 0.25 μm df; (2) 25 m × 0.25 mm ID × 0.15 μm df; and (3) 5 m × 0.25 mm ID × 0.15 μm df. Three Es GC-​QMS chromatograms, illustrating the separation of α-​hexachlorohexane (α-​HCH) enantiomers, are illustrated in Figure 5.13. In the separation on the first column, the α-​HCH enantiomers eluted at temperatures of 156°C and 158°C, with a resolution value of 5.9. With regard to column II, the α-​HCH enantiomers eluted at temperatures of 140°C and 142°C, with a resolution value of 8.8. As a consequence, it can be concluded that the lower temperatures of interaction with the stationary phase on column II, much more than the phase thickness itself, had a beneficial effect on selectivity. The result attained on column III, in terms of resolution, was more than what could have been expected by following basic theoretical dictations [see eqn (3.5) in Chapter 3). More specifically, if column length is shortened by five times, then one would expect a decrease in

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Figure 5.13 Enantioselective GC-​MS analysis of α-​HCH enantiomers using three different columns. Reproduced from Ref. 38 with permission from Elsevier, Copyright 2008.

resolution by a factor of 2.2 (√5), leading to a resolution of 4 (from 8.8). Instead, a resolution value of 5.3 was observed (about the same as that achieved on column I), with the α-​HCH enantiomers eluting at temperatures of 114 and 115°C. Such an outcome can be related to the fact that only a part of column II (and I) was exploited for separation; once the temperature exceeded a certain value, the guest–​host interactions with the CD phase had no additional effects on resolution. Obviously, such considerations are valid for the CD utilized and the enantiomers subjected to separation. Apart from the enantiomer separation results attained on short CD column segments, a general decrease in separation power will always be observed when passing from a long to a short capillary, using an equivalent gas flow and temperature gradient. In such instances, the support of mass spectrometry with its additional separation capabilities is fundamental. The analysis of TAGs in food products is most often performed using reversed-​ phase or silver-​ ion high-​ performance liquid chromatography, and is considered challenging because of the high number of possible FA combinations along the glycerol backbone.41 The availability of reliable GC methods to analyze such high-​molecular-​weight compounds represents an attractive alternative. In such a respect, and in a GC prediction study, van Deursen et al. used the combination of rapid heating and a short mega-​bore column for fast TAG separation.42 A C22–​C52 series of standard TAGs was injected under cold on-​column conditions onto a 5 m × 0.53 mm ID × 0.17 μm df non-​polar capillary; such injections conditions are often used in the GC analysis of TAGs to avoid mass discrimination.43 The high-​temperature column was rapidly heated (an oven insert was used to increase the heating capability) from 60°C to 200°C at 100°C min–​1, and then from 200°C to 355°C (0.5 min) at 65°C min–​1. Hydrogen was used as carrier gas, while the average linear velocity was 280 cm s–​1. As can be observed from Figure 5.14, the C50 TAG elutes at approximately 3.65 min, corresponding to an elution

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Figure 5.14 Chromatogram expansion relative to a fast GC-​FID separation of standard TAGs (C32–​C50 zone), performed on a short mega-​bore column. Reproduced from Ref. 42 with permission from John Wiley and Sons, Copyright © 2001 John Wiley & Sons, Inc.

temperature of about 350°C. The generated peak capacity was rather limited due to the rather wide peaks; such an event would not represent a great problem because the selectivity of non-​polar stationary phases is low when involved in TAG separations (TAGs with the same C number can co-​elute). On the other hand, such a restricted space for separation would be a considerable disadvantage for stationary phases with a high phenyl content [e.g., poly(50%dimethyl50%diphenylsiloxane)]. The use of 70-​eV (or “hard”) electron ionization (EI) is the most common GC-​ MS ionization method. It is well-​known that such a process generates highly repeatable, fragment-​rich mass spectra, and that MS databases contain mainly (QMS) hard EI spectra. However, the often limited intensity (or absence) of the molecular ion (MI) and the excessive formation of low-​mass fragment ions represent two disadvantages. An optimal ionization technique should, in fact, preserve both the MI information and the fingerprinting capability. For such a reason, several alternative (softer) ionization technologies have been proposed, such as supersonic molecular beam (SMB) EI, also defined “cold EI”.44 The SMB approach is characterized by the EI of vibrationally cooled molecules in a fly-​through source, leading to a reduced degree of fragmentation and to the formation of an evident MI (in particular for analytes composed of 15 or more atoms). Further reported advantages consist of the possibility to operate at high gas flows, in the reduction of source-​induced peak tailing and in the possibility to investigate isotopic patterns. Fialkov et al. used a 4 m × 0.25 mm ID × 0.10 μm df non-​polar column in various proof-​of-​principle

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Figure 5.15 Hyper fast GC-​SMB EI MS results (SIM upper chromatogram; MSMS lower chromatogram). Reproduced from Ref. 45 with permission from Elsevier, Copyright 2006.

GC-​SMB EI MS experiments, one of which involved the hyper fast analysis of diazinon at a 10 ppb concentration in a vegetable matrix.45 The temperature program –​130°C to 200°C at 50°C min–​1 –​was accompanied by a very high gas flow, viz. 32 mL min–​1. The target analyte eluted in just over 7 s, and was first determined through SIM, and then in the MSMS mode (Figure 5.15). The presence of the MI (m/​z 304) was exploited in both instances. As to be expected, the specificity of the MSMS mode was higher; in general, both the SIM and MSMS specificity will benefit from the presence of the higher-​mass ions generated by using a softer ionization methodology.

References 1. A. T. James and A. J. P. Martin, Biochem. J., 1952, 50, 679. 2. M. J. E. Golay, Gas Chromatography 1958 (Amsterdam Symposium), ed. D. H. Desty, Butterworths, London, 1958, pp. 36–​55.

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3. D. H. Desty, A. Goldup and W. T. Swanton, Gas Chromatography, ed. N. Brenner, J. E. Callen and M. D. Weiss, Academic Press, New York, NY, 1962, pp. 105–​135. 4. J. C. Giddings, Anal. Chem., 1962, 34, 314. 5. J. C. Giddings, J. Chromatogr. A, 1995, 703, 3. 6. F. David, D. R. Gere, F. Scanlan and P. Sandra, J. Chromatogr. A, 1999, 842, 309. 7. M. van Lieshout, M. van Deursen, R. Derks, H.-​G. Janssen and C. Cramers, J. Microcolumn Sep., 1999, 11, 155. 8. A. Van Es, J. Rijks and C. Cramers, J. Chromatogr. A, 1989, 477, 39. 9. M. van Deursen, H.-​G. Janssen, J. Beens, P. Lipman, R. Reinierkens, G. Rutten and C. Cramers, J. Microcolumn Sep., 2000, 12, 613. 10. C. Bicchi, C. Brunelli, C. Cordero, P. Rubiolo, M. Galli and A. Sironi, J. Chromatogr. A, 2005, 1071, 3. 11. C. Bicchi, C. Brunelli, M. Galli and A. Sironi, J. Chromatogr. A, 2001, 931, 129. 12. M. M. van Deursen, J. Beens, H.-​G. Janssen, P. A. Leclercq and C. A. Cramers, J. Chromatogr. A, 2000, 878, 205. 13. J. C. Giddings, S. L. Seager, L. R. Stucki and G. H. Stewart, Anal. Chem., 1960, 32, 867. 14. C. P. M. Schutjes, E. A. Vermeer, J. A. Rijks and C. A. Cramers, J. Chromatogr. A, 1982, 253, 1. 15. M. S. Klee and L. M. Blumberg, J. Chromatogr. Sci., 2002, 40, 234. 16. G. Purcaro, P. Q. Tranchida, C. Ragonese, L. Conte, P. Dugo, G. Dugo and L. Mondello, Anal. Chem., 2010, 82, 8583. 17. G. M. Gross, B. J. Prazen, J. W. Grate and R. E. Synovec, Anal. Chem., 2004, 76, 3517. 18. R. B. Wilson, J. C. Hoggard and R. E. Synovec, Anal. Chem., 2012, 84, 4167. 19. L. Mondello, A. Casilli, P. Q. Tranchida, R. Costa, B. Chiofalo, P. Dugo and G. Dugo, J. Chromatogr. A, 2004, 1035, 237. 20. P. Q. Tranchida, M. Lo Presti, R. Costa, P. Dugo, G. Dugo and L. Mondello, J. Chromatogr. A, 2006, 1103, 162. 21. E. Colombo, C. Ghizzoni and D. Cagni, Citrus, ed. G. Dugo and A. Di Giacomo, Taylor & Francis, London, 2002, pp. 539–​556. 22. G. Dugo, A. Cotroneo, A. Verzera and I. Bonaccorsi, Citrus, ed. G. Dugo and A. Di Giacomo, Taylor & Francis, London, 2002, pp. 201–​317. 23. P. Sandra and F. David, J. Chromatogr. Sci., 2002, 40, 248. 24. G. F. Fries, J. Anim. Sci., 1995, 73, 1639. 25. L. Mondello, P. Q. Tranchida, P. Dugo and G. Dugo, J. Pharm. Biomed. Anal., 2006, 41, 1566. 26. P. Q. Tranchida, M. Zoccali, L. Schipilliti, D. Sciarrone, P. Dugo and L. Mondello, J. Sep. Sci., 2013, 36, 2145. 27. S. Jaggi, C. Sood, V. Kumar, S. D. Ravindranath and A. Shanker, J. Agric. Food Chem., 2001, 49, 5479. 28. L. Mondello, R. Shellie, A. Casilli, P. Q. Tranchida, P. Marriott and G. Dugo, J. Sep. Sci., 2004, 27, 699.

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29. E. N. Fuller, P. D. Schettler and J. C. Giddings, Ind. Eng. Chem., 1966, 58, 19. 30. K. Maštovská, S. J. Lehotay and J. Hajšlová, J. Chromatogr. A, 2001, 926, 291. 31. T. Cajka, J. Hajslova, O. Lacina, K. Mastovska and S. J. Lehotay, J. Chromatogr. A, 2008, 1186, 281. 32. U. Koesukwiwat, S. J. Lehotay, S. Miao and N. Leepipatpiboon, J. Chromatogr. A, 2010, 1217, 6692. 33. U. Koesukwiwat, S. J. Lehotay and N. Leepipatpiboon, J. Chromatogr. A, 2011, 1218, 7039. 34. A. Wang, H. D. Tolley and M. L. Lee, J. Chromatogr. A, 2012, 1261, 46. 35. C. Bicchi, C. Brunelli, C. Cordero, P. Rubiolo, M. Galli and A. Sironi, J. Chromatogr. A, 2004, 1024, 195. 36. E. U. Ehrmann, H. P. Dharmasena, K. Carney and E. B. Overton, J. Chromatogr. Sci., 1996, 34, 533. 37. J. Luong, R. Gras, R. Mustacich and H. Cortes, J. Chromatogr. Sci., 2006, 44, 253. 38. C. Bicchi, E. Liberto, C. Cagliero, C. Cordero, B. Sgorbini and P. Rubiolo, J. Chromatogr. A, 2008, 1212, 114. 39. C. Bicchi, A. D’Amato and P. Rubiolo, J. Chromatogr. A, 1999, 843, 99. 40. V. Schurig, TrAC, Trends Anal. Chem., 2002, 21, 647. 41. P. Dugo, T. Kuum, M. L. Crupi, A. Cotroneo and L. Mondello, J. Chromatogr. A, 2006, 1112, 269. 42. M. van Deursen, H.-​G. Janssen, J. Beens, G. Rutten and C. Cramers, J. Microcolumn Sep., 2001, 13, 337. 43. M. Giardina and J. D. McCurry, J. Chromatogr. Sci., 2016, 54, 683. 44. A. Amirav, A. Gordin, M. Poliak and A. B. Fialkov, J. Mass Spectrom., 2008, 43, 141. 45. A. B. Fialkov, U. Steiner, L. Jones and A. Amirav, Int. J. Mass Spectrom., 2006, 251, 47.

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Part 4 Two-​dimensional Gas Chromatography-​ based Processes: Principles, Practical Aspects and Applications in Food Analysis

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CHAPTER 6

Heart-​cutting Two-​dimensional Gas Chromatography HANS-​GEORG SCHMARRa,b a

Dienstleistungszentrum Ländlicher Raum (DLR) Rheinpfalz, Institute for Viticulture and Oenology, Breitenweg 71, 67435 Neustadt an der Weinstraße, Germany;  bUniversity Duisburg-​Essen, Faculty for Chemistry, Instrumental Analytical Chemistry, Universitätsstraße 5, 45141 Essen, Germany Email:  hans-​[email protected]

6.1 Definitions and Fundamental Considerations In the course of the present chapter, abbreviations and conventions previously proposed will be used.1,2 The basic concepts of multidimensional chromatography (MDC) date back to the early work of Giddings, who distinguished separations with a continuous two-​dimensional (2D) separation, and coupled column separations with sequential zone displacement.3,4 Later, Blumberg and Lee discussed the previous concepts and proposed the definition of “n-​ dimensional analysis as one that generates n-​dimensional displacements”.5 In gas chromatography (GC), MDC processes with continuous 2D displacements became later known as comprehensive two-​dimensional GC, and is today abbreviated as GC×GC.6,7 Chapter 7 will outline this technique in detail. In the literature, coupled column separations are abbreviated as GC-​GC, or MDGC (multidimensional GC). The technique MDGC may involve one second dimension (2D) column, or more than one, and one or several

Food Chemistry, Function and Analysis No. 17 Advanced Gas Chromatography in Food Analysis Edited by Peter Q. Tranchida © The Royal Society of Chemistry 2020 Published by the Royal Society of Chemistry, www.rsc.org

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Figure 6.1 Schematic presentation of normal 1D (top), H/​C 2D with two H/​Cs (center) and comprehensive 2D chromatography (bottom).

numbers of 2D displacements (so-​called heart-​cuts: “H/​Cs”). A schematic representation of these fundamentally different chromatographic modes is visualized in Figure 6.1. A typical MDGC setup is often only a two-​dimensional GC system, with a limited number of zones sampled from a (first-​dimension) 1D column. These zones are transferred onto the 2D column with a different stationary-​ phase selectivity and subjected to an additional and independent separation process. The main goal of coupling two (or more) analytical separations is to enhance the peak capacity of the analytical system. The reasons for this can be the complexity (dimensionality) of the sample, particularly insofar as fully ­resolved peaks of target analytes may not be guaranteed with a one-​ dimensional separation.8 An important aspect for the second-​dimension separation is that its selectivity should be sufficiently deviant from the 1D column selectivity to allow for a different elution pattern. This is best explained with enantioselective separations, using an achiral stationary phase in the 1D separation column and a chiral stationary phase in the 2D separation column.9,10 For the continuous mode of GC×GC, a fast 2D separation is required, which is usually achieved with short and narrow-​bore 2D columns, when using thermal modulation.11 In H/​C MDGC, the 2D separation is more independent, and most applications involve normal dimensions for the 2D column, resulting in 2 D peak widths that are in the range of seconds. Therefore, MDGC applications do not require fast detectors, and thus allow the implementation of a

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biological detector, such as the human nose (GC–​olfactometry or GC-​O; see Chapter 9), an important detector in flavor analysis.12–​15 On the contrary, peak widths after a typical GC×GC separation are more in the range of 50–​300 ms, therefore demanding fast detector acquisition rates.16 Such an aspect either prevents olfactometric detection or at least generates high demands on the application of this detection mode.17 Since its first report in the literature,18 the principals and applications of the H/​C MDGC technique have been reviewed over the last decades, e.g., by Deans,19 Willis,20 Bertsch,21 Schomburg,22 Mahler et al.,23 Engewald and Steinborn,24 Bertsch,25 Mondello and co-​authors,26 Kollmannsberger and Nitz,27 Ramos and Brinkman,28 Tranchida et al.,29 Marriott et al.30 and Nolvachai et al.,31 to name a few distinguished ones. Thus, it is not intended to review all details here, but to recommend these publications for more intense study. The following sections will try to emphasize the fundamental technical implementations, optimization strategies, application fields and recent developments in the area of MDGC.

6.2 Technical Implementations for H/​C MDGC The principal instrumental implementations for H/​C MDGC or comprehensive 2D GC are presented in Figure 6.2. Whereas H/​C MDGC involves a dedicated “switching device”, GC×GC involves a “modulator”. Either technical implementation may utilize a common GC oven, but particularly MDGC applications gain in flexibility when using a dedicated oven for individual temperature programming of the 2D column.32 Furthermore, an additional cryo-​focusing device in the 2D oven is beneficial, notably with applications that utilize more than one H/​C and transferring compounds with low retention onto the 2D column. Beneficial and straightforward to realize with current state-​of-​the-​art electronic pressure regulation is a 1D column backflush.33 Such a column backflush ensures that high-​boiling material that is still on the 1D column after elution of the compounds of interest can then be flushed backwards into, for example, a hot vaporizing or programmable temperature vaporizing (PTV) injector. The latter injector type also allows utilization of additional bake-​out programs to ensure that no high-​boiling material builds up within the injector. Thus, with an optimized 1D column backflush, there is no demand for high conditioning temperatures or long conditioning periods in order to restore a blank status for the next sample being injected. Particularly with a single-​oven MDGC system and 2D columns with limited operational temperatures, such as with chiral stationary phases, this is an important aspect for overall system stability in routine analysis and should be incorporated whenever possible.34 Some basic considerations on how to optimize a backflush operation have been discussed previously.35 The main flow-​switching techniques employed with MDGC instrumental set-​ups are mechanical valve switching,20,36 and differential pressure regulation, as first described by Deans –​a principal that entered the literature as the so-​called “Deans switch” (Figure 6.3).37 Today, H/​C MDGC implementations

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H/C MDGC

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G GC1 C1

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1st Det

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GC2* GC2*

2nd Det

2D

comprehensive 2D GC Inj

G GC1 C1

GC2* GC2*

2nd Det

modulator

1D

2D

Figure 6.2 Schematic presentation of fundamental instrumental setups for H/​C MDGC (top) and comprehensive 2D GC (bottom). The asterisk at GC2 denotes this as an optional device.

often rely on a Deans switching device and benefit from the improvements made in manufacturing inert microfluidic devices by several vendors, as described recently.30,35 The review by Sharif et al. also summarizes a remarkable 50 years of history of the Deans switch with additional insights into earlier developments of the MDGC technique.35 A device for column flow-​switching that can be found in numerous applications from the 1980s to the 1990s is based on “Live Switching”, which was incorporated into the well-​known “SiCHROMAT 2” GC from Siemens. The principle of Live Switching is outlined in Figure 6.4. Today, this formerly widespread switching device is no longer commercially available to the (laboratory) analytical chemist, but can still be found in the Siemens instruments GC line for “Process Gas Chromatography”.

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Figure 6.3 The primary design of a Deans switch. Arrows show the flow direction. The design consists of a pressure controller (3), a tap (2) through which an external flow was fed into the system. Columns 1 (4) and 2 (12) are mounted in the oven (5) with an injector port (1) and detector (13), and are connected by a piece of capillary (8). A further tap (6) was connected to needle valve (7) to control the primary gas flow. Another flow was introduced by a second pressure controller (11) and fed through tap (10), and measured by a pressure gauge (9). In the standby mode (a), the flow introduced by 11 is higher than the flow from 3, which diverts the primary flow toward 7 and is vented. In the switching mode (b) the flow from 11 is lower than flow 3 and merges into a single flow toward 13, through 12. Reproduced from Ref. 35 with permission from Elsevier, Copyright 2016.

Live Switching is based upon the balance of flows by in-​line restrictors and two pressure controllers (head and medium pressure) indicated on gauges (Pa, Pm) and utilizes a specially designed T-​piece, shown in Figure 6.4B. Some differences to Deans switching exist, with these being described in more detail in a review by Mahler et al.23 Besides valve and Deans switching, there is an alternative switching device available, called “Moving Capillary Stream Switching” (MCSS), which is offered by Brechbühler AG.38 This system achieves column effluent switching basically by an open coupling, mechanical positioning of the 1D capillary column within a glass “dome” and tuning of the column and effluent flows.39 Setup and optimization have been described earlier,40 and Figure 6.5 shows a diagram of the MCSS device. Both the MCSS and Deans switch devices may also be used as variable effluent switchers, important, for example, in flavor analysis in GC-​O applications

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Pm (+)

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Injector Pre Column

Split and

Main Column

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Purge to Vent or Detector

Live Tee Switch

Operating Modes

C)

Heartcut On Pa > Pm(-) > Pm(+)

Heartcut Off Pa > Pm(+) > Pm(-)

Backflush Mode Pm(+) > Pm(-) > Pa

Figure 6.4 Flow diagram of the Siemens Live Switching system. (A) Schematic diagram, (B) “Live Tee Switch” for capillary columns, (C) Operating modes. Abbreviation: NV: needle valve. Reproduced from Ref. 101 from SIEMENS AG, Karlsruhe, Germany, Copyright 2002–​2008.

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Figure 6.5 Scheme of the “Moving Capillary Stream Switching” device. Abbreviations: P: pressure regulator, inj: injector, det: detector.

and parallel vacuum outlet conditions, as with detection by mass spectrometry (MS). The theoretical background of the underlying flow regimes have recently been outlined by Boeker and co-​authors for the MCSS,41 as well as for the Deans switch.42 The latter publication also provides a useful spreadsheet-​ based Deans switch calculator as part of the supporting information. As stated by Sharif et al., the ideal flow switching device should inter alia be (1) suitable for high-​temperature operation, (2) inert to the analytes, and (3) able to change the flow of the gas by an external gas control without altering the natural flow of the system.35 Notably, the demand for high-​temperature operation favors the use of switching devices without a mechanical motion within the hot GC oven, although current switching valves for GC are specified to withstand temperatures up to 350°C.43 However, classical valve switching is more often employed in gas analysis, and MCSS/​Deans switch are typically used in flavor analyses –​applications that normally are performed with moderate operating temperatures. Among the current suppliers for MDGC instrumentation, Shimadzu offers a switching system called “Multi-​Deans Switching” (flow channel switching based on pressure difference), characterized by the presence of three

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restrictors. According to Shimadzu information, classical Deans switching may result in problems such as a reduced recovery rate and fluctuations in the retention time. It is stated that their Multi-​Deans Switching system (Figure 6.6) significantly reduces the likelihood of fluctuations in the retention times of eluted components, even if switching is performed several times. The Shimadzu dedicated GC system “MDGC-​2010” is available with one or two GC ovens. A dedicated MDGC control software package, “MDGCsolution”, is also provided that allows the setting of the analytical conditions for both the first and second GC systems, or GC-​MS together. It is not necessary to switch Standby Mode Pressure(ΔP2)

Pressure(ΔP1) Pressure P-ΔP1

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Pressure(ΔP1) Pressure P-ΔP2

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Figure 6.6 Scheme of the “Multi-​Deans Switching” device from Shimadzu Abbreviations: APC: advanced pressure controller, DET: detector. Reproduced with permission from Shimadzu Corporation MDGC-​2010 brochure 2013.

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between different software products in order to make fine adjustments to the analytical conditions, a user-​friendly aspect that is not implemented with, for example, microfluidic devices that may be retrofitted into third-​party GC instruments. In the standby mode, pressure P at the entrance of the 2D column is higher with respect to that at the 1D column outlet (P –​ΔP1), directing the 1D flow to the monitor detector. After valve switching, the pressure P –​ΔP2 at the entrance of the 2D column is now lower with respect to that at the 1D column outlet, directing the 1D flow to the second column. Agilent Technologies offers a product line based on their capillary flow technology (CFT), one of which is a Deans switch device (Agilent documentation G2855B, 2011). The CFT line is based on photolithography techniques with an inertized metal base. It offers a high-​pressure capability, low void volume, high degree of inertness, and a high maximum operational temperature. A useful “Deans switch calculator” software is available, which features the method parameters (column dimensions, temperature, flows, etc.) and calculates the required restrictor length and pressure settings necessary to operate the system (Figure 6.7). As can be seen in the software, the Deans switch receives (on its front side) the 1D flow in a central position, while the ports for the 2D column and the restrictor (connecting the monitor detector) are located in external positions. The two auxiliary flow branches are connected to two external ports, on the back side of the Deans switch. A selectable one-​or two-​dimensional gas chromatography–​mass spectrometry (selectable 1D/​ 2D GC-​ MS) system with selective olfactometric/​ nitrogen-​phosphorus/​pulsed flame photometric/​MS detection (O/​NPD/​ PFPD/​MS) was described by Sasamoto and Ochiai.44 The instrument uses Agilent CFT and low thermal mass (LTM) GC ovens, and is commercialized by Gerstel. Software support for Deans switch calculation (Agilent) as well as instrument control is provided (Gerstel). Besides the reduced lab-​bench footprint, other advantages are represented by the simple and fast selection of 1D GC-​MS or 2D GC-​MS operation without any change of the instrumental set up, and simultaneous MS and olfactometric or element-​specific detection for both 1D and 2D separations. The potential of the instrument was demonstrated in the analysis of sulfur compounds in whisky.45 The described system is also available with a preparative fraction collector (PFC) module, suitable for further (structural) analysis of potent flavor compounds. Various 1D and 2D configurations (with O/​MS detection and fraction collection), attained by diverting flows from two pressure control modules (PCMs), are shown in Figure 6.8.46 The option to trap and enrich compounds after MDGC separation is particularly helpful in applications where unknown (off-​)flavor compounds have to be detected and further characterized within complex matrices, as was demonstrated for wine.46 Figure 6.9 shows both 1D GC and 2D GC total ion current (a) and olfactometry (b) signals of a wine spiked (at the 5–​50 ng L−1 level) with 3-​iso-​butyl-​2-​methoxypyrazine (IBMP; bell pepper-​ like), 2,4,6-​trichloroanisole (TCA; corky) and geosmin (earthy/​musty). Sample preparation was carried out through stir bar sorptive extraction (SBSE). Two heart-​cuts were performed in the periods 12.40–​12.55 min and 16.10–​17.00

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Figure 6.7 Interface of the “Deans switch calculator” software (Agilent) for entering relevant operational parameters. Abbreviation: PCM: pressure control module.

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Figure 6.8 Schematic flow diagrams for the selectable 1D/​2D GC-​O/​MS system with a single preparative fraction collector (PFC) module. (a) 1D GC-​O/​ MS; (b) 1D GC-​PFC; (c) 2D GC-​O/​MS; (d) 2D GC-​PFC. Numbering and abbreviation: 1, thermal desorber; 2, PTV inlet; 3, LTM –​GC1; 4, restrictor; 5: cryotrap (optional); 6, LTM –​GC2; SV, solenoid valve. Reproduced from Ref. 46 with permission from Elsevier, Copyright 2011.

min; at the end of the second heart-​cut the first column was backflushed, while the 2D separation proceeded at the same time. It is noteworthy that IBMP, TCA and geosmin were not detected through MS in the scan mode, while they were clearly detected through olfactometry. As mentioned previously, the selectable 1D/​2D GC-​O/​MS instrument was also characterized by the presence of a PFC unit. Figure 6.10 illustrates the 2D GC-​MS chromatogram after the collection of 20 injection cycles, followed by thermal desorption, leading to the clear detection of IBMP and geosmin. The

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Figure 6.9 First and second dimension TIC and olfactometric signals obtained by stir bar sorptive extraction–​thermal desorption (SBSE-​TD) and selectable 1D/​2D GC-​O/​MS for wine spiked at the 5–​50 ng L−1 level with 3-​ iso-​butyl-​2-​methoxypyrazine, 2,4,6-​trichloroanisole and geosmin. (a) 1D/​ 2D TIC; (b) 1D/​2D olfactometric signals. Reproduced from Ref. 46 with permission from Elsevier, Copyright 2011.

extraction of ion chromatograms was, on the other hand, necessary for the detection of TCA. Besides the selectable 1D/​2D GC-​MS system with the LTM GCs described above, Gerstel also offers a flow-​switching system defined as “Multi Column Switching” (MCS), which is illustrated in Figure 6.11. It is based on a proprietary switching device, advanced electronic pneumatic control, and –​according to the company information –​allows optimized gas flows on both 1D and 2 D columns without diluting or splitting the 1D eluate. Whereas LTM GCs operate with dedicated capillary columns, the MCS system can be installed in standard GCs, thus allowing use of the full spectrum of available separation columns.

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Figure 6.10 Second dimension total ion chromatogram and mass chromatograms (m/​z 195, 197) after PFC enrichment with 20 injection cycles for spiked wine. (1) IBMP (25 ng L−1); (2) TCA (5 ng L−1); (3) geosmin (50 ng L−1). Reproduced from Ref. 46 with permission from Elsevier, Copyright 2011.

The company SGE Analytical Science (now part of the Trajan organization) offers a micro-​channel device (MCD) that is based on a proprietary micro-​ channel technology called SilFlow™ with deactivated internal channels. Low dead volumes, high pressure capability, low thermal mass and high maximum operation temperature are benefits of this device. It can be fitted into third-​party instruments, as e.g., with Thermo Fisher Scientific, the latter offering a Deans switch installation kit for their current GC line and a Deans Switch Calculator software that allows the calculation of the relevant operating parameters. A scheme of a Deans switch set-​up based on a SilFlow device is given in Figure 6.12. A similar MCD line is available from PerkinElmer, defined “Swafer”, which uses small circular metal disks into which micro-​channels have been laser-​ fabricated to provide flow switching and splitting capabilities (Figure 6.13). The D-​Swafer allows the original Deans switching process, whereas the S-​ Swafer is used rather as a splitting device to direct or combine the flow to multiple outlets or combine it from multiple inlets. The “Cookbook” for the PerkinElmer Swafer platform lists a total of 15 different hardware arrangements in which the two Swafers can be configured. These configurations enable a total of 18 different analytical techniques. Also available from PerkinElmer is the “Swafer Utility Software” (SUS) with support for all Swafer platform devices, facilitating the necessary settings for a complex application, such as heart-​cutting.

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Figure 6.11 Scheme of the MCS device. Abbreviations: CIS, cooled injection system; FID, flame ionization detector; CTS, cryogenic trapping system; ODP, olfactometric detection port; MSD, mass selective detector. Reproduced with permission from Gerstel GmbH, Mülheim, Germany.

Figure 6.12 Scheme of the SGE MCD “SilFlow™” Deans switch (photograph to the right). Reproduced with permission from Thermo Fisher Scientific.

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Figure 6.13 (A) Photograph of the D-​Swafer from PerkinElmer and (B) interface of the “Swafer Utility Software” with parameter options for Deans switch operation. (A) Reproduced from Ref. 35 with permission from Elsevier, Copyright 2016.

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6.3 Optimization in MDGC Applications 6.3.1 Benefits of Narrow Heart-​cut Windows

signal intensity

analyte

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Probably one of the most important issues for choosing H/​C MDGC as an analytical technique is the goal or necessity to reduce the sample matrix that might interfere with the separation of target analytes. Particularly in food analysis with complex matrices, the number of potentially co-​eluting substances should be reduced as much as possible to allow for undisturbed detection of the targeted analyte(s). As a key issue during method development, the question then arises about how to define the so-​called “cut-​window” or “transfer-​ window”, namely the time necessary to transfer compounds eluting from the 1D column onto the 2D column. The appropriate transfer period has to consider several aspects, most notably the retention time of a 1D peak and its width. These are parameters that depend on the chromatographic conditions, as well as on the concentration of the analyte in the sample. Although current GC ovens allow highly reproducible temperature programming, 1D retention times may shift for other reasons. An aspect that should be considered in this respect is solvent-​trapping, encountered with large-​volume splitless injection, on-​column injection, or in on-line liquid chromatography–​gas chromatography (LC-​GC). Depending on the amount and nature of the solvent injected, volatiles released at the end of solvent-​trapping may experience different positions within the capillary system (and peak shapes) when they

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Further information on the above-​mentioned implementations may be obtained from the relevant manufacturer (sources summarized in ref. 47), or found in recent reviews on MDGC.30,35

b

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* 1

D retention time

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D retention time

Figure 6.14 Optimization for H/​C MDGC with SIDA-​based quantification. (a) Wide cut-​window with isotopic separation due to (normal) isotope effect in the first dimension. (b) Narrow cut-​window with no or marginal isotope effect in the first dimension. Arrows in red indicate mandatory widths for cut-​windows to transfer the analyte(s), whereas arrows in green indicate optional transfer periods to ensure a complete transfer.

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start their chromatographic process. This then causes shifts in retention time (and sometimes in peak shape) if analytes are not focused further down the capillary system, e.g., by phase soaking,49 or the so-​called “retention gap effect”.50 However, even in a solvent-​free situation, distorted peak shapes or even shifts in retention may occur due to “co-​chromatography” of a minor compound co-​eluting with an abundant compound.51 Furthermore, in real-​ world samples, one also has to consider the effect of concentration on peak shapes and widths, as overloading may broaden the width of a peak considerably.52 In practice, all this means that various situations have to be considered and the definition of an appropriate and narrow transfer window is an important and non-​trivial step. Commonly, particularly in food flavor applications, quantitative analysis is performed through a stable isotope dilution assay (SIDA), and optimization must then also consider the nature of the isotope effect.53 The latter is notably pronounced with the commonly used deuterated isotopic standards in SIDA applications. The ideal optimization to achieve a narrow cut-​window then is a chromatographic pre-​separation without a noticeable isotope effect in the 1 D separation, as illustrated in Figure 6.14b. The non-​optimized (common) situation illustrated in Figure 6.14a demands wider cut-​windows to ensure a complete transfer of both the isotopic standard and the analyte, thus provoking a co-​transfer of additional (matrix) compounds. As a consequence, such co-​transferred compounds could disturb the unambiguous detection of the targeted analytes. An example of this optimization strategy for narrow-​cut windows involved the trace-​level analyses of 3-​alkyl-​2-​methoxypyrazines [3-​iso-​propyl-​2-​ methoxypyrazine (IPMP), sec-​butyl-​2-​methoxypyrazine (SBMP) and 3-​iso-​ butyl-​2-​methoxypyrazine] in a complex matrix (galbanum oil).54 Besides SIDA-​based quantification with deuterated internal standards, enantioseparation of the chiral SBMP occurred on an enantioselective 2D separation column. The authors discussed in detail the benefit of their optimization strategy in order to reduce the transfer of possibly co-​eluting matrix compounds. A crucial point in their application was to choose a suitable chemical composition for the 1D column stationary phase. A medium-​polar ionic liquid phase [1,12-​di(tripropylphosphonium)dodecane bis(trifluoromethanesulfonyl)imide; IL60] resulted in an insignificant first-​dimension separation between labeled and unlabeled compounds. Critical aspects of such narrow-​cut windows with a partial transfer versus a complete transfer were discussed, particularly with respect to the consequences for quantitative data, as well as for the enantiomeric composition. Some of these results will also be presented hereafter. First of all, the increase in selectivity with a narrow (18 s) versus a wide (42 s) cut-​window is evident in Figure 6.15. With a transfer period of 42 s, a major co-​eluting peak was seen at the 2D retention time of SBMP, an interference that was not present with a transfer period of only 18 s (Figure 6.15b), allowing undisturbed quantification on the corresponding quantifier fragment ion

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Figure 6.15 Enantioselective-​MDGC with SIDA-​based quantification of 3-​alkyl-​2-​ methoxypyrazines (MP)s in galbanum oil. (a) 1D separation on an SLB-​ IL60 pre-​column; a* cut-​windows. (b) Separation on an enantioselective 2 D column (Lipodex G®), upper trace with 42 s cut-​windows (each) or 18 s (lower trace); absolute scaling intensities given for estimation of real peak size. (c) Quantification on narrow, 18 s cut-​windows via quantifier ions: m/​z 137 (140), 138 (141) and 124 (127) for IPMP (d3-​IPMP), SBMP (d3-​SBMP), and IBMP (d3-​IBMP), respectively. Reproduced from Ref. 54 with permission from Springer Nature, Copyright 2013.

traces (Figure 6.15c). On the other hand, an excessively narrow heart-​cut window has to be avoided because it can lead to partial transfer of the labeled and unlabeled compounds (Figure 6.16a), resulting in erroneous data when using SIDA-​based quantification. Such a situation is illustrated in Figure 6.16b. On the other hand, in enantioselective-​MDGC (enantio-​MDGC) with an achiral stationary phase in the first dimension and an enantioselective one in

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Figure 6.16 Consequences of complete or partial transfer in MDGC analysis. (a) Co-​ eluting isotopic MPs in 1D allow narrow cut-​windows, but complete or partial transfer conditions (as indicated by the arrows) must be considered. (b) Influence on peak area ratios of unlabeled/​labeled MPs (n = 2). (c) Influence on peak area ratios for (S)/​(R)-​SBMP (n = 2). Reproduced from Ref. 54 with permission from Springer Nature, Copyright 2013.

the second, a partial transfer of the 1D peak does not influence the enantiomeric composition as the achiral 1D column cannot separate the enantiomers (Figure 6.16c).

6.3.2 Overcoming Loss of Selectivity with Wide Heart-​cut Windows in the First Dimension by MS Detection Contrary to the use of narrow heart-​cut windows, one may encounter recently described applications that do not consider a minimizing strategy to reduce the sample matrix by using H/​C MDGC. A recent example in this respect is an application on the stereodifferentiation of oak (whisky) lactones that utilized a H/​C window of more than 10 min for transferring the two diastereomers

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from an achiral D column to a chiral D one. Despite such an extensive transfer period, the authors proposed the use of multidimensional chromatographic techniques, here enantio-​MDGC [and enantio-​(LC-​GC)], for the improvement of the analytical performance in the analysis of complex samples, more specifically, the reliability of oak lactone stereoisomer identification. However, that statement, promising at first glance, has to be discussed in more detail. In their work, Martinez et al. used MS detection with electron ionization (EI) and an extracted ion chromatogram at m/​z = 99 for the selective identification of the oak lactone enantiomers. In principle, selectivity was thus increased using a selective detection mode, probably overcoming co-​elution risks by the non-​optimized transfer time. Yet, selective MS detection has to consider the low molecular weight of volatile compounds, particularly in food flavor analysis. Fragment ions in the range below about 100 Da have to be scrutinized due to the ubiquitous presence of such small fragment ions. For example, the fragment ions m/​z 85 or 99 that are usually considered to be suitable ions for the selective detection and quantification of γ-​lactones and δ-​lactones (and methyl-​branched γ-​lactones, such as oak lactone), respectively, are not unique to these compound classes. In complex matrix situations, co-​eluting compounds may interfere with these lactones on the respective ion traces. Hener and Mosandl discussed this in detail and listed some ubiquitous mass fragments, such as (C6H13)+, from alkyl chains containing ≥ six carbon atoms, or (C5H9O)+, from 2-​methylbutanoic acid (esters), interfering with, for example, the base peak of γ-​lactones (m/​z 85).56 Therefore, the necessary selectivity should be attained through MS/​MS detection,57 or at best by a combination of separation and detection, as with MDGC-​MS/​MS.34,58,59 However, despite using H/​C MDGC and MS/​MS detection, co-​elution was an issue for reliable quantification of α-​ionone in an application involving norisoprenoids in wine aroma.34 In this respect, the careful optimization of selected reaction monitoring (SRM) operation was necessary to finally allow undisturbed analysis of the target compounds (Figure 6.17). In their work, the authors highlighted an important aspect in food flavor analysis, and proposed that precursor ions for MS/​MS analysis should involve the highest possible mass to initiate SRM fragmentation. As emphasized above, ubiquitous masses in the low Da mass range may otherwise yield non-​selective SRM transitions, possibly resulting in erroneous data. A problem in flavor analysis consists of the excessive fragmentation of many flavor compounds, so no or only low-​intensity molecular ions or mass fragments with higher Da values are available for SRM experiments. Alternatively, the popular EI mode might be changed to a soft ionization technique, such as positive chemical ionization, or a moderate EI voltage of 10–​20 eV, using a dedicated ion source.34 In conclusion, sample preparation, separation and detection are best utilized in combination, and H/​C MDGC is a key step to minimize

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Figure 6.17 Cases of co-​elution involving the analysis of α-​ionone in a wine sample (Pinot Noir), selecting different SRM transitions [H/​C MDGC chromatograms (2D)]. Reproduced from Ref. 34 with permission from Springer Nature, Copyright 2016.

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co-​elution risks. Although for some analysts, with the availability of modern MS instrumentation, allowing for MS/​MS and high-​resolution analyses, the recommendation of a classical H/​C multidimensional separation could appear old-​fashioned and is occasionally questioned. However, on the basis of the author’s own experience involving the quantitative analysis of trace-​ level compounds in the field of wine aroma analysis, MS as such is not always capable of guaranteeing reliable analytical data, and often H/​C MDGC with an optimized chromatographic separation has proved to be an appropriate choice to solve quantification problems. With low molecular weight flavor compounds, the unequivocal identification of structurally similar compounds by GC-​MS is critical, due to their similar spectrometric and often also chromatographic behavior. A demonstrative example is the analysis of dimethyl methoxypyrazines, with two of three isomers having identical mass spectra and almost identical retention behavior on a common stationary phase.60 As MS alone failed to differentiate two of the isomers, inconsistencies can enter the literature and (MS) databases, if no chromatographic separation can be achieved. This confirms an earlier statement by Molyneux and Schieberle, outlining their perspectives for reliable compound identification, ensuring high standards of work submitted to a renowned food chemistry journal.61

6.4 Applications of H/​C MDGC in Food and Flavor Analysis With the development and availability of sophisticated instrumental techniques, classical “wet chemistry” methods in food analysis have become less important. The fundamental role of GC for the analysis of volatile and semi-​volatile compounds contained in foodstuffs is obvious, and various one-​or two-​dimensional GC techniques, comprising packed and capillary coupling, as well as sample preparation, or hyphenation with sophisticated detection techniques, were already summarized in the 1980s, among others by Schreier,62 Nitz63 and David.64 In food analysis, applications of MDGC are widely found in the field of flavor and fragrance analysis and have been reviewed earlier.31,35,65 The well-​known field of enantio-​MDGC in flavor analysis will be covered in the next section. However, MDGC applications can also be found in environmental contaminant analysis, with dioxins, polychlorinated biphenyls, polycyclic aromatic hydrocarbons as important substance classes to the food analyst.66,67 Reasons for choosing MDGC are once again the benefits gained with respect to improved separation and thus more reliable quantification. Of particular interest in the analysis of environmental toxicants are studies that consider the fate of individual enantiomers of chiral substances in bio-​or ecosystems. Here, enantio-​MDGC has been successfully applied for the analysis of, for example, toxaphenes, a highly complex mixture of chlorinated bornanes, bornenes and camphenes.68,69 In a field that is on the

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Figure 6.18 An example for a versatile two-​dimensional GC system suitable for flavor research. Abbreviations: SPME, solid-​phase microextraction; HSSE, headspace sorptive extraction; D1, intermediate effluent monitor detector; D2, effluent monitor detector; Column 1, high-​capacity pre-​column: Column 2, high-​resolution capillary or chiral column; PM, pressure regulator; R, restrictor, MV, magnetic valve, LN2, liquid nitrogen; FTIR, Fourier-​transform infrared spectroscopy; IRMS, isotope ratio mass spectrometry. Further explanations can be found in this reference. Reproduced from Ref. 27 with permission from Springer Nature, Copyright 2007.

borderline of nutrition and clinical studies, Mosandl and co-​workers investigated metabolic disease indicators that produce abnormal chiral metabolites with enantio-​MDGC, an area of research that probably deserves more intense attention in the future.70–​72 In the field of flavor analysis, in particular, trapping and enrichment techniques are important aspects in order to study and identify trace-​level compounds with aroma activity.27,73,74 In this respect, due to the complexity of most of the matrices encountered in food (flavor) analysis, satisfactory separation prior to enrichment of targeted effluent zones is required and best achieved with MDGC, as it offers excellent separation efficiency. Figure 6.18 outlines the basic concepts of general workflows in aroma analysis and aroma research, also comprising the identification and characterization of unknown odor-​active compounds. Depending on the situation, various sampling techniques, sniffing (olfactometric analysis), specific detection, effluent enrichment and on-​or off-​line detection for structural elucidation of unknown compounds become necessary and are best combined with the benefits derived from an MDGC separation. The latest developments and technologies in MDGC, bridging from H/​C MDGC to comprehensive 2D GC, as well as the implementation (hyphenation) of LC into the overall separation process, have recently been summarized by

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Figure 6.19 Scheme of an integrated GC×GC/​GC-GC system with flame ionization, olfactometry and mass spectral detection options. Reproduced from Ref. 77 with permission from the Royal Society of Chemistry.

several authors.29,31,75,76 These reviews also highlight selected applications from food analysis that include improved untargeted and targeted compound identification, fingerprinting and group-​ type analysis. In particular, the reviews of Chin and Marriott76 and Nolvachai et al.31 outline recent advances and future trends in MDGC, including three-​and higher-​dimensional GC with MS separation, the on-​line hyphenation with LC, and hybrid or multifunctional MDGC systems. Noteworthy is the system described in Figure 6.19, namely an integrated GC×GC/​GC-​GC system, suiting almost all demands of a flavor analyst.77 In a further hybrid system, a GC×GC separation step was used, prior to microfluidic flow switching (heart-​cutting) of a targeted region onto a third analytical column. Such an approach allows discrete single or multiple components, bands or regions, or any combination of these to be selected and excised from within the 2D GC×GC separation space, as was demonstrated for the analysis of coffee aroma (Figure 6.20).78 Such sophisticated setups illustrate how far the individual analytical techniques have matured, and that they can even be combined to suit demanding applications, again, particularly found with complex (food) matrix compositions, where 1D GC alone is not sufficient to deliver the necessary separation power. The last example leads to a previous conclusion of Giddings, who had earlier stated

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Figure 6.20 Two-​dimensional plots of coffee volatiles, sampled by using SPME. (A) GC×GC-​FID1 result prior to the heart-​cutting of the components closed in rectangles; (B) FID1 result obtained when the three selected components were cut from the end of the 2DM (a medium length column) to a 3DL (long) column. Further explanations can be found in this reference. Reproduced from Ref. 78 with permission from American Chemical Society, Copyright 2012.

that the two multidimensional GC modes (MDGC and GC×GC) are more complementary than competitive,3,4 a statement that is fully supported in the authors’ opinion.

6.5 Multidimensional GC in Authenticity Control: Enantioselectivity and Isotope Discrimination Authenticity evaluation in food analysis often takes advantage of the high degree of stereospecificity in enzymatic reactions. With natural chiral products, high enantiomeric purities can therefore be expected. Besides enantioselectivity, isotope discrimination during biosynthesis is also a well-​known and important principle for authentication. The information of the relationship between stable isotopes (13C/​12C, 2H/​1H, 15N/​14N and 18O/​16O) in authentic samples gives evidence on the genuineness of the natural constituents. In this respect, important analytical tools to investigate the genuineness of food products are enantio-​MDGC, with the combination of a non-​chiral 1D column

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65,79–​81

and a chiral D one, and IRMS. The on-​line determination of hydrogen isotope ratios using gas chromatography–​pyrolysis–​isotope ratio mass spectrometry (GC-​P-​IRMS) was first demonstrated by Hilkert et al.82 However, the use of GC-​IRMS is often not sufficient for the precise and accurate measurement of 2H/​1H ratios, such as those related to flavor compounds in complex fruit extracts. Therefore, MDGC-​IRMS is a better alternative and was first reported by Nitz et al.83 and further developed into enantio-​MDGC-​IRMS by Juchelka et al.84 An important aspect in MDGC-​IRMS is that the H/​C technology used should consider the dependence of the carrier gas flow on temperature. The classical pressure-​controlled column-​switching technique, as introduced by Deans in 1968,37 was found to be unsuitable in evaluating 2H/​1H isotope ratios, when temperature-​programmed column switching is necessary.85 Bilke and Mosandl demonstrated the importance of a constant carrier gas flow to obtain accurate 2H/​1H isotope ratio data.86 It was shown that a decrease in flow from 1.2 to 0.8 mL min–​1 resulted in a significant decrease of the 2H/​1H isotope ratios (9–​25‰), due to the different residence times of the analytes in the high-​temperature zone of the pyrolysis reactor. Therefore, constant-​flow MDGC was recognized as an essential prerequisite of reliable δ2H measurements. To meet such requirements, the “Multi Column Switching System” was used by Sewenig et al. in such an application.85 The schematic diagram of the system used for MDGC-​pyrolysis-​IRMS is presented in Figure 6.21. Horii et al. used MCSS as the switching device in their MDGC-​IRMS application, even though they did not provide information on carrier gas

Figure 6.21 Schematic diagram of a MDGC-​pyrolysis-​IRMS system. Further explanations can be found in this reference. Reproduced from Ref. 85 with permission from American Chemical Society, Copyright 2005.

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regulation, although in principle, constant flow mode is possible with MCSS.87 The important field of authenticity control and enantio-​MDGC-​ IRMS has been reviewed extensively.88–​92 The latest developments, using capillary flow technology and a low thermal mass 2D GC oven, are summarized in a recent publication on the characterization of the δ13C and δ2H values of individual compounds within the wax fraction of plant materials using H/​C MDGC-​IRMS.93

6.6 Preparative H/​C  MDGC Preparative gas chromatography (prep-​GC), different to analytical GC, is exploited to collect specific amounts of a pure-​as-​possible analyte (or more than one) from a mixture of volatile compounds. Such a target compound is often subjected to further analysis, i.e., nuclear magnetic resonance (NMR), for the scope of identification. In other instances, the isolated compound is used as a reference compound, especially if not available commercially. The prep-​GC collection of sample constituents, at low concentration levels, is commonly carried out using large sample volumes, a mega-​bore capillary column [i.e., 0.53 mm internal diameter (ID)] with a thick stationary-​phase film and a suitable fraction collector. The latter is often characterized by the presence of a cooling agent and/​or an adsorbent material.94 Currently, there are a series of commercially available fraction collectors for prep-​GC (e.g., Gerstel, JAS, Brechbühler, GL Sciences). The use of mega-​bore columns with a thick film of stationary phase is obviously related to the necessity of a high sample capacity; however, such separation tools are characterized by low resolving power: for example, a 0.53 mm ID × 0.53 μm df column will generate approximately 2 000 theoretical plates per meter, if operated under ideal conditions. Consequently, prep-​GC normally enables the collection of pure compounds from relatively simple food samples. When the analytical challenge increases, a common occurrence in the food-​analysis field, then the use of prep-​MDGC is strongly advisable. Two pertinent examples are described hereafter. Schellenberg et al. used off-​line prep-​MDGC to characterize the absolute configuration of thiolactones.95 Thiolactones result from the substitution of oxygen with sulfur in the rings of γ-​ and δ-​lactones, and have been reported as flavor compounds with interesting sensory properties.96 The authors first subjected a racemic mixture of γ-​thiohexalactones to a chiral separation on a 20 m × 0.46 mm ID × 2 μm df column, containing a heptakis-​(2,3-​di-​O-​methyl-​ 6-​O-​tert-​butyldimethylsilyl)-​β-​cyclodextrin (DiMe-​β-​ CD) stationary phase. Enrichment of the first eluting enantiomer was attained through a H/​C process (an MCSS device was used) and collection on a lab-​made glass trap containing Tenax, with this located in a second GC oven maintained at ambient temperature (Figure 6.22). Overloading of the chiral stationary phase, leading to a reduction in resolving power, was not considered a problem because only a partial separation of the two enantiomers was required. The enantiomers

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S

2

1

R

µ-preparative MDGC

O

tR [min]

DiMe-β-CD

Tenax trap

SH

1. elution 2

tR [min]

DiMe-β-CD

2. LiAlH4

OH

S

tR [min]

DiAc-γ-CD

Figure 6.22 Representation of an off-​line prep-​MDGC process involving the chiral separation of γ-​thiohexalactone enantiomers, enrichment of the first eluting enantiomer, chemical reduction with LiAlH4, and final separation of the formed 4-​mercaptohexanol enantiomers with known elution order on DiAc-​γ-​CD. This allowed retrospectively the assignment to be R before S (1 before 2) on DiMe-​β-​CD. Refer to the text for the abbreviations and further details. Adapted from Ref. 95 with permission from Leibniz-​Institut für Lebensmittel-​Systembiologie an der Technischen Universität München, Copyright 2000.

Figure 6.23 Scheme of a three-​dimensional prep-​GC system. Abbreviations: Inj, injector; APC, advanced pressure control unit; DS, Deans switch. Reproduced from Ref. 97 with permission from American Chemical Society, Copyright 2012.

were desorbed by using diethyl ether and subjected to chemical reduction by using LiAlH4, forming (R/​S)-​4-​mercaptohexanol. The enantiomers were then separated on an octakis-​ (2,3-​ di-​ O-​acetyl-​6-​O-​tert-​butyldimethylsilyl)-​ γ-​cyclodextrin (DiAc-​γ-​CD) column, because the separation order of (R/​S)-​ 4-​mercaptohexanol was known (the S enantiomer prior to the R one). The (R)-​4-​mercaptohexanol peak was much more intense, enabling the absolute configuration assignment of the enriched γ-​thiohexalactone.

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Sciarrone et al. used a preparative three-​dimensional prep-​GC instrument (with three monitor FIDs) to increase both the quantities and purities of the compounds subjected to collection.97 The prep-​MDGC system was composed of three GC ovens, the first housing a 30 m × 0.53 mm ID × 5 μm df “5% diphenyl” column, the second a 30 m × 0.53 mm ID × 2 μm df polyethylene glycol one, and the third a 30 m × 0.53 mm ID × 0.85 μm df medium-​polarity ionic liquid (IL-​59) column. Two Deans switches were used to link the columns in series, while a third was used to connect the third column to a glass collection tube (Figure 6.23). In the case of highly volatile solutes, the collection tube could be cooled by a flow of liquid CO2 and/​or be packed with an adsorbent. At the end of the collection process, the target compound was recovered by flushing the tube with a suitable organic solvent. The three-​ dimensional prep-​ GC system was used to isolate carotol (C15H26O), an oxygenated sesquiterpene compound, from carrot seed essential oil. Carotol is present in such a source at high concentrations (30–​40% v/​v), and is not commercially available as a pure standard compound. The first H/​C was performed between 26.8 and 28.3; as can be seen in Figure 6.24 (upper chromatogram), the monitor trace highlights overloading of the 1D low-​polarity column, a factor of minor importance because only a small fraction of the eluate is directed to the second column. The polyethylene glycol column enabled the separation of carotol from interfering compounds, with a further H/​C performed between 49.6 and 51.8 min, as shown in the middle chromatogram in Figure 6.24. The remaining co-​eluting compounds were isolated from carotol on the ionic liquid column, with a final H/​C applied between 62.5 and 64.6 min, directing the target compound to the collection tube. Due to the relatively low vapor pressure of carotol, there was no need for an adsorbent or cooling. It took an analysis time of only 216 min (three applications) to collect 2.22 mg of carotol. A purity of approximately 99.6% was found for the collected solute (measured through a conventional GC-​ FID process).

6.7 Future Perspectives: Chip-​based MDGC The need for in situ analysis with very small sample volumes and low analyte concentration is a cause for miniaturizing systems via microelectromechanical system (MEMS) technology. Conventional GC instruments are bulky and cannot be used for in situ analysis, hence in the past decades many studies have been reported with the aim of designing and developing chip-​based GC. A recent review summarizes the development and achievements in the field of chip-​based GC and its components.98 With respect to the possibilities for incorporating MDGC, the system developed by Liu et al. deserves to be mentioned here (Figure 6.25).99 Their multichannel 2D-​ µGC system was applied to the analysis of hazardous volatile organic compounds. The authors affirmed that future work will be performed on further miniaturization using micro-​valves, micro-​thermal injectors and on-​chip

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Figure 6.24 Upper chromatogram: FID1 result with and without (black trace) the H/​C; middle chromatogram: FID2 result with and without (black trace) the H/​C; FID3 result with and without (black trace) the H/​C. Reproduced from Ref. 97 with permission from American Chemical Society, Copyright 2012.

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Figure 6.25 Schematic of a 2D-​µGC system with on-​column gas detector (E). Further explanations can be found in this reference. Reproduced from Ref. 99 with permission from the Royal Society of Chemistry.

on-​ column detectors. In the field of “Process Gas Chromatography”, Siemens had already demonstrated the possibilities of MEMS-​based GC technology, incorporating MDGC with Live Switching in a dedicated system (MicroSAM™). Technical and analytical possibilities, as well as practical implementations, were discussed by Mueller.100 Such developments open the application field toward more complex samples, and eventually we will see chip-​based MDGC also in selected food analysis approaches, with on-​site availability probably becoming a driving force.

Acknowledgments The author is grateful to the various contact persons from industry that provided valuable technical information, discussion and support in copyright questions. Notably, Jörg Riener (Agilent), Thomas Albinus (Gerstel), Thomas Becker (PerkinElmer), Stephan Schröder (Shimadzu), Harald Mahler and René Jacobs (Siemens) and Daniela Cavagnino (Thermo Fisher Scientific) should be mentioned here. Special thanks go to my wife for her ongoing support in this particular period of life.

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98. F. Haghighi, Z. Talebpour and A. Sanati-​Nezhad, Lab Chip, 2015, 15, 2559–​2575. 99. J. Liu, J. H. Seo, Y. Li, D. Chen, K. Kurabayashi and X. Fan, Lab Chip, 2013, 13, 818–​825. 100. F. Mueller, ATP, Automatisierungstech. Prax., 2004, 3–​10. 101. Maintenance Manual Edition 6/​2008, MAMUMTM edition II Process Gas Chromatograph, https://​cache.industry.siemens.com/​dl/​files/​996/ ​21502996/​att_​102123/​v1/​2000596manual_​51.pdf (last accessed April 2019).

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Comprehensive Two-​dimensional Gas Chromatography PETER Q. TRANCHIDAa* AND LUIGI MONDELLOa,b,c a

Dipartimento di Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali, University of Messina, Polo Annunziata, 98168 Messina,  Italy;  bChromaleont s.r.l., c/​ o Dipartimento di Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali, University of Messina, Polo Annunziata, 98168 Messina, Italy;  cUnit of Food Science and Nutrition, Department of Medicine, University Campus Bio-​Medico of Rome, 00128 Rome, Italy *Email: [email protected]

7.1 Introduction The concept of comprehensive two-​dimensional chromatography (C2DC) is not new, inasmuch as the first C2DC application was described in 1944 by the chromatography pioneers Consden, Gordon and Martin, who separated wool amino acids on paper.1 The authors reported as follows: “A considerable number of solvents has been tried. The relative positions of the amino-​ acids in the developed chromatogram depend upon the solvent used. Hence by development first in one direction with one solvent followed by development in a direction at right angles with another solvent, amino-​acids

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Figure 7.1 Comprehensive 2D chromatogram of wool hydrolysate. Reproduced from Ref. 1 with permission from Portland Press, Copyright 1994 Cambridge University Press.

(e.g., a drop of protein hydrolysate) placed near the corner of a sheet of paper become distributed in a pattern across the sheet to give a two-​dimensional chromatogram characteristic of the pair of solvents used”. The C2DC result of a wool hydrolysate is illustrated in Figure 7.1: amino acids were first eluted along the dimension A–​B, and then along the dimension A–​C. The concept of a C2DC process is straightforward, and it was evident in that first research. It is obvious that considerable evolution has occurred in the field of C2DC, and to mention the main methodologies, comprehensive 2D LC (LC×LC) and GC (GC×GC) were first described during the same time period (beginning of the 1990s).2,3 The focus of the present chapter is on GC×GC, a revolutionary methodology, fruit of the work of J.B. Phillips and collaborators.3 In that first research, the first dimension (1D) consisted of a 21 m × 0.25 mm ID × 0.25 μm df column, with a polyethylene glycol stationary phase, while the second dimension (2D) was a 1 m × 0.10 mm ID × 0.50 μm df column with a methyl silicone stationary phase. The transfer device used was a thermal desorption modulator, while detection was carried out by using a flame ionization detector (FID). A mixture of standard compounds and a sample of coal liquids were subjected to GC×GC-​FID analysis. It is curious that the first use of GC×GC in the field of food analysis was published nearly 10 years after the first description of the technology, namely in 2000.4 To date, GC×GC has been on the analytical scene for nearly 30 years, and it has undergone considerable evolution, some of which will be described in this chapter.

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7.2 Basic Theory, General Principles, Practical and Instrumental Aspects The great strength of any C2DC method, as can be seen in Figure 7.1, is the generation of a planar separation space. Very simply, the room for peak accommodation (peak capacity –​nc) is much greater than for any other chromatographic approach. Technologies in chromatography can be divided into two main classes: one-​dimensional (1D) and multidimensional (MD). The latter approaches can be further divided in “heart-​cutting” and “comprehensive” MD methodologies (as reported in Chapter 6). The term multidimensional is herein used because even though the vast majority of MD processes are two-​dimensional, the use of three-​dimensional chromatography has been reported.5 The peak capacity of a 1D GC approach, using a classical 30 m × 0.25 mm ID × 0.25 μm df column, is dependent on the operational conditions (temperature program, gas flow) and is most often within the range 400–​600. The peak capacity of a 1D GC approach can be calculated by simply dividing the retention time window (the void time is not considered) by the average peak width (4σ). In theory, if a GC method generates a peak capacity of 500, then this essentially means that 500 peaks could potentially be stacked side by side in the resulting 1D space. However, real-​world samples, and in particular mixtures of food volatiles, are often composed of a high number of compounds with different chemical features, leading to elution sequences with a low degree of order. Such characteristics led to the following requisite: generally, the GC method peak capacity must greatly exceed the number of volatiles contained in a food sample if full analyte-​to-​analyte separation is desired. Such a concept does not account for the separation capability of the detector (e.g., mass spectrometry, vacuum ultraviolet detection). On the basis of theoretical dictations, the method peak capacity should exceed the number of sample constituents by a factor of 100 if a resolution level of 98% is desired.6 As a consequence, a peak capacity of 5 000 is required to fully separate a 50-​compound sample. Such numbers indicate that the separation power of a conventional GC column will be insufficient in many applications involving foods. Comprehensive 2D GC technologies generate the highest peak capacities available today in the GC field. The leap from heart-​cutting MDGC to GC×GC was achieved using a special transfer device, named a modulator.3 The modulation process enables continuous and sequential heart-​cutting during the evolution of the 1D chromatographic process: bands of eluate at the outlet of the first column are isolated (other definitions used are entrapped, accumulated, etc.) and then re-​injected (other definitions used are re-​mobilized, released, launched, etc.) usually onto a short column segment, which is used as the second analytical dimension. The modulation period is equivalent to the time required to complete the modulation process, as well as to the 2D analysis time, and is commonly in the range 3–​8 s. The first column is normally of conventional dimensions, and with a stationary phase of low polarity [e.g., poly(95%dimethyl-​5%diphenylsiloxane)]. The second column

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is normally of higher polarity [e.g., poly(ethylene glycol)], of smaller or same ID (e.g., 0.1–​0.25 mm) and with a 1–​2 m length (2D separations are very fast). Ideally, compounds that are unseparated at the 1D outlet are then resolved on the second column. A GC×GC chromatogram can be considered as a steady stream of hyper-​ fast (definition reported in Chapter 5) GC separations (in the second dimension), positioned one after the other. For instance, considering a GC×GC analysis time of 4 000 s and a modulation period (PM) of 5 s, the resulting chromatogram will be composed of 800 mini-​separations on the second column. Usually, only a single detector is used in GC× GC applications and so the resulting chromatogram appears as a 1D one. Figure 7.2 illustrates the positive outcome of three modulation + 2D separation processes (in b), on a peak (in a) formed of three co-​eluting compounds (α, β, γ). A standard number of 3–​4 modulations per peak are required to preserve the resolution attained in the first dimension.7 A further golden rule is that all the compounds involved in a single transfer process should elute within the timeframe of the modulation period. If an analyte fails to elute within such a

Figure 7.2 Result of three modulation + 2D separation processes (in b), on a peak (in a) formed of three overlapping compounds (α, β, γ). Reproduced from P. Q. Tranchida, P. Dugo, G. Dugo and L. Mondello, J. Chromatogr. A, 2004, 1054, 3, with permission from Elsevier, Copyright 2004.

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time (e.g., 6 s), then it will elute with the re-​injected analytes during the following modulation period (or even later), with overlap being the main risk. Such a phenomenon is defined as “wrap-​around”, and will be discussed and illustrated later. The performance of the modulation process can be evaluated through the observation of the “raw” chromatograms, and in particular, of peak shapes. Visualization in a 2D chromatogram, however, is a fundamental step for an overall evaluation of the GC×GC separation (e.g., presence of wrap-​around, amount of occupied 2D space, chemical-​class pattern formation, etc.). Apart from visualization, 2D peak integration and (mass-​spectral) identification require the use of specialized software. The generation of a 2D plot is a rather simple process, inasmuch as each 2 D hyper-​fast separation is rotated orthogonally, with respect to the 1D chromatogram x-​axis. At this point, modulated peaks are aligned along the y-​axis and are defined by a retention time usually expressed in seconds. Modulated peaks, relative to sequential modulation processes, are merged if they present the same 2D retention time (within a certain time tolerance range); if MS has been used, mass spectral similarities can also be considered for 2D peak reconstruction. The resulting planar separation space contains oval-​shaped peaks (intensity is related to color), each defined by a 1D and 2D retention time (tR1, tR2) and an area (some authors and software manuals use the term volume). Although not a necessity, three-​dimensional plots can also be visualized, containing cone-​shape peaks projected into a space defined by a z-​axis (Figure 7.3). There are six main advantages of a GC×GC process over a 1D GC one: 1. enhanced separation power [the total peak capacity (nc2D) becomes the product of that of the first and second dimensions –​nc1 × nc2]; 2. enhanced specificity (the entire sample is subjected to a separation on two chemically different stationary phases); 3. enhanced sensitivity (the chromatography band isolation process is accompanied by analyte re-​concentration, especially when using cryogenic modulation); 4. enhanced identification power due to the formation of highly organized chemical class (e.g., alkanes, fatty acid methyl esters, pyrazines, etc.) patterns in the 2D chromatograms; 5. the capability to generate true sample-​specific fingerprints; and 6. the generation of a higher amount of usable data per unit of time.

7.2.1 Modulation Techniques The invention of the GC×GC modulator is one of the most important achievements in the field of GC, comparable to the introduction of open-​tubular capillary (OTC) columns. Technological evolution occurring across four decades –​packed column, OTC column, GC×GC –​produced an increase in peak

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Figure 7.3 The process of generation of a two-​dimensional (and three-​dimensional) GC×GC chromatogram. Reproduced from J. Dallüge, J. Beens and U. A. T. Brinkman, J Chromatogr. A, 2003, 1000, 69, with permission from Elsevier, Copyright 2003.

capacity of approximately two orders of magnitude. Let us again consider a 30 m × 0.25 mm ID × 0.25 μm df column with a hypothetical peak capacity of 500: an order of magnitude increase in peak capacity would require a ×100 extension in length. Obviously, and for a series of reasons, such an option cannot be considered. However, the same peak capacity enhancement can be obtained by using a modulator, and a short segment of 2D OTC capillary column. A description of the many modulation models that have been published cannot be reported here; rather, focus will be devoted to the modulation approaches which have made the most significant impact on the GC×GC field. Obviously, initial attention must be directed to the modulator first reported by Phillips and co-​workers, which was constructed by using the initial part (15 cm) of the 2D column, this being coated with a film of electrically conductive paint (gold) and looped outside the GC oven. The thermal desorption modulator (TDM) was characterized by two stages and functioned as follows (Figure 7.4):

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Figure 7.4 The dual-​stage thermal desorption modulator. Reproduced from Z. Liu, S. R. Sirimanne, D. G. Patterson, Jr., L. L. Needham and J. B. Phillips, Anal. Chem., 1994, 66, 3086 with permission from American Chemical Society, Copyright 1994.

• a ​ ccumulation (first stage –​I): a re-​concentrated 1D chromatography band is formed at the head of the modulator, this being at a much lower temperature with respect to the GC oven; • ​re-​injection (first stage –​II): an electrical pulse of brief duration (e.g., 20 ms) is directed to the first segment of the modulator, enabling the heat-​ induced re-​mobilization of the 1D chromatography band; • ​accumulation (second stage –​I): the re-​mobilized band reaches the downstream “cold” part of the modulator and is compressed in space a second time; meanwhile, analytes begins to accumulate again at the modulator head, which has rapidly cooled down; • ​re-​injection (second stage –​II): a second electrical pulse, shortly after the first (e.g., 100 ms), is this time directed to the downstream modulator segment, re-​injecting the narrow analyte band onto the 2D column. The initial modulator model, which belongs to the rather vast group of thermal modulators, was quite fragile, with burnouts occurring frequently. Furthermore, the electrical connections and metal paint application were delicate issues, them being performed in a research laboratory. The TDM was never produced commercially. The history of cryogenic modulation (currently, the most popular modulators) began in 1998, with the longitudinally modulated cryogenic system (LMCS).8 The LCMS consists essentially of a moving trap, cooled by an internal flow of carbon dioxide, and positioned either at the end of the first column,

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Figure 7.5 Scheme of the LMCS. Reproduced from Ref. 8 with permission from John Wiley and Sons, Copyright © 1998 WILEY-​VCH Verlag GmbH, Weinheim, Fed. Rep. of Germany.

or at the head of the second one. The accumulation process occurs when the LCMS is immobile (position T in Figure 7.5); at the end of the entrapment step, the trap moves along the column (to position R), exposes the previously cooled region to the heat of the GC oven, thus enabling the re-​injection of the re-​concentrated analyte band onto the second column. The LMCS is an effective modulator and is still used (although not widely); however, the cryogenic approach was also characterized by some disadvantages, namely the costs related to the consumption of CO2, the wear-​and-​tear of movement itself, and the lack of additional heating for the re-​injection process. The latter factor is important, in particular, for the re-​mobilization of higher molecular-​weight compounds. The first static cryogenic modulator was described in 2000,9 was named the quad-​jet modulator, because it was composed of two pairs of hot/​cold jets situated at the head of the second column. Each pair of jets heated and cooled the same zone of the column (herein defined as upstream and downstream points). Dual-​stage modulation was performed as follows: first-​stage accumulation was achieved through activation of the upstream cold jet (liquid N2-​based cooling) [the downstream hot jet (air) was activated at the same time]. First-​stage re-​injection, and then second-​stage accumulation, were carried out through activation of the upstream hot and downstream cold jets, respectively. Second-​stage re-​injection, and a new accumulation process (first stage), were performed by the activation of the downstream hot jet and the

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upstream cold jet, respectively. Compared to the LMCS, the quad-​jet modulator was characterized by a greater entrapment and re-​injection efficiency, due to the lower temperatures reached by liquid N2 and to the presence of the heating jets, respectively. Furthermore, there were no moving parts inside the GC oven. However, the necessity of high amounts of liquid N2 was a disadvantage. The quad-​jet modulator is one of the most popular and efficient transfer devices, with the currently availability of consumable-​free refrigeration units capable of reaching a temperature of −90°C. At this point, a brief discussion on single-​and dual-​stage cryogenic modulation is necessary. As discussed previously, dual-​stage devices are characterized by two accumulation and two re-​injection steps. At the end of first-​stage accumulation, a heating step (first-​stage re-​injection) re-​mobilizes the entrapped compounds. However, and because elution from the first column is continuous, a certain amount of non-​compressed (breakthrough) analytes will follow the main chromatography band, with both being re-​concentrated during second-​stage accumulation. The latter process does not occur in single-​stage systems, and so breakthrough analytes will reach the second column and will be detected as a tail (with various forms) after the modulated peak. The loop-​type modulator is a further popular dual-​stage cryogenic transfer device, and was proposed by Ledford et al. shortly after the quad-​jet system.10 The loop-​type modulator functions in a similar manner to the quad-​jet system, with only two jets. The two stages were created by looping a segment (e.g., 1 m) of capillary column, defined as delay loop, through the pathway of a continuous stream of cold gas (Figure 7.6). The latter is attained by flowing N2 gas within a metal coil, situated in a small Dewar containing liquid N2. The cooling gas is directed vertically downwards onto the delay loop, hence creating two cold spots (the upstream and downstream points); consequently, first-​ and second-​stage accumulations are performed simultaneously. At the end of the entrapment step, the cooling gas is diverted by the heating gas (usually heated air or nitrogen) for a brief period (e.g., 250–​300 ms) and in a periodic manner. The hot jet is located orthogonally with respect to the cold jet; again, both stages of re-​injection are carried out at the same time. As for the quad-​jet, Loop Second column

First column

Hot jet Cold jet Figure 7.6 Scheme of the cryogenic loop-​type modulator. The cooling gas is directed downwards, while the heating gas is directed toward the reader. Reproduced from P. Q. Tranchida, G. Purcaro, P. Dugo and L. Mondello, TrAC, Trends Anal. Chem., 2011, 30, 1437 with permission from Elsevier, Copyright 2011.

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effective consumable-​free refrigeration units are now available for loop-​type modulation. Cryogenic-​modulation method optimization is a delicate issue, with the main parameters requiring attention being the accumulation/​re-​injection periods and cooling/​ heating temperatures. The quad-​ jet and loop-​ type devices can be considered the most efficient forms of modulation available today. However, the economical requirements for purchase and operation are rather high and, for such a reason, several alternative modulation routes have been explored. Among such approaches, the greatest attention in terms of research has been devoted to flow modulation (FM). All FM methodologies are based on the manipulation of the gas pressure/​flow between the first and second columns, to enable GC×GC analyses. Several FM models have been proposed, with the most interesting devices (in the present authors’ opinion) stemming from research published in 2006.11 Seeley et al. proposed a single-​stage FM system constructed by using two microvolume T-​unions (upstream and downstream), three fused-​silica capillaries and a three-​port solenoid valve situated outside the GC oven, connected to an auxiliary pressure source (Figure 7.7). The upstream and downstream T-​ unions were connected to one another by a fused-​silica column, acting as accumulation (or sample) loop. Additionally, both T-​unions were connected to the output ports of the solenoid valve each via a fused-​silica capillary. Finally, the 1 D outlet and 2D inlet were linked to the upstream and downstream T-​unions, respectively. During the accumulation step, the auxiliary pressure is directed to the downstream union, and the 1D effluent flows into the loop; before the 1D effluent reaches the outlet of the sample loop, the solenoid valve is activated (e.g., after 1.4 s), the auxiliary pressure is directed to the upstream union for a

Figure 7.7 Scheme of the flow modulator, similar to that proposed by Seeley et al.11

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brief period (e.g., for 100 ms) and the loop content is re-​injected onto the second column by using a high gas flow (e.g., 20 mL min–​1). Stop-​flow conditions arise in the 1D during the re-​injection step. Obviously, the economical requirements for construction and operation were low. The main disadvantage of such an FM approach, and in general of flow modulation, is the generation of excessively high 2D gas flows to produce efficient modulation. An example of how to reduce FM gas flows, described in a food application, will be given later.

7.2.2 Column Optimization Aspects The enhancement in specificity, due to the use of two different stationary phases, is one of the great strengths of comprehensive 2D GC. The selection of the most appropriate stationary phases is nearly always a case of trial-​and-​ error testing. As mentioned previously, the most common choice consists in the use of a low-​polarity + medium-​polarity column combination; such a setup is presumed to be the most orthogonal: packets of overlapping isovolatile compounds (the 1D separation is basically a boiling-​point one) are separated in the second dimension only on the basis of polarity. However, and as will be seen in food analysis, in some cases non-​orthogonal options can give a better outcome. It must be emphasized that, whatever stationary phases are used, there is always a certain degree of correlation between the two dimensions, because compound elution in GC is always tightly related to vapor pressures. For such a reason, it is very rare to observe analytes with a low tR1 value, and a high tR2 one, and vice versa. Consequently, GC×GC peaks are usually contained within diagonal elution bands crossing the 2D plane from the lower left-​hand side to the upper right-​hand one. The two columns can be located either in a single GC oven, or in two, with the latter option being by far the most desirable. In fact, in single-​oven applications the maximum operational temperature is related to the less thermally stable column. Furthermore, the 2D analysis temperature is often not ideal, being dependent on the 1D elution temperature. If lower 2D analyses temperatures are required then the best choice is to slow down the temperature program gradient: analytes will elute from the 1D at lower temperatures, and undergo more intense interactions with the stationary phase (the extent of wrap-​around must also be considered). The use of a slower temperature gradient will generate broader peaks in the first dimension, and so the extension of the modulation period should also be reconsidered. Sensitivity will be reduced if the number of modulations per peak exceeds four (over-​sampling). If, on the contrary, a higher 2D analysis temperature is required, then the best choice is to steepen the temperature program gradient. However, the use of a faster temperature gradient will decrease resolution and generate narrower peaks in the first dimension. Again, attention should be devoted to the modulation period, because resolution attained on the first column will degrade greatly with less than three modulations per peak (under-​sampling). Such optimization problems, related to the housing of both columns in the same GC oven, can be resolved simply by using two independent GC ovens.

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Apart from the selection of the most appropriate stationary phases, operated under satisfactory temperature conditions, one must also consider the physical dimensions of the columns during method optimization. The direct consequence of using two capillaries with different IDs (as often occurs in the GC×GC field) is that, under specific conditions of gas flow, there will be a mismatch between the average linear velocity values. The use of a 1D 30 m × 0.25 mm ID × 0.25 μm df column, and a 1 m × 0.10 mm ID × 0.10 μm df 2D one, is a popular choice. If one uses such columns in a GC×GC-​FID experiment and applies a relative inlet pressure of 125 kPa (H2) at a temperature of 50°C, then a gas flow of approximately 1.6 mL min–​ 1 will be generated, corresponding to average gas velocities of about 30 and 265 cm s–​1 in the first and second dimensions, respectively. Such separation conditions are close to ideal for the 0.25-​mm ID column and far from ideal for the 0.1-​mm ID column (the gas velocity is too high). The GC×GC “gas velocity compromise” becomes less evident when the diameters of the two columns are closer: for example, considering a 30 m × 0.25 mm ID + 1 m × 0.18 mm ID GC×GC-​FID column set and a hydrogen gas flow of 1.6 mL min–​1 at a temperature of 50°C, then average gas velocities of circa 40 and 110 cm s–​1 will be generated in the first and second dimensions, respectively. Such gas flow conditions can be considered close to ideal in both analytical dimensions.

7.2.3 Detection The combination of analyte re-​concentration, a short segment of OTC column and high gas velocity will generate very narrow chromatography bands, both in space and time. In general, GC×GC peak widths at the base are within the range 100–​600 ms, this being a parameter dependent on the analytical conditions. For example, a long 2D column, intense analyte-​stationary phase interactions in the second dimension and a low 2D gas velocity, will all contribute to wider peaks. In any case, GC×GC peak elution is normally very rapid, and peak volumes small (e.g., a peak with a 100-​ms width, at a flow of 1 mL min–​1 will occupy a volume of under 2 μL), requiring detection systems with high sampling frequencies (minimum 50 Hz), limited internal volumes and a rapid rise time. Such features are obviously necessary to reduce the effects of extra-​column band broadening and to enable reliable peak reconstruction. Although several opinions exist, it is generally agreed that 10 data points per peak are sufficient for such a purpose.12 As reported previously, a flame ionization detector was used as detection system in the first published GC×GC research3 and was operated at a sampling frequency of 100 Hz. The main drawback of using an FID, which is an otherwise perfect GC×GC detector, is obviously the lack of structural information. The use of mass spectrometry (MS), as GC×GC detector, was first reported in 1999;13 the authors used a single quadrupole mass spectrometry (QMS) system with a spectral production capability of 2.43 scans s–​1, which was far too slow for GC×GC requirements. To circumvent such a

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technological problem, the authors applied a slow temperature gradient (0.5°C s–​1), to enable low 2D analyses temperatures, with the scope of generating wider peaks (approximately 1 s). Under such conditions only 2–​3 spectra per peak were attained, while the total GC×GC-​QMS analysis time reported was over 7 h! The authors, well-​aware of the shortcomings of the QMS at that time, as well as with good hindsight, affirmed that “an ideal solution would be to use a time-​of-​flight MS that can produce hundreds of full-​scans per second.” More detailed descriptions of the detectors used (both MS and non-​MS) in the field of GC×GC are reported in the application sections.

7.3 Applications in the Field of Food Analysis The first report of a GC×GC food application appeared rather late with respect to the invention of the 2D technology, namely in 2000:4 Dimandja et al. subjected samples of spearmint and peppermint essential oils to GC×GC-​FID analysis, recognizing its superior resolving power compared to GC-​MS. In that first experiment, and to the present day, the outstanding advantage of using GC×GC in untargeted applications has remained evident. Furthermore, the capability of the 2D technology to generate true food sample fingerprints has a demonstrated high utility, as will be seen. On the other hand, in targeted applications it is the high specificity of mass spectrometry which still plays a dominant role. For convenience, and on the basis of a previously defined classification, food samples can be located into four groups:14 samples composed of up to 50 volatiles (e.g., olive oil fatty acids) can be considered as low-​complexity (S1); a mixture containing 50–​200 food volatiles (e.g., lemon essential oil) can be classified as medium-​complexity (S2); a high-​complexity sample (S3) is one formed of 200–​400 compounds (e.g., red wine headspace); finally, a mixture of very-​high complexity (S4) is characterized by the presence of over 400 constituents (e.g., roasted coffee headspace). The use of a single GC column for the untargeted analysis of high-​and very-​high complexity samples is inadequate. A differentiation has been made between MS and non-​MS detection due to the continuously increasing importance of mass spectrometry in all fields of research. Within the GC×GC-​MS food applications, a further distinction will be made on the basis of the MS system used.

7.3.1 Mass Spectrometry Detection 7.3.1.1 Low-​resolution Time-​of-​flight Mass Spectrometry Low-​resolution time-​of-​flight mass spectrometry (LR ToFMS) is the most common form of MS used in the GC×GC field, followed at quite a distance by QMS. In fact, the intrinsic characteristics of LR ToFMS (as reported in Chapter 5) match well with the fast-​eluting nature of GC×GC peaks.

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7.3.1.1.1  Untargeted Applications  Comprehensive 2D GC-​LR ToFMS was used by Risticevic et al. for the profiling of volatiles released from apples (S4), with these being captured through solid-​phase microextraction (SPME).15 Of high interest was the exploitation of the GC×GC-​LR ToFMS results to finely optimize the headspace (HS) SPME process. A 100-​g sample of apple tissue was added to 250 mL of a saturated NaCl water solution and then subjected to homogenization. The salt solution was used to quench the apple metabolism, interrupt enzymatic activity and to enhance SPME enrichment factors. Then, an additional 250-​mL volume of water was added to the mixture and subjected to further homogenization. Headspace SPME was carried out using incubation and extraction periods of 15 and 60 min at a temperature of 30°C. The performances of seven fibers were evaluated, specifically PDMS (polydimethylsiloxane), carbopack Z/​PDMS, CW (carbowax), PA (polyacrylate), PDMS/​DVB (divinylbenzene), DVB/​CAR (carboxen)/​PDMS and CAR/​PDMS, leading to the detection [minimum signal-​to-​noise ratio (s/​n) and database match: 50 and 750] of 549, 745, 897, 977, 1 053, 1 163 and 1 167 metabolites, respectively. The elimination of MS database matches lower than 800 enabled the simplification of the peak tables, and increased the reliability of peak identity. It was found that the DVB/​CAR/​PDMS fiber gave the most satisfactory analyte coverage, with a total number of 830 detected apple metabolites. Considering such a high number of analytes and the untargeted nature of the application, the need for GC×GC-​LR ToFMS was clear, as can also be derived from the crowded peak apex plots shown in Figure 7.8. It is noteworthy that the results reported in Figure 7.8 do not represent the “real” apple aroma profile, but instead the different selectivities of the fibers subjected to evaluation in relation to the volatiles present in the apple headspace. Detailed knowledge on the aromatic apple profile is certainly important, although probably less than that of one of the most popular alcoholic beverages in the world, namely wine. The final wine aroma is an economically crucial factor and is the result of a complex interplay of factors, such as the grape variety, the fermentation/​ageing process, storage conditions, as well as possible deterioration stages. In total, almost 1 000 volatile compounds have been associated with wine, in its wide variety of forms, with such samples generally belonging to the S3 category.16 Comprehensive 2D GC-​LR ToF MS, preceded by solid-​phase extraction (SPE), was used by Weldegergis et al. to investigate the aromatic profile of young South African wines (two Pinotage and a Cabernet Sauvignon).17 Overall, 276 compounds were identified (at various levels of reliability) in the three wines, many for the first time, through MS-​database matching, first-​dimension linear retention indices (LRIs) and standard compounds (when available). It is must be noted that, to date, there is no well-​established approach to calculate retention indices in both the first and second GC dimensions, even though interesting attempts have recently been made.18 The high complexity of the Cabernet Sauvignon sample can be observed in Figure  7.9A–​C; in particular, the need for a second separation dimension

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Figure 7.8 Peak apex plots constructed by using HS SPME-​GC×GC-​LR ToFMS data relative to the analysis of apple metabolites. The graphs refer to the use of the following fiber coatings: PDMS (A), PA (B), CW (C), DVB/​CAR/​PDMS (D), CAR/​PDMS (E), PDMS/​DVB (F) and carbopack Z/​PDMS (G). Graph H illustrates the comparison between fiber coatings. Reproduced from Ref. 15 with permission from Elsevier, Copyright 2012.

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Figure 7.9 Comprehensive 2D GC-​LR ToFMS chromatogram relative to a sample of Cabernet Sauvignon wine (A), along with two expansions (B,C). For peak identity, refer to Ref. 17. Reproduced from Ref. 17 with permission from Elsevier, Copyright 2011.

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separations is evident in the two expansions (B,C). Along with the separation/​detection step, attention was focused on the importance of the sample-​ preparation process. In particular, it was found that reversed-​phase (RP) SPE efficiently removed major polar volatiles (e.g., alcohols, esters, acids), which caused severe overloading when using HS SPME in a previous GC×GC-​ LR ToFMS research.19 Moreover, SPE was found to be more selective with respect to the apolar and semi-​volatile compounds, while HS SPME provided a better coverage of the more volatile compounds. In short, the two sample-​ preparation approaches were found to be complementary. It is noteworthy that HS SPME is much more environmentally friendly compared to RP SPE, while neither of the two approaches enabled the elucidation of the “real” wine aroma. With regard to the LR ToFMS operational conditions, a mass range of m/​z 35–​350 and a spectral production frequency of 100 Hz were applied; as mentioned before, a minimum of 10 data points per peak are required to attain reliable quantification information. However, an efficient deconvolution process must also be considered with at least 30 data points per peak required, as well as a minimum degree of peak-​to-​peak resolution. A curious and noteworthy SPME-​GC×GC-​LR ToFMS study involving the aroma of in-​oven roast beef was described by Rochat et al.20 Following deconvolution, the authors reported the presence of 15 000 peak apexes, and the tentative identification of an astonishingly high number of volatiles, namely 4 700, in a severly overloaded chromatogram. To the best of the authors’ present knowledge, no other GC×GC study on a food product has reported a higher number of compounds. It is presumable, however, that the peak integration method could have made an over-​estimation on the number of aroma constituents. The in-​depth investigation of such data complexity is highly challenging. Rochat et al. reduced the size of the data set by focusing the study on S-​containing compounds, reporting the identity of 50 analytes. Three untargeted GC×GC-​LR ToFMS investigations on food aroma (apple, wine, roasted beef) have been described, demonstrating the outstanding separation power of GC×GC. It is worthy of note, however, that a detailed investigation of a food aroma, via GC analysis, will require, among other detectors, the human nose. However, hyper-​fast 2D separations, involving the rapid elution of compounds in sequence, do not match well with olfactometry (O) detection. In such a respect, a technique such as heart-​cutting MDGC-​O is certainly more suitable, and has been widely used for the investigation of food aroma (see Chapters 6 and 9). 7.3.1.1.2  Targeted Applications  As mentioned previously (in Chapter 5), phytosanitary compounds applied to tea plants can end up in the infusions; within such an applicational field, Schurek et al. used HS SPME-​GC×GC-​LR ToFMS for the pre-​targeted determination of 36 pesticides in a variety of teas (black, green, fruit).21 A 100-​μm PDMS fiber was exposed to the headspace of an infusion (2 mL of water and 2 g of tea); limits of quantification

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(LoQs) were in the range 1–​28 μg kg and, as expected, were always lower compared to an HS SPME-​GC-​LR ToFMS process. The superior analytical performance of the 2D GC approach is evident in Figure  7.10a–​e: a case of partial co-​elution, between parathion (I) and chlorpyriphos (II), spiked at the 50 ppb level, can be observed in the GC-​LR ToFMS extracted-​ion traces highlighted in (a); parathion was characterized by an s/​n value of 20, and an MS database similarity of 678 for its deconvoluted spectrum [in (c)]. The second column succeeded in the complete resolution of the two compounds (b). Furthermore, the s/​n value for parathion was enhanced by just over four times, while the spectral similarity reached 893 [(e) experimental spectrum; (d) MS database spectrum]. Apart from GC×GC-​LR ToFMS and GC-​LR ToFMS, Schurek et al. also compared HS SPME with a traditional sample preparation process, involving extraction with ethyl acetate and gel-​permeation chromatography. The latter approach provided a better performance in terms of linearity and repeatability, whereas sensitivity and reduction of matrix interferences were found to be the strong points of HS SPME. The majority of published GC×GC-​MS work lacks an objective evaluation of the approach against other available GC-​MS technologies. An exception can be found in research performed by Focant et al., who used GC×GC-​LR ToFMS for the analysis of seven 2,3,7,8-​substituted polychlorinated dibenzo-​ p-​dioxins (PCDDs) and ten 2,3,7,8-​substituted polychlorinated dibenzofurans (PCDFs), as well as four mono-​ortho-​and eight non-​ortho-​substituted (dioxin-​ like) polychlorinated biphenyls (PCBs), in milk, pork and fish.22 The GC×GC-​ LR ToFMS approach was compared to a reference GC-​HRMS method and to a GC-​MSMS one. The analysis of PCDDs + PCDFs + PCBs in foods is challenging because numerous compounds must be determined at very low concentration levels in the lipid fraction of food products. It is obvious that within such an analytical context, both the sample preparation and separation-​science processes hold equal importance. Focant et al. used pressurized liquid extraction for the isolation of lipids (and contaminants) from fish and pork, while liquid–​liquid extraction was used for milk. Further clean-​up was carried out through an automated multicolumn system. A magnetic sector MS system was used in the reference method, and was operated in the selected-​ion-​monitoring (SIM) mode, at a mass resolution of 10 000. The MSMS analyses were performed by using an ion trap mass spectrometer. A low-​polarity 50 m × 0.20 mm ID × 0.33 μm df capillary column was used in both GC-​MS applications. With regard to the GC×GC-​LR ToFMS method, a column set with a high thermal stability was used: a (high-​resolution) 40 m × 0.18 mm ID × 0.10 μm df carborane-​based dimethyl polysiloxane was used in the 1D, and a mid-​polarity (50% diphenyl) 1.5 m × 0.10 mm ID × 0.10 μm df one in the 2D. The use of a high-​resolution 1D column, enabling a fine boiling-​point separation, can be highly beneficial in applications involving several compounds with similar chemical structures. If 1D co-​elutions occur between such constituents, then these will most probably remain unresolved on the second analytical column (i.e., linear and branched hydrocarbons). With regard to the latter, a length of

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Figure 7.10 (a) Extracted-​ion traces relative to the HS SPME-​GC-​LR ToFMS analysis of parathion (I) and chloropyriphos (II) spiked at the 50 ppb level in tea; (b) summed extracted-​ion traces relative to the HS SPME-​GC×GC-​LR ToFMS analysis of the same contaminants; (c) GC-​LR ToFMS spectrum for parathion; (d) database spectrum for parathion; (e) GC×GC-​ LR ToFMS spectrum for parathion. Reproduced from Ref. 21 with permission from Elsevier, Copyright 2008.

1.5 m is an advisable choice in GC×GC-​MS applications because a substantial part of the column is located within a heated transfer line. The drawbacks of the GC×GC-​LR ToFMS method, compared to GC-​HRMS, were a lower sensitivity [at the pg g–​1 (fresh weight) level], and an increased time required for data processing. If the publication year of the article is considered (2005), advances in software and computer technology have made such an analytical step much faster. Obviously, the inferior specificity of LR ToFMS often represents a hindrance in reaching very low quantification limits. The authors also affirmed that the ToFMS instrument was less prone to contamination with respect to the ion trap, and thus more suitable to be used on a routine basis. To conclude, the analytical results demonstrated that GC×GC-​LR ToFMS is a highly flexible approach, it being suitable for trace analysis in food products, beside its well-​known and unsurpassed capability in untargeted applications. It is noteworthy that, since 2014, European regulations have included

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GC-​triple quadrupole (QqQ) MS as a tool to confirm the presence of PCDDs, PCDFs, and dioxin-​like PCBs in food.23

7.3.1.2 Single Quadrupole Mass Spectrometry Single quadrupole mass spectrometers are the most common devices used in the GC-​MS field. The reasons for such popularity can be related to the relatively low cost, limited maintenance requirements and to the ability to perform well in both untargeted (scan) and pre-​targeted [SIM; extracted-​ion-​ chromatograms (EICs)] experiments. As mentioned previously, however, QMS systems are the second most common form of mass spectrometry used in the GC×GC field, with such a situation depending on a technological feature. In fact, single quadrupole mass spectrometers are scanning devices, with the capability to monitor m/​z values one at a time, across an applied mass range. As a consequence, sensitivity (duty cycle < 1%) and maximum spectral generation frequencies are much lower compared to ToF analyzers. Even so, as will be seen, QMS technology has evolved greatly over recent years, to the extent that such instrumentation can now be used for analyte quantification in GC×GC analyses. In 2005, Adahchour et al. evaluated a rapid-​scanning QMS instrument, within the context of comprehensive 2D GC analyses.24 The single quadrupole mass spectrometer was the result of significant technological advances, it being capable of monitoring up to 10 000 amu s–​1 and of generating up to 50 spectra s–​1, albeit using a narrow mass range (95 amu). Under such conditions, a single scanning time of 9.6 ms was reported, with a further 10.4 s required to reset the quadrupole to the initial scanning conditions (total scan time of 20 ms). The authors calculated relative standard deviation (%RSD) values for peak areas (n = 6) using spectral generation frequencies of 25, 33 and 50 Hz. It was found that %RSDs were equal or lower than 5% when peak reconstruction occurred with at least seven data points per peak (above the baseline). Mass spectral skewing occurs in QMS instruments because the intra-​ion-​ source concentration of an analyte changes during a single scanning process. Skewing is enhanced, in particular, when the scanning process is too slow compared to the velocity of analyte elution. The main consequence of MS skewing is the generation of inconsistent mass spectra ion profiles. Such an event does not occur in ToFMS systems because packets of ions, comprised in the applied mass range, are pulsed sequentially into the flight tube. Adahchour et al. evaluated mass spectral skewing by measuring ion ratios at each data point across three peaks (linalool, 3-​octen-​2-​one, coumarine), at spectral generation frequencies of 33 and 20 Hz. It was found that ion ratio variations were satisfactory only at the higher frequency (%RSDs ≤ 6%). 7.3.1.2.1  Untargeted Applications  The same type of mass spectrometer used by Adahchour et al.24 was exploited by Ryan et al. for the development

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of an LMCS-​modulated GC×GC-​QMS method for the qualitative analysis of a sample of very high complexity, namely roasted coffee headspace. Coffee is characterized by an enormous economical importance, being, together with tea, the most diffused beverage in the world. The two types of coffee subjected to industrial processing are Arabica and Robusta, with the former being both more consumer-​appreciated and expensive. Commercial roasted coffee is available as 100% Arabica or Robusta, or as a blend. The roasting of green coffee beans, necessary to trigger the Maillard reaction, leads to the formation of a very high number of volatile compounds.26 The acquisition of in-​depth knowledge on the qualitative and quantitative composition of such constituents, as well as on their aromatic qualities and potencies, is fundamental to understanding and possibly tuning coffee aroma. Such a scope can be achieved only through the use of different GC technologies, combined with different detection systems, such as the FID, MS and olfactometry. Considering the complexity of roasted coffee aroma, the choice of GC×GC-​QMS made by Ryan et al. appears to be fully justified.25 Sample preparation was carried out using HS SPME, while separation of the extracted volatiles was performed on a mid-​polarity (100% polyethylene glycol) + low-​polarity (“5% diphenyl”) set of columns. Such a combination of stationary phases was found to provide a better outcome compared to an orthogonal set-​up. The QMS instrument generated 20 spectra s–​1, across a 40–​400 m/​z mass range, being sufficient only for the purpose of analyte identification. The total ion current (TIC) 2D chromatogram, relative to a sample of Arabica coffee, is illustrated in Figure 7.11. As can be readily seen, there is still a considerable amount of empty separation space, even though the number of detected compounds was indeed very high (presumably > 1 000). The majority of compounds are located within the band of analytes crossing the chromatogram from left to right, with a gradual upward slant. On the basis of the appearance of the chromatogram, it would seem that the sample is still far from being fully resolved. Even though not immediately evident, there was a group-​type pattern present in Figure 7.11 involving pyrazines. More specifically, it was found that 14 pyrazines, which were tentatively identified through MS database matching (with the support of LRI values), eluted together along horizontal bands on the basis of the carbon number (C0–​C4) of the substituent (Figure 7.12). As mentioned previously, the formation of highly organized chemical-​class patterns is a significant advantage of GC×GC over other GC technologies. A clear demonstration of how GC technological evolution has enabled increasing knowledge on food composition can be attained by consulting three research works (one characterized by the use of GC×GC-​QMS), among many others.27–​29 All such investigations were focused on the elucidation of the volatile profile of bergamot essential oil, one of the most valued citrus oils. Among several uses, bergamot essential oil is used as a food and beverage ingredient (carbonated drinks, cakes, etc.), and is formed of a volatile and non-​volatile fraction, with the former reaching a 93–​96% level. The volatile fraction is composed of monoterpene (43–​65%) and sesquiterpene (< 2%) hydrocarbons, and oxygenated derivatives (alcohols, aldehydes, ketones,

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Figure 7.11 Comprehensive 2D GC-​QMS chromatogram relative to a sample of Arabica coffee. Reproduced from L. Mondello, P. Q. Tranchida, P. Dugo and G. Dugo, Mass Spectrom. Rev., 2008, 27, 101, with permission from John Wiley and Sons, Copyright © 2008 Wiley Periodicals, Inc.

esters, etc.), along with minor-​quantity compounds, such as aliphatic ketones, aldehydes, esters and alcohols.30 Toward the end of the 1950s, Liberti showed a packed-​column GC chromatogram (at a congress on essential oils), relative to the analysis of a bergamot essential oil, and characterized by the presence of six numbered peaks.27 Liberti was the first to subject an essential oil to GC analysis, soon after the invention of gas chromatography itself; obviously, it is no surprise that the packed column by him used was characterized by a far too low peak capacity to unravel the volatile fraction of bergamot oil. The introduction of the OTC column in the GC field enabled a considerable increase in the space available for separation. Mondello et al. used an apolar OTC column (30 m × 0.25 mm ID) for the GC-​FID separation of bergamot essential oil, and illustrated a chromatogram reporting the separation of approximately 90 peaks in 47 min.28 In 2013, Tranchida et al. performed a normal-​phase high-​performance liquid chromatography pre-​separation of a bergamot essential oil sample into two chemical classes, namely hydrocarbons and oxygenated constituents.29 Each fraction was concentrated and then subjected to GC×GC-​QMS analysis. A total of 195 compounds, at various levels of reliability, were identified, comprising 142 oxygenated compounds and 53 hydrocarbons. Many more detected compounds remained unidentified. It is noteworthy that the QMS instrument used by Tranchida et al. was capable of generating 50 spectra s–​1 under relatively normal mass range conditions (m/​z 40–​330) for GC analyses.

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Figure 7.12 Comprehensive 2D GC-​QMS chromatogram expansion relative to a sample of Arabica coffee, highlighting the pyrazine zone. Peak assignment: (1) pyrazine, (2) 2-​methylpyrazine, (3) 2,5-​dimethylpyrazine, (4) 2,6-​dimethylpyrazine, (5) 2-​ethylpyrazine, (6) 2,3-​dimethylpyrazine, (7)  2-​ethyl-​6-​methylpyrazine, (8)  2-​ethyl-​5-​methylpyrazine, (9)  2,3,5-​trimethylpyrazine, (10) 2-​ethyl-​3-​methylpyrazine, (11) 2,6-​diethylpyrazine, (12) 2-​ethyl-​3,5-​dimethylpyrazine, (13) 2,3-​ diethylpyrazine, (14) 2-​ethyl-​3,6-​dimethylpyrazine. Reproduced from L. Mondello, P. Q. Tranchida, P. Dugo and G. Dugo, Mass Spectrom. Rev., 2008, 27, 101, with permission from John Wiley and Sons, Copyright © 2008 Wiley Periodicals, Inc.

The flow-​modulation approach introduced by Seeley et al. in 200611 was later (2011) followed by Tranchida et al. in the design of a compact seven-​port wafer chip, with internal channels and an external sample loop.31 In an initial FM GC×GC-​QMS research involving the analysis of fish oil and plasma fatty acid methyl esters (FAMEs), a 2D gas flow well-​in-​excess of 20 mL min–​1 was generated, with approximately 80% of the effluent directed to a waste line.32 Such a choice was made to enable both an efficient modulation process and to avoid the arrival of such a high gas flow to the ion source. The main price to pay, under such circumstances, was obviously reduced sensitivity. Such an issue was faced in a further comparative study: it was found that analyte s/​n values were on average 3–​4 times higher in a single-​column analysis.33 The authors concluded that, with such a disadvantage, the use of flow modulation in MS-​based GC×GC applications made no real sense. The “high-​flow” problem was resolved through fine-​tuning of the FM process;34 more specifically, and very simply, a re-​injection period of 100 ms, using a gas flow of 24 mL min–​1, is equivalent to a re-​injection period of 400 ms, using a gas flow of 6 mL min–​1 (see Section 7.2.1). At the same time, however, the stage of re-​injection must not be too long, otherwise the stop-​flow

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condition in the first dimension will cease. The direct consequence of such an occurrence will be that the 1D effluent will start to accumulate in the loop before the end of the stage of re-​injection, leading to unsatisfactory chromatography. Side-​by-​side experiments were performed on a commercial perfume using the “reduced-​flow” GC×GC-​QMS approach and a GC-​QMS approach (the 1D column was connected directly to the MS system). It was found that, for 22 compounds with various polarities, FM GC×GC-​QMS s/​n values were higher on average by a factor of 1.5, which was a considerable improvement with respect to previous research.33 In a later study, the “reduced-​flow” concept was exploited in FM GC×GC-​ QMS applications involving the use of a low-​polarity 2D mega-​bore column (10 m × 0.53 mm ID).35 The instrumental configuration enabled the generation of low-​pressure (LP) conditions across the second dimension. In short, the concept of “vacuum outlet” fast GC (see Chapter 5) was extended to GC×GC (the authors used the term low-​pressure comprehensive two-​dimensional gas chromatography –​GC×LPGC). A restriction with dimensions 1.5 m × 0.25 mm ID bridged the modulator and the 2D column to avoid subatmospheric conditions reaching the modulator. The 1D column was characterized by dimensions 30 m × 0.25 mm ID, and by a 0.25-​μm film of polyethylene glycol stationary phase. The FAMEs, derived from a sample of fish origin (menhaden oil), were subjected to FM GC×GC-​QMS analysis. The method was a constant average linear velocity (ALV) one, with a 1D ALV of about 11 cm s–​1, and a 2D ALV of approximately 180 cm s–​1. The initial 2D gas flow was ≈ 7.5 mL min–​1, being about 50% of the maximum pumping capability of the QMS system used. The TIC 2D chromatogram of the menhaden oil FAMEs is illustrated in Figure 7.13. As can be seen, the fatty acid composition of menhaden oil is rather complex, with high levels of polyunsaturated fatty acids (PUFAs) such as C20:5 (eicosapentaenoic acid –​EPA) and C22:6 (docosaesaenoic acid –​DHA). The medium polarity-​low polarity combination of stationary phases generated an ordered elution pattern: in the 1D column, retention times increased with the number of double bonds, considering FAMEs with the same C-​number. An opposite behavior occurred on the 2D column, causing the saturated FAMEs to elute in the higher parts of the chromatogram (e.g., C16:0, C18:0, etc.), and PUFAs to elute in the lower parts of the chromatogram (e.g., EPA, DHA, etc.). The formation of group-​type patterns, along with the availability of MS data, increases greatly the reliability of identification. Cordero et al. used HS SPME GC×GC-​QMS for the fingerprinting analysis of samples of roasted coffee and hazelnuts.36 The GC fingerprint of a food sample is represented by the specific distribution of compounds, present in the native material or generated through a transformation process; the latter can be induced intentionally (e.g., the Maillard reaction), or occur naturally (e.g., oxidation products). A GC fingerprint is the result of the scattering of sample features, subjected to detection by using a suitable analytical method, and can be exploited for comparative purposes. Fingerprinting, for example, can be used to identify a food product (e.g., on the basis of geographical origin), or to highlight a sensorial quality (e.g., a rancid vegetable oil). The creation of

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Figure 7.13 A TIC FM GC×GC-​QMS 2D chromatogram of menhaden oil FAMEs. Reproduced from Ref. 35 with permission from Elsevier, Copyright 2014.

fingerprint requires the untargeted analysis of a series of samples of the same type; once the sample-​specific fingerprint has been created, it is then used in targeted applications. Comprehensive 2D GC-​MS technologies form highly informative sets of data, containing 1D/​2D retention times, MS responses and spectra, often related to hundreds and sometimes thousands of compounds. Such complexity requires adequate data mining technologies to extract useful information. In such a respect, Cordero et al. used various approaches, with these defined “group-​type characterization”, “direct fingerprint comparison” and “template matching”.36 Group-​type characterization was performed by pinpointing 88 food markers, predefined on the basis of technological, sensory and botanical importance. Such an approach consisted essentially of the second step (targeted) of a fingerprinting process, because the markers were selected a priori. Histograms and bubble plots were constructed using retention times and peak areas of the identified compounds, highlighting intra-​sample (thermal treatment) and inter-​sample (geographical origin) chemical-​class (i.e., pyrazines, furans, etc.) differences. Group-​type characterization can be considered as a simple approach, with no dedicated software requirements.

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On the contrary, a dedicated software was necessary for “direct fingerprint comparison”, a method which can be classified as an image processing technology. More specifically, comprehensive 2D GC raw data can be represented as an a [m, n] matrix, where “a” is the chromatogram, generated by pixels each characterized by a first (m) and second (n) dimension retention time, and a detector response. The software subtracted a sample chromatogram from a reference one, with direct visual differentiation enabled through color (gray) scaling: a darker gray color indicated a negative difference (with respect to the analyzed sample), while the opposite was true for a lighter gray color. Direct fingerprint comparison enabled faster and finer sample differentiation compared to group-​type characterization, without requiring analyte identification. The limits of the image processing technology, commented on by the authors, were related to between-​analysis variations in retention times and peak areas, along with distorted chromatography effects. Dedicated GC×GC software was again used to perform template matching: a “template peak pattern” is created by selecting all, or a fraction of peaks, from a reference chromatogram. Each peak is characterized by a 1D and 2D retention time, and an area/​intensity value. A reference chromatogram, relative to the HS SPME GC×GC-​QMS analysis of roasted Roman hazelnuts, along with its template peak pattern (containing 231 peaks), is shown in Figure 7.14. Following the creation of the template peak pattern, an unknown sample is subjected to HS SPME GC×GC-​QMS analysis (e.g., Piedmontese roasted hazelnut), with the number of matched compounds, along with inter-​sample similarity parameters, listed in a report. The authors affirmed that the main usefulness of template matching could be found in its potential to (1) classify sample fingerprints on the basis of the number of matched peaks, and (2) indicate between-​sample differences through the definition of unmatched peaks. An image processing technology, derived from the principles of two-​ dimensional gel electrophoresis protein analysis, has been exploited for the HS SPME GC×GC-​QMS fingerprinting of fruit volatiles (apples, pears and quince fruit).37 A dedicated software (ImageJ) was used to transform the GC×GC-​QMS data to grayscale images, with the latter imported into a further software (Delta2D) for image processing and data analysis. Specifically, 32 “images” were subjected to alignment, with the scope of creating a “spot consensus pattern”. The latter contained more than 700 “spots”, with each confined by a boundary. The application of the consensus pattern to each GC×GC-​QMS image enabled sample differentiation, through gray-​level integration. A disadvantage of the image processing technology, highlighted by the authors, was that MS information was available only offline. A software tool exploited by Kiefl et al. enabled the inclusion of QMS information, during a fingerprinting process defined “comprehensive template matching” (CTM), with this being applied to cross-​sample hazelnut analysis.38 Initally, data processing parameters were finely selected –​peak s/​n and area thresholds, retention time search windows (three times the standard deviation in each dimension), MS matching threshold (minimum 600) –​by

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Figure 7.14 Reference chromatogram and template peak pattern, derived from the HS SPME-​GC×GC-​QMS analysis of roasted Roman hazelnuts. Reproduced from Ref. 36 with permission from American Chemical Society, Copyright 2008.

focusing on 24 target compounds in nine replicate analyses on standard and self-​roasted hazelnuts. Following the preliminary optimization step, a consensus template was generated by subjecting 23 (roasted and raw) hazelnut (Corylus avellana L.) samples to HS SPME GC×GC-​QMS analysis. The hazelnuts were from various geographical origins and of different varieties and roasting degrees. All the peaks in a randomly selected chromatogram (one of the 23), detected on the basis of the previously defined data-​processing conditions, were included in an empty template. Then, the template was compared with a second chromatogram, to add newly detected peaks and to create an “updated” template; the latter was then matched to a third chromatogram, to further include unmatched peaks to the template, and so on until the end of the data set.

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Automatic alignment of retention times compensated for slight peak shifting. All the peaks present in the consensus template were defined by a number, along with gas chromatography and mass spectrometry information. The consensus template was finally used to extract qualitative and quantitative data on the analytes in all 23 chromatograms, to investigate the presence of potential roasting markers, independent from the variety and the geographical origin of the hazelnuts. In such a respect, 11 roasting markers (2-​methylbutanal, 2,3-​pentandione, 1-​methyl-​pyrrole, pyridine, 3-​hydroxy-​2-​butanone, 2-​ethyl-​ pyrazine, furfural, phenylacetaldehyde, and three unknowns) were pinpointed. For instance, it was found that 2-​methylbutanal, 2,3-​pentandione and phenylacetaldehyde could be used as markers to predict the roasting degree, because they increased in a linear manner with roasting time, with no relationship to variety. In a similar HS SPME GC×GC-​QMS study involving roasted hazelnuts, Cordero et al. created a consensus template containing 422 peaks from nine samples of different geographical origin and variety.39 Each peak in the consensus template is contained in at least two chromatograms across the sample set, and is characterized by an average 1D and 2D retention time, an average mass spectrum and an average match factor. A 2D chromatogram of an Italian (Piedmonte) sample is reported in Figure 7.15, with the matched compounds (196) defined by filled circles; the sample illustrated in Figure 7.15 showed the lowest degree of similarity with the consensus template (46.4%). It is noteworthy that all the volatiles highlighted in the Piedmonte sample were found in the other eight samples, albeit with different normalized peak volumes. As seen in the present chapter, the amount of information generated in GC×GC-​MS experiments is exceedingly high, generating very detailed fingerprints. Software packages have also evolved considerably over recent years, enabling fine across-​food sample differentiation and classification. The data generated can also be exploited for in-​depth statistical analysis (see Chapter 10). For instance, Purcaro et al. used CTM and statistical data treatment after HS SPME GC×GC-​QMS analyses to extrapolate a chemical blueprint of different types of olive oils.40 The chemical blueprint of a food can be considered an aromatic signature, or more specifically a fingerprint composed of those compounds which contribute in a significant manner to the aroma of a food product. The definition of a blueprint is an analytical objective which can be confined within the wider discipline named as sensomics. The term sensomics comprises a group of methodologies focused on the elucidation of sensorially important constituents in a food. In such a respect, Purcaro et al. focused their attention toward volatiles which contribute negatively to the aroma of olive oil. Such oil defects derive from non-​appropriate production behaviors, such as an excessively long olive storage period (should be no longer than 24–​48 h) leading to fermentation, a prolonged oil-​sediment contact within the tanks, and oxidation processes. Normally, the presence (or not) and the intensity of olive oil defects are evaluated by trained panelists, who classify olives in three categories: extra-​virgin olive oil (EVOO –​the most valued), virgin olive oil and lampante oil. The latter, if untreated, cannot be

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Figure 7.15 An HS SPME GC×GC-​QMS chromatogram of a sample of Italian (Piedmont) hazelnuts, with 422 filled + empty circles indicating the peaks contained in the consensus template. The 196 white filled circles relate to the matched peaks. Reproduced from Ref. 39 with permission from Elsevier, Copyright 2010.

commercialized, while the first two types of oil can. Purcaro et al. created a blueprint of olive oil defects, which was exploited to distinguish EVOO from lampante oil with the support of a supervised approach, namely partial least squares-​discriminant analysis (see Chapter 10). 7.3.1.2.2  Targeted Applications  As mentioned, the QMS system reported in ref. 29 was capable of generating 50 spectra s–​1, using a 290-​m/​z wide mass range. The same instrument was used by Purcaro et al. in the development of a GC×GC-​QMS method for the determination of pesticides in drinking water.41 During method optimization the authors kept in mind the maximum residue limits (MRLs) of 0.1 and 0.03 μg L–​1 set by European legislation for the 28 pesticides involved in the research.42 The pesticides were extracted from water by using direct-​immersion (PDMS) SPME. The QMS system was operated across a mass range of m/​z 50–​450, at a spectral production frequency of 33 Hz. The applied MS conditions enabled reliable peak reconstruction, with quantification carried out by using extracted ions. Satisfactory method LoQs were reported, them being in the range 0.003–​0.084  ppb. As described previously, the offline combination of LC and GC×GC-​QMS (LC//​GC×GC-​QMS) has been used for the untargeted analysis of bergamot essential oil.29 In such a case, the selectivity of the LC process was exploited for chemical-​class separation. In other research, LC//​GC×GC-​QMS was used for the analysis of the hexane extracts of meat, fish and fruit baby foods.43 More specifically, and in particular in relation to the meat and fish samples,

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the LC step was exploited for sample preparation: the lipids (also derived from the sunflower oil used to prepare the baby food), mainly composed of triacylglycerols, were retained on the silica stationary phase, leaving the possible hydrocarbon contaminants to elute from the LC column free from interferences. The contamination of foods with mineral oil derivatives (see Chapter 8), in particular mineral oil-​saturated hydrocarbons (MOSH), is rather common. Such a fraction of compounds is composed of linear and branched alkanes along with cyclic ones. Mineral oil aromatic hydrocarbons can also be found in foods, albeit in lower quantities, although with a higher concern in terms of toxicity.44 The baby foods (16 samples) were initially subjected to online LC-​GC-​FID analysis for the scope of quantification. Various degrees of MOSH contamination were found in all samples, from 0.3 to about 14 mg kg–​1. Hence, the fruit-​ based baby foods were also found to be MOSH-​contaminated (corn starch and sugar were affirmed to be the origin). The LC//​GC×GC-​QMS method was used to attain detailed qualitative information on the MOSH fractions isolated from the baby foods. Purcaro et al. reported on the use of GC×GC with parallel QMS and FID detection for the identification and quantification of MOSH and MOAH contaminants in dry foods (sugar, pasta, rice).45 The two hydrocarbon fractions were first isolated offline using SPE on a stationary phase composed of Ag silica. The FID-​derived quantitative data were similar to those derived from LC-​ GC-​FID and large-​volume-​injection (LVI) GC-​FID analyses. As an example, the LVI GC-​FID results (for the MOAH) for samples of pasta before packing, and after being stored for three months in a paperboard container (refer to the inset), are illustrated in Figure 7.16. The characteristics of the packaging, and the storage time duration, lead to the generation of a MOAH hump. The latter was investigated in more detail by exploiting the GC×GC-​QMS data: the intense peaks on the top of the MOAH humps were identified as esterified fatty acids. Such compounds most probably derived from the vegetable oil-​based ink applied to the packaging material. The presence of such interferences in the chromatogram did not affect GC×GC-​FID quantification, because they were simply subtracted from the zone of the 2D chromatogram containing the MOAH (surrounded by a polygon in Figure 7.16). Purcaro et al. reported MOSH and MOAH pasta concentrations (with a chain length shorter than C25) of 3.6 and 1.7 ppm, respectively. In conclusion of the sections devoted to GC×GC-​LR ToFMS and GC×GC-​ QMS, it is the present authors’ opinion that the majority of possible applications in the food-​analysis field (involving compounds amenable to GC analysis) could be covered by using such methodologies. It is obvious that even though such three-​dimensional technologies are very powerful, the use of an adequate sample preparation process is mandatory (see Chapters 1 and 2). In principle, the generation of an increased space for separation could lead to the toleration of a higher number of interferences compared to GC-​MS.

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Figure 7.16 A  GC×GC-​QMS chromatogram, relative to the MOAH fraction in a sample of pasta. The inset: LVI GC-​FID traces relative to the MOAH fraction in the pasta sample, before packing (t0) and after a storage period of three months (t3). Refer to Ref. 45 for peak assignment. Abbreviations: FA, fatty acid; DINP, diisopropyl naphthalenes. Reproduced from Ref. 45 with permission from Elsevier, Copyright 2013.

However, problems related to column and ion source contamination by the matrix must still be considered.

7.3.1.3 High-​resolution Time-​of-flight Mass Spectrometry The combination of GC×GC, with high-​resolution (HR) ToFMS, generates an instrument with an outstanding analytical power. In fact, the number of samples (not only food products) for which the use of GC×GC-​HR ToFMS finds justification is rather limited. High-​ resolution ToFMS provides database-​ searchable full-​ spectrum information, characterized by both high-​mass accuracy and resolution. If the molecular ion is present, and the mass accuracy is high, then a good idea on the molecular formula can be derived. More specifically, the higher the mass accuracy, the lower the number of potential molecular formula candidates corresponding to a specific mass value. With regard to pre-​targeted determinations, the use of EICs with narrow mass windows (e.g., ≤ 5 ppm) enables the reduction or elimination of matrix and chemical noise interferences, enhancing the level of specificity.

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Finally, preserved full-​spectrum data can be investigated at any time for the presence of compounds initially ignored (post-​targeted analysis). In pre-​ targeted applications, the necessity of a GC×GC process, prior to HR ToF MS, is doubtful to the least. The use of GC×GC-​HR ToFMS in the food analysis field has been very scarce. Most of the published GC×GC-​HR ToFMS research has been focused on environmental and petrochemical applications.46 Cao et al. used an HR ToFMS instrument, under fast spectral generation frequency conditions (100 Hz) and with a reported mass resolution of 4 000 (fwhm), for the qualitative profiling of Mentha haplocalyx.47 One hundred and sixty-​three compounds were identified among several hundreds of detected compounds; it is noteworthy that many of the database matches were low, namely less than 800. The chromatograms shown by the authors were characterized by a limited occupation of the two-​dimensional space. Moreover, no information was provided on the levels of mass accuracy observed. It is noteworthy that such an application-​ type could have been undoubtfully performed, in a satisfactory manner, by using nominal mass MS.

7.3.1.4 Triple Quadrupole Mass Spectrometry Triple quadrupole MS is a highly specific and sensitive form of mass spectrometry, often preceded by a GC separation step, most typically in pre-​ targeted applications. Modern-​day QqQ MS systems are formed of two quadrupole mass analyzers (for convenience defined as Q1 and Q3), with a collision cell (usually a hexapole or octapole) positioned between them. Collision-​induced dissociation (CID) reactions can occur in the cell. The most common QqQMS operational mode is multiple reaction monitoring (MRM): Q1 and Q3 are both operated in the SIM mode; usually, one or two Q1-​selected (precursor) ions are subjected to CID (an inert gas, such as Ar, is present in the cell). Usually, two product ions are monitored during the Q3 SIM process, one used as quantifier and the other as qualifier. The MRM mode enables a great reduction (or complete elimination) of signals related to matrix interferences and background noise. Specificity usually increases when heavier precursor ions are selected, compared to lighter ones. Other possible tandem MS (MSMS) modes are: (1) product ion scan (Q1 SIM–​Q3 scan), during which the ions derived from the CID of the precursor ion are monitored; (2) neutral-​loss scan (Q1 scan–​Q3 scan), during which Q1 and Q3 are synchronized in such a manner that the difference in mass of ions passing through the mass analyzers is constant. Such an operational mode is used to monitor compounds in a sample that contain a particular substituent (e.g., Cl); and (3) precursor ion scan (Q1 scan–​Q3 SIM), during which Q3 monitors a single ion, derived from the CID of the ions transmitted by Q1. A precursor ion scan experiment will contain a record of compounds possibly containing a specific functional group (e.g., phenyl).48 It is noteworthy that QqQMS systems can provide classical scan and SIM information.

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A commonly observed GC-​QqQMS food application is that involving the MRM pre-​targeted determination of phytosanitary compounds in vegetable products.49 In such analyses, it is the specificity of the QqQMS process which holds much higher importance with respect to the resolving power of the GC one. As a consequence, it comes of no surprise that the use of GC×GC-​ QqQMS in the food-​analysis field has been limited. The first description of GC×GC-​QqQMS was published in 2008 by Poliak et al. in a flow-​modulation pre-​targeted application.50 Mass spectrometry ionization was performed through supersonic molecular beam (SMB) electron ionization (EI), a methodology defined as “cold EI” because of its ability to produce intense molecular ions with little additional fragmentation. A further advantage of using the SMB interface in that experiment was its compatibility with the high FM gas flows. Chromatogram expansions relative to the GC-​SMB MS, GC×GC-​ SMB MS (untransformed data) and GC×GC-​SMB MSMS (untransformed data) analysis of diazinon (a pesticide) in coriander (the leaves and fruits are used in several food preparations, such as salads, chutney, as a spice, etc.) at the 100 ppb level are reported in Figure 7.17. The GC-​SMB MS result is characterized by co-​elution between the target analyte and matrix interferences, and by the lack of success of the MS database-​matching process. As can be readily observed, the use of FM GC×GC-​SMB MS lead to a great improvement, inasmuch as a database match of nearly 94% was obtained for diazinon. It is worthy of note that the scan applications were performed using a mass range of m/​z 50–​400 and a spectral production frequency of just over 6 Hz. A high spectral generation rate was not necessary because peak widths were rather wide, due to the use of a 2D column of mid-​polarity, with dimensions 2.2 m × 0.32 mm ID, and a thick stationary-​phase film (1 μm). In the FM GC×GC-​SMB MSMS analysis, the transition m/​z 304 → 179 allowed an outstanding increase in specificity and sensitivity. The research described by Poliak et al. was a proof-​of-​principle one, even though it must be noted that no information was given by the authors on a possible GC-​SMB MSMS result. In a later study, Tranchida et al. evaluated an ultimate-​generation QqQMS system, again in FM-​based applications.51 The tandem mass spectrometer was a very fast one with maximum limits of 20 000 amu s–​1 scan speed and 600 MRM transitions s–​1. A unique characteristic of the instrument was its ability to rapidly switch back and forth between scan and MRM operational modes (also scan/​SIM, scan/​SIM/​MRM, etc.), thus producing distinct types of information in a single run. In such a respect, an FM GC×GC-​QqQMS method was developed for both the qualitative analysis of mandarin essential oil volatiles, and the MRM determination of a fungicide (ortho-​phenylphenol –​OPP) and two antioxidants (butylated hydroxyanisole –​BHA; butylated hydroxytoluene –​ BHT). The scan + MRM event time was 0.04 s. A low-​polarity 11.75 m × 0.10 mm ID × 0.10 μm df column was used in the first dimension and a mid-​polarity (ionic liquid –​IL60) 5 m × 0.25 mm ID × 0.20μm df one in the second. Part of the flow exiting the modulator (approximately 72%) was diverted to waste to avoid exceeding the maximum pumping capacity of the mass spectrometer.

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Figure 7.17 Upper chromatogram expansion: GC-​SMB MS analysis of diazinon in coriander at the 100 ppb level; middle chromatogram expansion: the GC×GC-​SMB MS result; lower chromatogram expansion: the GC×GC-​ SMB MSMS result. Reproduced from Ref. 50 with permission from Elsevier, Copyright 2008.

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An FM approach characterized by the use of lower gas flows was developed in a later research.34,35 An example of the flexibility of the scan/​MRM operation mode can be observed in Figure 7.18A, which reports a “raw” chromatogram expansion highlighting the complete co-​elution in both dimensions of farnesene (higher trace), a sesquiterpene hydrocarbon, and BHT [middle trace: quantifier transition (m/​z 205 → 177); lower trace: qualifier transition (m/​z 205 → 145)], spiked at the 20 ppm level. The averaged scan spectrum for farnesene (also containing BHT fragments), recognized with a database similarity of 88%, is shown in Figure 7.18B, while the MRM spectrum for BHT is reported in Figure 7.18C. Apart from mandarin oil, a spearmint essential oil was subjected to FM GC×GC-​QqQMS MRM-​only analysis, after spiking it at the 1 ppb level with five phytosanitary contaminants. The final concentration of the target analytes in the injection solution was 0.1 ppb, because the essential oil was diluted in hexane. To gain sensitivity, the voltage of the MS detector was enhanced by 1.2 kV with respect to the tuning result. Such an increase can be applied because of the highly specific nature of the MRM process. Obviously, possible negative effects on the detector lifetime have to be accounted for. The resulting 2D MRM chromatogram is illustrated in Figure 7.19. As can be readily seen, the MRM process provided high specificity for terbufos, fenchlorphos, fenthion and bupiramate. For the two resmethrin isomers, on the other hand, it was the resolving power of GC×GC which enabled the separation of the target analytes from abundant matrix interferences.

7.3.1.5 Other Forms of Mass Spectrometry Apart from the four forms of mass spectrometry previously discussed, other MS systems have been used in the GC×GC field, albeit in a limited manner. One such example is isotope-​ratio mass spectrometry (IRMS), which is a form of MS which can measure isotopic ratios (e.g., 13C/​12C) with great precision (often used in the food analysis field). Specifically, Tobias et al. hyphenated a dedicated IRMS instrument to a GC×GC instrument, and used it in applications involving urinary steroids.52 Among others, and with the intention of limiting band broadening, the authors used a combustion chamber with a reduced ID (0.20 mm) and Faraday cups with a rapid response (time constant: 30 ms). To the best of the present authors’ knowledge, GC×GC-​IRMS has not been used in the field of food analysis. At this point, it must be emphasized that usually only a specific number of compounds are subjected to attention in GC-​IRMS analyses and, for such a reason, the use of heart-​cutting MDGC-​IRMS has often been described in food research.53 Hybrid mass spectrometry, namely the QToF combination, has been used in the GC×GC field in food-​related analyses: Chin et al. used the four-​ dimensional technology in the analysis of chiral compounds contained in

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Figure 7.18 (A) upper “raw” modulated scan trace: farnesene co-​eluting with BHT; middle “raw” modulated MRM trace: BHT quantifier transition; lower “raw” modulated MRM trace: BHT qualifer transition. (B) Averaged scan spectrum for farnesene. (C) MRM spectrum for BHT. Reproduced from Ref. 51 with permission from Elsevier, Copyright 2013.

cardamom essential oil.54 Cardamom is a seed-​derived spice commonly used in Asian dishes; the essential oil is also extracted from the seeds. A series of health benefits are related to the consumption of cardamom essential oil, with it also used in specific cooking recipes.54,55 It is worthy of note that

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(273 Fenchlorphos (285 93; 285 240)

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Figure 7.19 Flow-​modulation GC×GC-​QqQMS MRM chromatogram after spiking spearmint essential oil at the 1 ppb level with five phytosanitary contaminants. Reproduced from Ref. 51 with permission from Elsevier, Copyright 2013.

essential oils usually contain several enantiomer pairs, with a characteristic percentage distribution. As mentioned in previous chapters (Chapters 3, 5 and 6), such percentage measurements can be exploited to confirm (or not) authenticity, geographical origin, etc. In particular, heart-​cutting MDGC is an approach with a high suitability for enantiomeric GC (enantio-​GC) analysis, often using a non-​chiral + chiral combination of columns (see Chapter 6). Chin et al., on the other hand, used enantio-​GC×GC-​QToFMS to elucidate the distribution of α-​ and β-​pinene as well as limonene. In an initial enantio-​GC-​QToFMS application, overlapping occurred between (+)-​ limonene and two other monoterpenes, cymene and cineole. The availability of accurate-​mass information, under MSMS operational conditions, was of no help in isolating the target analyte from, in particular, cineole. The co-​elution between (+)-​limonene and cineole was resolved using enantio-​ GC×GC with a medium-​polarity ionic liquid (IL59) 2D column. The QToFMS system used was capable of generating approximately 48 spectra s–​1, using the total transfer of ion (TTI) mode (ToF-​only analysis). On the other hand, the number of compounds that could be monitored at the same time under product-​ion-​scan conditions was limited, a factor related to the limited velocity of the QToF MSMS process.

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7.3.2 Other Detectors A variety of different non-​MS detectors have been used in the GC×GC food analysis field, with some contributing rather excessively to extra-​column band broadening. Less space will be devoted to such detectors, due to the fundamental importance of mass spectrometry. The electron capture detector (ECD) is a concentration-​dependent device and is characterized by a high specificity, inasmuch as it responds to compounds with a high electron affinity, i.e., chlorinated organic analytes, at very low on-​column amounts.56 The use of GC-​ECD has often been described in the food analysis field, in particular for the determination of halogenated contaminants (see Chapter 3).57 The use of a micro-​ECD, characterized by a low internal volume (approximately 150 μL) and operated at a 50 Hz acquisition frequency, has been described in a GC×GC application involving a standard mixture of pesticides.58 It was found that band broadening was slightly greater when using the micro-​ECD compared to an FID for seven organophosphorus pesticides: peak widths at half height were in the ranges 0.22–​0.26 s and 0.14–​0.23 s, respectively. With regard to peak symmetry, tailing factors were comparable, namely in the ranges 1.32–​1.52 and 1.09–​1.72 for the micro-​ECD and the FID, respectively. In the same study, the performance of a nitrogen phosphorus detector (NPD) was also subjected to evaluation. The acquisition frequency used was the same as that of the micro-​ECD, while the detector gas flows were finely optimized. The NPD is characterized by a high specificity toward N-​and P-​ containing compounds. Peak tailing occurs commonly when using such a detection system, it being related to various parts, such as the ceramic bead.56 In fact, the NPD tailing factors (for the same seven organophosphorus pesticides) were higher compared to the micro-​ECD and the FID ones, being in the range 2.03–​2.13. It is noteworthy that peak widths were in a range similar to that of the ECD (0.23–​0.30 s). The atomic emission detector (AED) can monitor up to 23 elements and is characterized by a high sensitivity for most of the important elements, an excellent element vs. carbon specificity and a linear response range extending 3–​5 orders of magnitude.59 The performance of a GC×GC-​AED instrument has been evaluated in pesticide and petrochemical analyses.60 The acquisition frequency of the AED was too low (10 Hz) for the requirements of a GC×GC analysis; as a consequence, an increase in band broadening was intentionally induced by connecting a 0.7 m × 0.25 mm ID uncoated column to the outlet of the 0.1 mm ID 2D one. Three pesticide applications were carried out, each focused on a specific group of elements; in fact, simultaneous element measurements can only be made if emission lines fall within a wavelength range (e.g., 20 nm). It is noteworthy that, in general, peak widths were rather wide. The flame photometric detector (FPD) is characterized by a high specificity and sensitivity toward analytes containing P and S. The use of GC×GC-​ FPD is rather recent, it being described for the first time in 2010.61 In that first research, and compared to FID data, peak tailing was observed when

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monitoring S, and not in the P mode. Such behavior was confirmed in a study involving red wine and brewed coffee.62 Apart from mass spectrometry, the FID has been the most popular detector in the GC×GC food-​analysis field. The presence of FID data is useful for the purpose of quantification, especially if MS information is also available.45 However, the formation of organized patterns of chemically related compounds can diminish the need for mass spectrometry. A classical example is that represented by the analyses of FAMEs. For example, Tranchida et al. used a 100% polydimethylsiloxane + polyethylene glycol column combination for the GC×GC-​FID separation of plasma FAMEs.63 The use of such stationary phases generates highly structured FAME patterns, with such analytes: (1) eluting in specific zones of the 2D space on the basis of carbon number (in this case from C8 to C24), (2) aligning along (seven) distinct bands (more or less horizontal) on the basis of the number of double bonds (in this case from 0 to 6), and (3) aligning along descending diagonal bands on the basis of the ω number (i.e., C18:3ω6 and C18:2ω6). Overall, 65 plasma FAMEs were identified, with 29 assigned only through the specific chromatogram position (pure standard compounds were available for the remaining compounds). An interesting and unprecedented GC×GC-​FID method for the analysis of FAMEs was described by Delmonte et al.,64 inasmuch as the same highly polar ionic liquid (SLB-​IL111) stationary phase was used in both analytical dimensions. A capillary tube coated with palladium, located between the first dimension (200 m × 0.25 mm ID × 0.20 μm df) and a cryogenic loop-​type modulator, enabled the reduction of unsaturated FAMEs (H2 was used as carrier gas) to their saturated counterparts. In such a manner, the sample dimensionality was reduced because only saturated FAMEs reached the FID (after eluting from a 2 m × 0.10 mm ID × 0.08 μm df column), with compounds with the same C number (e.g., C18, C20, etc.) being characterized by the same 2 D retention time (applications were performed at an isothermal temperature of 170°C). A 2D chromatogram of menhaden oil FAMEs is shown in Figure 7.20. The differences, in terms of FAME elution order, are evident if reference is made to the GC×GC-​QMS chromatogram reported in Figure 7.13. Although at different levels, the ECD, NPD, AED, FPD and FID can all be considered as information-​poor detectors compared to the mass spectrometer. Ultraviolet detectors, on the other hand, are more powerful inasmuch as they can provide structural analyte information. In such a respect, a vacuum ultraviolet (VUV) detector for use in GC analyses has been described.65 Such a form of detection can provide complementary information with respect to that obtained through MS. For example, compounds which are indistinguishable when using MS, such as FAME cis/​trans isomers, are characterized by different VUV responses.66 Furthermore, the VUV detector is non-​destructive, leading to the possibility to in-​series couple a mass spectrometer. Finally, as with MS, the deconvolution of overlapping spectra is possible.

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Although not specifically a food application, Zoccali et al. combined an FM GC×GC system with a VUV detector in applications on a mixture of standard FAMEs and a sample of biodiesel.66 With respect to other types of detectors, it must be noted that the 2D outlet is at above-​ambient pressure conditions. Such a factor must be accounted for during optimization of the flow-​modulation conditions.

7.3.3 Hybrid Multidimensional Gas Chromatography As seen in Chapter 6, heart-​cutting MDGC finds wide use in the field of food analysis. In specific applications, such as for the determination of enantiomer ratios (enantio-​MDGC), olfactometry measurements (MDGC-​O) and isotope ratio analyses (MDGC-​IRMS), such a technology is certainly the prime choice. In other types of food research, in particular in untargeted and fingerprinting analyses, GC×GC-​MS represents the most powerful option. As a consequence, the development of unified MDGC systems can be considered a research area of high interest, even though it is limited to only a few research groups. In principle, there are two types of unified MDGC systems, namely those with a single or two transfer devices. In the former case, the interface between the two analytical dimensions is capable of both heart-​cutting and comprehensive 2D operation. As a consequence, the development of unified MDGC instruments with a single transfer device is more demanding from a technological standpoint. Marriott’s group has directed a substiantial amount of research work to unified MDGC systems. In particular, a switchable heart-​cutting/​comprehensive 2D GC instrument was constructed by connecting the outlet of the 1D column (low polarity, 30 m × 0.25 mm ID × 0.25 μm df) to the inlet of a Deans switch. The two outlets of the Deans switch were connected to a short (0.786 m × 0.10 mm ID × 0.10 μm df) and long (30 m × 0.25 mm ID × 0.25 μm df) column, both with a polyethylene glycol stationary phase. Both 2D columns were passed through an LMCS, and then each connected to an FID. Applications were carried out on a series of compounds, typical of essential oils, in heart-​ cutting (with and without cryogenic entrapment) and GC×GC modes. Mixed heart-​cutting MDGC/​GC×GC operation was also illustrated: chromatography bands at the outlet of the first dimension were subjected to 2D analysis mainly on the short column, with target separations performed on the long column at specific time intervals.67 In a further study, a hybrid (sequential) GC×GC-​heart-​cutting MDGC instrument was developed, characterized by a 30 m × 0.32 mm ID × 0.50 μm df polyethylene glycol 1D column and a 5 m × 0.15 mm ID × 0.15 μm df low-​polarity 2 D one.68 An LMCS was used as modulator. The outlet of the 2D column was connected to the inlet of a Deans switch; the two outlets of the Deans switch were connected to a short uncoated column (1.9 m × 0.10 mm ID) and long analytical one (20 m × 0.18 mm ID × 0.18 μm df), with the latter characterized by a “poly(50%dimethyl-​50%diphenylsiloxane)” type of selectivity. The

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Figure 7.20 Two-​dimensional result relative to the GC×GC-​FID analysis of menhaden oil FAMEs using the same ionic-​liquid stationary phase in both analytical dimensions. Reproduced from Ref. 64 with permission from American Chemical Society, Copyright 2013.

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uncoated column was linked to a “stand-​by” FID (1) (GC×GC analysis only), while the long analytical column was linked to another FID (2), to monitor the GC×GC-​heart-​cutting MDGC result. A GC×GC-​FID1 chromatogram, related to the analysis of coffee volatiles (extracted by using SPME), is illustrated in Figure 7.21A. The three target analytes, each confined within a quadrilateral in Figure 7.21A, were subjected to heart-​cutting. The efficiency of the heart-​ cutting process can be observed in Figure 7.21B, which shows the GC×GC-​ FID1 result, after heart-​cutting. The group of Seeley has performed a great deal of research on single transfer devices, capable of multi-​operation, by extending the concept of the Deans switch to the GC×GC field. For example, a classical (rapid-​switching) Deans switch was exploited to perform GC×GC analysis by sampling the first dimension eluent at specific time intervals, although continuously throughout the analysis. The Deans switch used in such a manner is a low-​duty cycle transfer device, because only a fraction of the injected sample reaches the detector at the 2D outlet.69 A later model defined as the multimode modulator, which stemmed from the Deans switch concept, was capable of heart-​cutting MDGC, as well as low and high (100%) duty cycle comprehensive 2D GC.70 Fuel applications were described in both investigations.

7.4 Conclusions The use of GC×GC in food analysis has a history of practically two decades.14 As observed in other research fields, the use of this 2D technology has enabled more in-​depth investigations in practically all aspects of food research. In fact, the power of GC×GC (in particular with MS detection) has been demonstrated not only in untargeted investigations, but also in food safety and fingerprinting studies. The availability of a GC×GC instrument, in a laboratory devoted to food analysis, is certainly an added value. One must bear in mind, at the same time, that technologies such as 1D GC and heart-​cutting MDGC are still valuable tools in many instances. With regard to mass spectrometry, it would appear that the use of GC×GC-​ LR ToFMS or GC×GC-​QMS would be capable of covering most food-​analysis requirements. Obviously, the type of modulation process must also be considered (cryogenic modulation provides the highest sensitivity). The use of more powerful forms of MS, namely QqQMS and HR ToFMS, would appear to be less justified, and probably for such a reason, the amount of published data is limited. Finally, an attempt will be made to answer one of the main questions asked in relation to GC×GC (not only in the food analysis field): will the technology become well-​established and widely diffused? The response is probably no. In the same manner as heart-​cutting MDGC, GC×GC will probably find use in particular areas of food analysis (e.g., untargeted analyses of complex samples) and less in others (e.g., targeted analyses). Such an opinion also derives from the fact that mass spectrometry is evolving faster than gas chromatography, leading to reduced needs of a high-​resolution GC step.

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Figure 7.21 A  GC×GC-​FID1 chromatogram, related to the analysis of coffee volatiles (extracted by using SPME) is shown in (A). The GC×GC-​FID1 result, after heart-​cutting, is shown in (B). Reproduced from Ref. 68 with permission from American Chemical Society, Copyright 2012.

References R. Consden, A. H. Gordon and A. J. P. Martin, Biochem. J., 1944, 38, 224. M. M. Bushey and J. W. Jorgenson, Anal. Chem., 1990, 62, 161. Z. Liu and J. B. Phillips, J. Chromatogr. Sci., 1991, 29, 227. J.-​M. D. Dimandja, S. B. Stanfill, J. Grainger, D. G. Patterson Jr., J. High Resolut. Chromatogr., 2000, 23, 208. 5. D. Sciarrone, S. Pantò, A. Rotondo, L. Tedone, P. Q. Tranchida, P. Dugo and L. Mondello, Anal. Chim. Acta, 2013, 785, 119. 6. J. C. Giddings, J. Chromatogr. A, 1995, 703, 3. 7. R. E. Murphy, M. R. Schure and J. P. Foley, Anal. Chem., 1998, 70, 1585. 1. 2. 3. 4.

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8. R. M. Kinghorn and P. J. Marriott, J. High Resolut. Chromatog., 1998, 21, 620. 9. E. B. Ledford Jr., 23rd International Symposium on Capillary Chromatography, 5–​10 June 2000, Riva del Garda, Italy. 10. E. B. Ledford, C. Billesbach and J. Termaat, Pittcon 2002, 17–​22 March 2002, New Orleans, USA. 11. J. V. Seeley, N. J. Micyus, J. D. McCurry and S. K. Seeley, Am. Lab., 2006, 38, 24. 12. G. Purcaro, P. Q. Tranchida, C. Ragonese, L. Conte, P. Dugo, G. Dugo and L. Mondello, Anal. Chem., 2010, 82, 8583. 13. G. S. Frysinger and R. B. Gaines, J. High Resolut. Chromatogr., 1999, 22, 251. 14. P. Q. Tranchida, P. Donato, F. Cacciola, M. Beccaria, P. Dugo and L. Mondello, TrAC, Trends Anal. Chem., 2013, 52, 186. 15. S. Risticevic, J. R. DeEll and J. Pawliszyn, J. Chromatogr. A, 2012, 1251, 208. 16. J. S. Câmara, M. A. Alves and J. C. Marques, Anal. Chim. Acta, 2006, 563, 188. 17. B. T. Weldegergis, A. M. Crouch, T. Górecki and A. de Villiers, Anal. Chim. Acta, 2011, 701, 98. 18. C. Veenaas and P. Haglund, J. Chromatogr. A, 2018, 1536, 67. 19. B. T. Weldegergis, A. de Villiers, C. McNeish, S. Seethapathy, A. Mostafa, T. Górecki and A. M. Crouch, Food Chem., 2011, 129, 188. 20. S. Rochat, J.-​Y. de Saint Laumer and A. Chaintreau, J. Chromatogr. A, 2007, 1147, 85. 21. J. Schurek, T. Portolés, J. Hajslova, K. Riddellova and F. Hernández, Anal. Chim. Acta, 2008, 611, 163. 22. J.-​F. Focant, G. Eppe, M.-​L. Scippo, A.-​C. Massart, C. Pirard, G. Maghuin-​ Rogister and E. De Pauw, J. Chromatogr. A, 2005, 1086, 45. 23. B. L’Homme, G. Scholl, G. Eppe and J.-​F. Focant, J. Chromatogr. A, 2015, 1376, 149. 24. M. Adahchour, M. Brandt, H.-​U. Baier, R. J. J. Vreuls, A. M. Batenburg and U. A. T. Brinkman, J. Chromatogr. A, 2005, 1067, 245. 25. D. Ryan, R. Shellie, P. Tranchida, A. Casilli, L. Mondello and P. Marriott, J. Chromatogr. A, 2004, 1054, 57. 26. S. K. Dart and H. E. Nursten, in Coffee Volume 1: Chemistry, ed. R. J. Clarke and R. Macrae, Elsevier, London, 1985, ch. 7, pp. 223–​265. 27. A. Liberti and G. Conti, Possibilità di applicazione della cromatografia in fase gassosa allo studio delle essenze. Proceedings of the “Convegno internazionale di studi e ricerche sulle essenze”, Reggio Calabria, Italy, 27–​29 March, 1956. 28. L. Mondello, A. Casilli, P. Q. Tranchida, R. Costa, P. Dugo and G. Dugo, J. Chromatogr. Sci., 2004, 42, 410. 29. P. Q. Tranchida, M. Zoccali, I. Bonaccorsi, P. Dugo, L. Mondello and G. Dugo, J. Chromatogr. A, 2013, 1305, 276.

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30. P. Q. Tranchida, P. Dugo, L. Mondello and G. Dugo, in Citrus bergamia –​ Bergamot and its Derivatives, ed. G. Dugo and I. Bonaccorsi, CRC Press, Boca Raton, FL, 2014, ch. 7, pp. 81–​113. 31. P. Q. Tranchida, G. Purcaro, A. Visco, L. Conte, P. Dugo, P. Dawes and L. Mondello, J. Chromatogr. A, 2011, 1218, 3140. 32. P. Q. Tranchida, F. A. Franchina, P. Dugo and L. Mondello, J. Chromatogr. A, 2012, 1255, 171. 33. P. Q. Tranchida, F. A. Franchina, M. Zoccali, I. Bonaccorsi, F. Cacciola and L. Mondello, J. Sep. Sci., 2013, 36, 2746. 34. P. Q. Tranchida, F. A. Franchina, P. Dugo and L. Mondello, J. Chromatogr. A, 2014, 1359, 271. 35. P. Q. Tranchida, F. A. Franchina, P. Dugo and L. Mondello, J. Chromatogr. A, 2014, 1372, 236. 36. C. Cordero, C. Bicchi and P. Rubiolo, J. Agric. Food Chem., 2008, 56, 7655. 37. H.-​G. Schmarr and J. Bernhardt, J. Chromatogr. A, 2010, 1217, 565. 38. J. Kiefl, C. Cordero, L. Nicolotti, P. Schieberle, S. E. Reichenbach and C. Bicchi, J. Chromatogr. A, 2012, 1243, 81. 39. C. Cordero, E. Liberto, C. Bicchi, P. Rubiolo, P. Schieberle, S. E. Reichenbach and Q. Tao, J. Chromatogr. A, 2010, 1217, 5848. 40. G. Purcaro, C. Cordaro, E. Liberto, C. Bicchi and L. S. Conte, J. Chromatogr. A, 2014, 1334, 101. 41. G. Purcaro, P. Q. Tranchida, L. Conte, A. Obiedzińska, P. Dugo, G. Dugo and L. Mondello, J. Sep. Sci., 2011, 34, 2411. 42. Council directive 98/​83/​EC of 3 November 1998 on the quality of water intended for human consumption, Official Journal of the European Communities OJ L 330, 1998, 32–​54. 43. L. Mondello, M. Zoccali, G. Purcaro, F. A. Franchina, D. Sciarrone, S. Moret, L. Conte and P. Q. Tranchida, J. Chromatogr. A, 2012, 1259, 221. 44. V. Simanzhenkov and R. Idem, Crude Oil Chemistry, Marcel Dekker, New York, NY, 2003. 45. G. Purcaro, P. Q. Tranchida, L. Barp, S. Moret, L. S. Conte and L. Mondello, Anal. Chim. Acta, 2013, 773, 97. 46. P. Q. Tranchida, I. Aloisi, B. Giocastro and L. Mondello, TrAC, Trends Anal. Chem., 2018, 105, 360. 47. G. Cao, Q. Shan, X. Li, X. Cong, Y. Zhang, H. Cai and B. Cai, Analyst, 2011, 136, 4653. 48. E. de Hoffmann, J. Mass Spectrom., 1996, 31, 129. 49. U. Koesukwiwat, S. J. Lehotay and N. Leepipatpiboon, J. Chromatogr. A, 2011, 1218, 7039. 50. M. Poliak, A. B. Fialkov and A. Amirav, J. Chromatogr. A, 2008, 1210, 108. 51. P. Q. Tranchida, F. A. Franchina, M. Zoccali, S. Pantò, D. Sciarrone, P. Dugo and L. Mondello, J. Chromatogr. A, 2013, 1278, 153. 52. H. J. Tobias, Y. Zhang, R. J. Auchus and J. T. Brenna, Anal. Chem., 2011, 83, 7158.

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53. D. Sciarrone, A. Schepis, M. Zoccali, P. Donato, F. Vita, D. Creti, A. Alpi and L. Mondello, Anal. Chem., 2018, 90, 6610. 54. S.-​T. Chin, Y. Nolvachai and P. J. Marriott, Chirality, 2014, 26, 747. 55. R. Joshi, P. Sharma. V. Sharma, R. Prasad, R. K. Sud and A. Gulati, J. Sci. Food Agric., 2013, 93, 1303. 56. C. F. Poole, J. Chromatogr. A, 2015, 1421, 137. 57. M. A. Martínez-​Uroz, M. Mezcua, N. Belmonte Valles and A. R. Fernández-​ Alba, Anal. Bioanal. Chem., 2012, 402, 1365. 58. E. Engel, J. Ratel, P. Blinet, S.-​ T. Chin, G. Rose and P. J. Marriott, J. Chromatogr. A, 2013, 1311, 140. 59. L. L. P. van Stee, U. A. T. Brinkman and H. Bagheri, TrAC, Trends Anal. Chem., 2002, 21, 618. 60. L. L. P. van Stee, J. Beens, R. J. J. Vreuls and U. A. T. Brinkman, J. Chromatogr. A, 2003, 1019, 89. 61. S.-​T. Chin, Z.-​Y. Wu, P. D. Morrison and P. J. Marriott, Anal. Methods, 2010, 2, 243. 62. S.-​T. Chin, G. T. Eyres and P. J. Marriott, J. Chromatogr. A, 2011, 1218, 7487. 63. P. Q. Tranchida, R. Costa, P. Donato, D. Sciarrone, C. Ragonese, P. Dugo, G. Dugo and L. Mondello, J. Sep. Sci., 2008, 31, 3347. 64. P. Delmonte, A. R. Fardin-​Kia and J. I. Rader, Anal. Chem., 2013, 85, 1517. 65. K. A. Schug, I. Sawicki, D. D. Carlton, Jr., H. Fan, H. M. McNair, J. P. Nimmo, P. Kroll, J. Smuts, P. Walsh and D. Harrison, Anal. Chem., 2014, 86, 8329. 66. M. Zoccali, K. A. Schug, P. Walsh, J. Smuts and L. Mondello, J. Chromatogr. A, 2017, 1497, 135. 67. B. Maikhunthod, P. D. Morrison, D. M. Small and P. J. Marriott, J. Chromatogr. A, 2010, 1217, 1522. 68. B. Mitrevski and P. J. Marriott, Anal. Chem., 2012, 84, 4837. 69. J. V. Seeley, N. J. Micyus, S. V. Bandurski, S. K. Seeley and J. D. McCurry, Anal. Chem., 2007, 79, 1840. 70. J. V. Seeley, N. E. Schimmel and S. K. Seeley, J. Chromatogr. A, 2018, 1536, 6.

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Multidimensional  LC-​GC M. BIEDERMANN* AND K. GROB Official Food Control Authority of the Canton of Zürich, Fehrenstrasse 15, Zürich, 8032, Switzerland *Email: [email protected]

8.1 Introduction 8.1.1 Why Online  LC-​GC? Using HPLC for sample preparation has advantages in two main directions: high separation efficiency and automation. High separation efficiency provides highly selective sample preparation, isolating the targeted substance or a group of substances out of a complex mixture with a minimum of by-​ products. It may also enable pre-​separation of similar substances in a way not achievable in GC. However, it is of no use if a broad range of compounds forming broad fractions needs to be analyzed (e.g., a variety of pesticides), because the separation efficiency cannot be exploited and the fractions are difficult to transfer to the GC system. A high-​resolution pre-​separation may enable the use of GC with non-​selective detectors, in particular with flame ionization detection (FID): because the FID provides virtually the same response per mass for components of similar structure, quantitation is possible without calibration of response factors, which is particularly useful for substances for which no pure standards are available.

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Automation is of particular interest for the analysis of a large number of samples. Getting a complex method running may not be worth the effort if it is just for a few samples, and keeping the method installed blocks a dedicated instrument if it is only for occasional analysis. Online LC-​GC usually enables lower detection limits, not only because of increased selectivity, better eliminating interferences, but also because of large aliquots: the amount of sample, such as a raw extract, that can be injected into HPLC is smaller than for solid-​phase extraction columns, but transfer to GC is complete, whereas in offline procedures usually only a small part of the sample is injected into the GC system. Further advantages of online LC-​GC are high repeatability, low solvent consumption (compared to conventional solid-​phase sample preparation) and the avoidance of sample contamination during the preparation step (it is a closed system). Finally, the fraction of interest can be accurately determined and monitored by, e.g., ultraviolet (UV) detectors –​which is also a prerequisite if sharp cuts of the fraction need to be transferred to the GC instrument. Liquid chromatography columns are usually usable for hundreds or even thousands of analyses. Method development in HPLC tends to be fast, as the substances of interest are mostly detectable. This enables optimization of the conditions in a few runs. It may be more challenging to find an internal standard eluted in the same retention window. The second step in method development is the transfer process. It comprises the selection of the transfer technique (which is usually quite obvious) and adjustment of the conditions for eluent evaporation. Online systems can be extended to more complex sample treatment, with more than one separation in the liquid phase, pretreatment in a multifunctional (“intelligent”) autosampler or a more sophisticated final analysis, such as comprehensive two-​dimensional GC. It may include steps like online liquid–​liquid extraction1–​2 or derivatization.3 Examples of such complex methods are reported in the applications described in the second part of this review. In 1991, a thorough review on LC-​GC was published.4 In the meantime, interfaces have been modified, but the basics of the technology are still the same. Recent reviews on online LC-​GC were published by Hyötyläinen5,6 and Purcaro et al.7

8.1.2 History of Online LC-​GC The principal problem in early LC-​GC coupling was the discrepancy between the volume of the LC fraction and that amenable to GC analysis: LC fractions commonly have volumes at least in the order of several 100 µL, far exceeding those conventionally used in GC. First attempts to couple LC to GC, described by Majors, were made via an autosampler injecting into a conventional vaporizing GC injector: the effluent from the LC was fed through the syringe to waste, and at a predetermined moment a few microliters of the LC fraction

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were injected into the GC system. In 1983, Apffel and McNair used this arrangement for hydrocarbon group-​type analysis in mineral oils.9 A similar system was also described by Ramsteiner.10 Because conventional GC injection enabled the transfer of only a few microliters at a given LC retention time, it had at least three drawbacks: detection limits were high, a shift in LC retention times caused the fraction analyzed by GC to be taken from another portion of the LC peak, which affected the reliability of quantitative data and, finally, group-​type analysis, such as of hydrocarbons, was possible only if the components of interest co-​eluted perfectly. Raglione et al.11 improved on this by what was called an “isotachic eluent splitter”: the volume of the LC fraction was reduced by splitting through bundled capillaries. This enabled the transfer of a proportion of a broader LC retention window, but sensitivity remained low. In 1985, Cortes et al.12 proposed using packed LC capillary columns to reduce the volume of the LC fraction, which did not, however, solve the problem of low capacity of sample preparation. Obviously the task was to find ways to introduce far larger LC fractions into the GC system, which required efficient and selective ways of removing large amounts of solvent. A proposal already existed: in 1979, Vogt et al. injected up to 250 µL through a programmed-​temperature vaporizing (PTV) injector,13 but nobody made use of it for LC-​GC at that time. In the early 1980s, on-​column injection was further investigated as the most accurate way of sample introduction in GC. Band broadening effects and reconcentration mechanisms were of interest, as they promised to be manageable and enable the injection of larger volumes. This resulted in the use of solvent trapping and the retention gap technique for reconcentration to the sharp initial bands required for GC (see below). In 1984, this on-​column technology was applied to online LC-​GC, using an HPLC pump, a UV detector, some manual valves and a GC instrument equipped with an on-​column injector. Up to 270 µL of eluent (cyclohexane) were transferred from a 10 cm × 2 mm ID HPLC column.14 To this end, a 50 m × 0.32 mm ID uncoated but deactivated (glass) capillary pre-​ column was used to retain the liquid. During the following years, other on-​column techniques for online LC-​GC were investigated, mainly in two directions of interfaces for eluent evaporation: evaporation fully concurrent with the transfer and partially concurrent eluent evaporation with the retention gap technique (described in detail in Section 8.3.4). They were based on normal-​phase LC (NPLC), typically with pentane or hexane and a modifier as eluent. Soon, numerous attempts were made to couple reversed-​phase HPLC (RPLC) to GC, but this proved to be more demanding and, in fact, still today no such technique has become routine by a wide range of users. There are technical problems, but also reasons why often NPLC is the choice anyway: samples (particularly food) frequently contain amounts of fat which cannot be injected into an RPLC system in sufficient amounts because of insufficient solubility in the mobile phase. Compounds amenable to GC are of up to moderate polarity; more polar ones need to be derivatized,

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which is not normally compatible with aqueous eluents. No salts can be tolerated in GC. The first instrument for automated online LC-​GC was brought to the market around 1990: the Dualchrom 3000 designed by F. Munari at Carlo Erba/​Fisons (today Thermo). The main users were involved in the control of edible oil authenticity, such as of extra-​virgin olive oil, as corresponding methods had soon been developed (see Sections 8.10.5 to 8.10.9). However, the commercial success of this instrument was modest, as the technique was considered demanding. Improving analysis using mass spectrometry (MS) was more attractive than by using more sophisticated chromatography. This changed when food contamination with mineral oil became a public issue in 2010 (see Section 8.10.1). Presently, numerous instrument manufacturers offer such instruments and many laboratories even run several of them, although almost exclusively for the analysis of hydrocarbons of mineral or synthetic origin in food or food-​related materials, such as packaging materials. The many applications touched upon in this review emphasize the fact that LC-​GC instruments are far more than hydrocarbon analyzers.

8.2 Concepts for the GC Introduction of Large Sample Volumes A range of techniques to introduce large volumes of liquid into GC instruments have been investigated. An overview is provided herein, with characteristics as well as the main advantages and drawbacks. The principal mechanisms and concepts are explained in their historical context, including some that have not been further followed up, but might be of interest for future developments. The interfaces to GC and instrumental aspects are described, again presenting a broad overview of what has been investigated in the past. Finally, Section 8.9 concludes from all this on the practical description of the two most commonly applied LC-​GC techniques. Large volume injection and transfer from an LC system to a GC one involve four elements to be considered: the solvent evaporation site, storage and discharge of the solvent vapors, solvent–​solute separation and reconcentration of the solutes to start the separation process as sharp initial bands. Injector-​ and on-​column-​based techniques are distinguished, even though the two are not fundamentally different and can even be combined.

8.2.1 Split/​Splitless Injection 8.2.1.1 Classical Split/​Splitless Injection Classical split or splitless injection uses a permanently hot injector chamber in which the sample is rapidly vaporized. The vapors (mainly solvent vapors) formed must be stored in this chamber temporarily before they are transferred into the column or released through the split outlet. The injection volume is

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limited by the vapor volume that fits into this chamber. Mainly depending on the type of solvent and the carrier gas inlet pressure, 1 µL of solvent is converted into 200–​700 µL of vapor. Inevitably these vapors mix with the carrier gas, resulting in an even larger vapor cloud. The internal volume of a typical injector liner (80 × 4 mm ID) is around 1 mL. There are ways to overcome such a limitation. “Overflow” techniques were investigated for organic solvents as well as aqueous samples: large samples (up to several 100 µL) are injected rapidly into a hot, packed vaporizing chamber, releasing the solvent vapors backwards through the widely open septum purge outlet. The solutes largely remain at the evaporation site, which is cooled to the solvent boiling point by the heat consumption of the vaporization process. Highly volatile compounds are lost, but the technique seemed adequate for a broad range of volatilities.15,16 Improvement was achieved by interrupting the carrier gas flow during the vaporization process.17 Using a co-​ solvent, such as butoxyethanol, up to 800 µL water were injected.18 Particularly with water, a violent process occurs, producing roughly half a liter of vapor in around 10 s that escapes through the septum purge. This overflow technique is largely self-​regulating and correspondingly simple to handle: the flow out of the exit automatically ceases when significant amounts of solvent vapors are no longer formed. Then, the carrier gas flow resumes and the evaporation zone is heated up again to the regulated injector temperature, releasing the solutes from the packing. Splitless injection with concurrent solvent recondensation (CSR) is a gentler alternative. It is based on the strongly accelerated transfer of the solvent vapors from the injector into the column through recondensation in the column inlet that is kept below the dew point (boiling point at the inlet pressure) of the solvent.19,20 Initially almost explosive evaporation in the packed hot injector increases pressure, pushing first vapors through the short piece of column mounted in the hot chamber into the cooler section in the oven. There, recondensation causes the volume of the vapors to shrink again to that of the liquid, which reduces pressure and sucks further vapor into the column. Cooling of the evaporation site to the solvent boiling point by the heat consumption of vaporization retains most solutes, but also the volatiles are not lost, because they are retained in the film of recondensed solvent in the column inlet (solvent trapping; Section 8.3.1). Recondensation causes “flooding” of the inlet, i.e., a flow of liquid spreading into the column. The resulting bands must be reconcentrated using an uncoated pre-​column that must be at least as long as needed to keep the liquid from the coated column (retention gap technique; Section 8.3.2). The splitless period must be longer than normal because of the time needed to bring the injector temperature back to the regulated level and vaporize the solutes. Compared to overflow, CSR splitless injection avoids losses of volatiles, but presupposes rather large uncoated pre-​columns. It can be seen as an on-​ column technique with the vaporizing chamber acting as a filter retaining high boiling or non-​volatile sample constituents, such as triglycerides or polymeric material (see Section 8.5.4).

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8.2.1.2 Programmed Temperature Vaporizing Injector The limitation of the injection volume by the size of the vaporizing chamber can also be overcome by a controlled evaporation rate in the injector. Using the so-​called solvent split technique in a PTV injector,17 the chamber is initially kept below the solvent boiling point at the inlet pressure (dew point), such that only as much vapors are formed as are carried away by the carrier gas.21 The solvent vapors are largely discharged through the split outlet. To keep the losses of volatiles low, the gas volume leaving through the split exit is critical, i.e., the flow rate and the duration until the split exit is closed for the splitless transfer of the solutes into the column. This regulation may be rather delicate. Transfer of the higher boiling solutes occurs by heating of the vaporizing chamber. The PTV injector used as LC-​GC interface may also be applied in modes other than solvent splitting, such as in the swing interface (Section 8.6.4) or as a vaporizing chamber ahead of an on-​column system for non-​wetting solvents or filter retaining non-​evaporating sample material (Section 8.5.4).

8.2.1.3 Packing of Injector Liners In solvent split injection or online transfer, the packing of the injector liner plays a key role for solvent–​solute separation. The liner must be packed with a material that retains the liquid. In an open tubular system, the liquid would mostly form a plug that is pushed forward by the carrier gas through the split exit and result in an almost complete loss of the solute material. In packed beds, the gas opens channels to flow through the liquid, removing the vapors it is saturated with. Often the recovery of volatile solutes is critical. Using PTV solvent splitting, solvent trapping may be used in the packed bed: the solvent ahead of the evaporation site retains the volatile solutes (see Section 8.3.1). However, solvent trapping in a packed bed is not as well-​organized a process as in open tubular (on-​column) systems, because solvent evaporation does not strictly proceed from the rear to the front. Solvent trapping presupposes closure of the split exit shortly before the end of solvent evaporation. Losses of volatiles can also be reduced by packing the vaporizing chamber with a material exhibiting retention power, such as a sorbent like Tenax®, but the reduced loss in volatiles may have to be paid for by more difficult desorption of high-​boiling substances. Packings should be inert to avoid adsorption or chemical degradation of solutes. The surfaces of the packing and the liner may also need to be chemically stable against hydrolysis, as the resulting hydroxyl groups (such as silanols) tend to be highly adsorptive (water is highly aggressive in the condensed form, but not as vapor). Silicones like polysiloxane polymers or silylated silicates are thermostable, but poorly resist hydrolysis. For instance, silylated glass or fused silica wool easily lose their inertness –​but may also deactivated again by involatile sample material. Organic polymers, such as

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®

Tenax or crosslinked polystyrenes, resist hydrolysis, but strongly retain the solute material. Polytetrafluoroethylene (PTFE) wool was proposed as a rather inert material.22 Carbofrit is a glassy carbon material of low retentive power. It consists of a regular, three-​dimensional network, providing lower resistance against gas flow than most glass wool packings. It is brittle, however, easily releasing particles that end up being blown into the column by the carrier gas. Polyimide was used for deactivation of the liner surface with a water-​resistant layer, for fixing the Carbofrit material to the insert wall and binding loose particles. Carbofrit is solid up to high temperatures, which prevents building up retention power.23

8.3 On-​column Techniques Classical on-​column techniques do without a vaporizing chamber: sample (mainly solvent) evaporation takes place at a controlled rate in the column inlet; the evaporation rate is limited by an oven temperature below the pressure-​corrected solvent boiling point.24,25 On-​column techniques make use of the two mechanisms, which are described in more detail below: solvent trapping and the retention gap technique.

8.3.1 Solvent Trapping for Volatile Solutes The vapor volume of a single microliter of solvent may exceed the entire internal volume of the column (even without considering dilution with carrier gas). It means that the first part may leave the column before the last portion has evaporated and started moving. This suggests extremely broad initial bands and severe peak broadening. However, due to partitioning effects, this does not happen in reality, perhaps with the exception of extremely volatile solutes. Apart from partitioning with the stationary phase, two “solvent effects” are important: the main one taking place in the column inlet and called “solvent trapping”, the other one effective throughout the coated column, called “phase soaking”.26,27 Solvent trapping describes the evaporation of volatile solutes in the presence of condensed solvent in the column inlet.28 Liquids introduced into a capillary tubing as such (e.g., by on-​column injection or LC-​GC transfer) or after recondensation (e.g., by splitless injection with the column being below the solvent dew point) form plugs or lenses that are pushed forward at high velocity by the carrier gas. Behind these plugs, an irregular layer of liquid is left on the capillary wall. The plugs are driven forward until the whole liquid is spread (the “flooded zone” in the column inlet; schematically shown in the summarizing description of Section 8.9.1). The thickness of this film is in the range of 10–​40 µm, which is thicker than the commonly used films of stationary phase by two orders of magnitude, and exhibits a correspondingly high gas chromatographic retention power. Solvent evaporation takes place virtually exclusively at the rear end of the layer, because the carrier gas is rapidly saturated by solvent vapors. The high

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boiling sample components are deposited onto the now dry capillary wall and remain there. The volatile compounds are no longer retained and evaporate from the dry column wall, but only get as far as the rear of the remaining solvent layer, where they are trapped again. This process is repeated until the last portion of solvent is evaporated, at once releasing the accumulated volatile solutes. There is, of course, a limitation to solvent trapping: extremely volatile substances, such as methane, are hardly trapped. Somewhat higher boiling ones and polar components in non-​polar solvents (or vice versa) are incompletely trapped, thus part of them pass into the column before solvent evaporation is completed. They form peaks with the shape of a stool or a chair, respectively.29,30 However, e.g., trapping of n-​octane by hexane is practically complete, which means that solvent trapping perfectly retains virtually all components eluted from a GC column after the solvent and forms the sharp initial bands required for obtaining sharp peaks. Solvent evaporation often takes several minutes. The volatile solutes are released at the moment when the last solvent fraction is vaporized and at the location in the column where this last evaporation event takes place. Hence, chromatography starts with a delay, essentially corresponding to the duration of solvent evaporation. This extra retention time depends on the sample volume introduced and other factors affecting the duration of solvent evaporation. Solvent trapping is an effective, well-​organized and reproducible process, more selective than any offline method, such as evaporation in a gas stream or a rotary evaporator. This means that as much as possible of the solvent evaporation should be performed in the GC system, namely by large volume injection rather than reconcentration before sample introduction, or by online LC-​GC rather than an analogous offline method.31 Phase soaking describes the effect of the solvent overloading the stationary phase beyond the flooded zone on the chromatography of solutes moving in the same zone.32,33 Depending on the interaction of the solvent with the stationary phase, the film is locally swollen to several times the normal thickness, affecting chromatography of the solutes which are co-​chromatographed on the up-​or downslope of this “hill” through at least part of the column. As a result, the bands of solutes migrating ahead of the solvent (eluted before the solvent) are broadened, whereas those migrating behind (eluted after the solvent) are sharpened, with an additional delay in retention time.

8.3.2 Reconcentration by the Retention Gap Effect Solvent trapping focuses the components moving in the column inlet at the oven temperature during solvent evaporation, which includes compounds eluted up to around 50°C above this temperature. Compounds eluted at higher temperatures, however, move too slowly or even remain where they were deposited by the evaporating solvent and form initial bands up to the length of the flooded zone.

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The liquid driven by the carrier gas easily floods a column inlet section far longer than acceptable for the separation process. As a rule of thumb, the flooded zone extends 5–​30 cm per microliter,34,35 with it easily ending up being many meters long. However, initial bands longer than 10–​30 cm (primarily depending on the length of the separation column) result in significant peak broadening. Broadening starts being visible some 50°C above the column temperature during solvent evaporation and affects all compounds eluted above that temperature in the same pattern. Typically peaks are not only broadened, but also distorted or split, depending on the distribution of the solute material within the flooded zone.36 Hence, the initial bands of the compounds eluted at elevated temperatures must be shortened/​narrowed, which is achieved by the retention gap effect.37,38 If the flooded zone is placed into an uncoated pre-​column, i.e., of low retention power, the components move through it at an oven temperature far below that allowing chromatography in the separation column. They are stopped and reconcentrated in the stationary phase at the entrance of the coated column and retained there until the oven temperature is increased to a level enabling chromatography at significant rate (also schematically shown in the summarizing description in Section 8.9.1). Under adequate conditions, initial bands are shortened approximately by the ratio of the retention powers in the uncoated pre-​column and the coated separation column.39 The retention power in an uncoated capillary that is deactivated without building up significant retention power (e.g., by silylation) corresponds to a coating of roughly 1–​4 nm stationary phase,40 which means that the band is shortened by a factor of around 50–​500, depending on the film thickness in the separation column.41 The uncoated pre-​column must not only be inert (non-​adsorptive), but also wettable by the sample liquid, essentially the solvent, to enable the formation of a continuous layer: non-​wetting solutions leave a droplet here and there on the capillary wall and are likely to slip through the whole pre-​column into the separation column, possibly directly into the detector. Wettability is no problem for pentane or hexane with volatile modifiers of intermediate polarity, but, e.g., trimethyl silylation is not suitable for solvents with an increased surface tension, such as dichloromethane, acetone, methyl tert-​butyl ether (MTBE) and more polar ones. Phenyl dimethyl silylation42 results in substantially better wettability.39 A very thin film of OV-​1701 (14% cyanopropyl phenyl methyl polysiloxane) provided wettability for a wide range of organic solvents, inertness at least equivalent to silylation and high resistance against hydrolysis.43 The uncoated pre-​column is prone to build-​up of retention power and adsorptivity from the deposition of non-​evaporating sample by-​products, sometimes also to chemical attack onto the deactivation. It was proposed to reduce this propensity and, furthermore, increase the reconcentration efficiency at the entrance of the separation column by installation of the pre-​column in a separate oven heated at a somewhat higher temperature.44,45 Although being convincing as a concept, it has not been implemented in practice, presumably

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because of insufficient necessity, also taking into account that an oven for commonly used 0.53-​mm ID pre-​columns cannot be small.

8.3.3 Solvent Vapor Exit For the first experiments with on-​column transfer to GC in 1984, involving a fraction of 270 µL volume, a 50-​m uncoated pre-​column was used and solvent evaporation took about 28 min (with a correspondingly broad solvent peak). All the solvent was burned in the FID, which may slowly contaminate the detector with soot (depending on the solvent). For other applications it was passed through an electron capture detector (ECD), which was no problem for most solvents. Using MS the vacuum seemed to collapse, which is, however, an artifact as no more vapor enters the ion source than carrier gas and vapors are even more efficiently pumped off than is carrier gas. High pressure is indicated because solvents are detected at higher sensitivity. The first experiments in 1988 to avoid the discharge of the solvent through the detector used an effluent splitter, positioned between the column exit and the detector. It was considered that this would rule out losses through an exit located earlier in the system.46 However, this solution was not convincing, mainly because the discharge of the vapors through the separation column remained slow and the solvent peak correspondingly broad. The first exit introduced before the separation column (also described in 1988) involved a “vaporizer” and a “cold trap” located before a “splitter valve”, both in a capillary pre-​column that was mounted into the “sample valve” through which the HPLC effluent was fed.47 It was shown that a stationary phase in this pre-​ column would reduce losses of volatiles and that thicker films result in peak distortion –​problems which can be solved using solvent trapping. The “early solvent vapor exit” is positioned at the analogous site (between the pre-​column(s) and the separation column), but simpler evaporation techniques are used.48,49 It consists of a T-​(or Y-​) piece and connects to a valve positioned outside the GC oven that switches between a fully open exit and a strong restriction to allow a small purge flow preventing solvent residues returning to the T-​piece and entering the separation column (shown in Figure 8.6). The early solvent exit improved the transfer in several respects. Because the resistance against the gas flow through the vapor exit (usually a short 0.53-mm ID capillary tubing) is far lower than through the separation column, the detector is largely protected from solvent vapors. Second, at a given inlet pressure, the flow rate is strongly increased when only passing through the pre-​ column, particularly if the latter is of 0.53-​mm ID, accelerating the discharge of the solvent vapors and allowing for higher transfer flow rates. Finally, the flooded zone is strongly shortened, because at high velocity the solvent layer is thicker, which enables to retain several times more liquid in a pre-​column of given length.50 A system was proposed that enables regulation of backpressure at the vapor exit and, thus, the flow rate in the pre-​column, to facilitate the adjustment of

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the evaporation rate to the transfer rate. However, present instruments do not use it, as the same can be achieved by adjusting the oven temperature and the inlet pressure. The early vapor exit must be closed at the appropriate moment, such as shortly before or after the end of solvent evaporation. Attempts were made to automate closure. Temperature on the outer column wall was measured to monitor the cooling by solvent evaporation at a given site, but the determination of the critical site was not easy.52 The changes in carrier gas flow rate resulting from the different viscosities of the solvent vapors and the carrier gas were recorded too.53 This does not work, however, for the most critical case, when solvent trapping is used: because the volatiles are released with the last fraction of solvent, the exit is closed too late. Furthermore, depending on the viscosity of the solvent vapors, the carrier gas flow rate may increase, decrease or hardly change at all.54 No such automated system went into routine, also because the initially used flame method is too easy to apply: solvent vapors in the effluent of the vapor exit are determined by lighting the gas: they produce a bright yellow flame, whereas hydrogen burns as an almost colorless flame and helium does not burn at all. Knowing the evaporation time, the exit is closed 1–​3 s earlier or later, depending on the evaporation technique applied (see below).

8.3.4 Partially or Fully Concurrent Eluent Evaporation? In developing an on-​column LC-​GC transfer method, the first decision to take is whether the sample contains volatile solutes of interest that require solvent trapping. If it does, partially concurrent eluent evaporation55 is the technique commonly chosen; otherwise it is fully concurrent evaporation.56 This decision has a major impact on the pre-​column(s) and the conditions to be selected. “Concurrent” relates to the evaporation during transfer: either part of the eluent or all of it is evaporated during introduction into the GC system. With partially concurrent evaporation, a minor amount of the transferred fraction needs to be retained as a liquid by the pre-​column –​none at all in fully concurrent evaporation. With (fully) concurrent evaporation, a basically unlimited volume of eluent can be transferred into a short pre-​column because no liquid accumulates. The volume is just limited by the amount of vapor/​carrier gas that drives the more volatile solutes through the vapor exit, i.e. the increasing losses of the more volatile constituents. As much as 20 mL of hexane were transferred at a rate of 1 mL min–​1, but the first sharp and quantitatively correct peak was only eluted at 134°C above the oven temperature during transfer,57 which limits the applicability to components eluted at a high temperature. With partially concurrent evaporation, a small proportion of the eluent must form a layer of liquid in the pre-​column in order to provide solvent trapping of volatiles. Using a constant transfer rate, this means that during transfer the flooded zone grows and eventually reaches the capacity limit of the uncoated pre-​column. By sharply selecting conditions, i.e., with an evaporation rate

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corresponding to a high percentage of the transfer rate, large volumes can still be transferred into pre-​columns of moderate size, e.g., 5–​10 m × 0.53 mm ID. Reducing the transfer rate or increasing the evaporation rate after an initial phase, this volume could be increased even further. However, the upper limit of the fraction volume for a simple and robust method might be in the range of 1–​2 mL, primarily determined by the stability of the LC flow rate. Partially as well as fully concurrent eluent evaporation require GC conditions resulting in an evaporation rate close to the transfer rate. An attempt was made to calculate these conditions from the pre-​column geometry, the vapor pressure of the solvent, the vapor volume and viscosity,58 but results were not sufficiently accurate. No such system is available today. Conditions are adjusted experimentally using the flame method (see Section 8.9) Co-​solvent trapping enables the transfer of larger volumes and in a more robust manner: a higher boiling co-​solvent is added that does not disturb HPLC, such as n-​heptane to pentane. The proportion added must be sufficient that a fraction of the co-​solvent remains condensed in the uncoated pre-​ column while the main solvent evaporates concurrently (e.g., 5% n-​heptane added to pentane).59,60 The higher the boiling point of the co-​ solvent is above that of the main solvent, the lower the amount needed. The evaporation conditions are more easily regulated for this small amount of co-​solvent. The column temperatures during transfer are not substantially higher, but the purity of the solvents suitable as co-​solvents may be limiting. There is, however, little experience with this technique, presumably because of an insufficient need. In conclusion, (fully) concurrent eluent evaporation is simpler than partially concurrent eluent evaporation and preferred if applicable, i.e., if the sample components to be analyzed are of a volatility below that of an n-​alkane of roughly 15–​18 carbons (depending on numerous factors). However, in probably more than half of all applications, partially concurrent evaporation has been used.

8.3.5 Gas Discharge Versus Overflow For the design of LC-​GC interfaces based on on-​column techniques, two basic concepts need to be distinguished, namely “gas discharge” and “overflow”.93 Partially concurrent eluent evaporation requires gas discharge in order to conduct solvent evaporation from the rear to the front of the flooded zone. This means that the carrier gas drives the liquid and discharges the vapors through the exit, part of the gas being replaced by vapors. The challenge is the adjustment of the conditions to achieve an adequate rate of eluent evaporation, principally the oven temperature, the carrier gas inlet pressure and the vapor volume generated by the transfer flow rate (depending on the vapor volume per volume of solvent). For fully concurrent evaporation, overflow is also possible. In the past, mostly overflow was preferred: the carrier gas flow is interrupted and undiluted

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vapors are discharged, driven by their own vapor pressure. This reduces the number of parameters to be optimized essentially to oven temperature: this must be sufficiently high to generate the vapor pressure required for the discharge of the vapors, but it should not be much higher, because this reduces the retention of the solutes in the system. Actually, this temperature must be slightly above the boiling point at the pressure required for the vapor discharge in order to provide the gradient for transferring the vaporization heat from the oven atmosphere to the liquid inside the capillary. The dew point of the solvent is a key parameter. In overflow, it corresponds to the boiling point at the inlet pressure required for the vapor discharge, which is above the standard boiling point. In gas discharge (schematically shown in Figure 8.6), the vapors are diluted, i.e., their vapor pressure in the mixture is only part of the total pressure (partial vapor pressure). If it is less than 1 bar, the dew point is below the standard boiling point. A lower oven temperature reduces the vapor pressure and increases the gas volume to discharge a given amount of vapors, which increases the flow rate driving the solutes forward, e.g., to a vapor exit. However, it also increases the retention power of the stationary phase in the pre-​column. As the latter might more than outweigh the effect of the larger gas volume driving the solutes forward, gas discharge seems to perform better in terms of losses of volatiles than overflow. In conclusion, for partially concurrent eluent evaporation, gas discharge is a prerequisite, but for (fully) concurrent evaporation the choice is open. At earlier times, overflow was used owing to simplicity, but today gas discharge is mostly preferred, as a single interface can be used for both techniques and performance is slightly better.

8.4 Problems with Water-​containing Eluents The on-​column transfer of water is difficult, because no pre-​column is available that is water-​wettable,61 and the deactivation of pre-​column surfaces tends to be hydrolyzed rather rapidly.62 Various attempts have been made to overcome these obstacles. Addition of an organic solvent may turn phenyl-​dimethyl-​silylated surfaces wettable, but the mixture needs to be such that toward the end of solvent evaporation the organic solvent is left (residual water would run through the column). For this reason, the organic solvent needs to be added at, or above the azeotropic mixture, as achieved with, e.g., water : n-​propanol at about 30 : 70.63 This was applied for the determination of atrazine (a herbicide) in water, using water : methanol : n-​propanol (57 : 38 : 5) as mobile phase for the desorption from a reversed-​phase HPLC column.64 The detection limit with a nitrogen/​phosphorus detector (NPD) was around 5 ng L–​1. However, at the evaporation temperatures required, water attacks the pre-​column surface and renders it adsorptive. Water in the liquid phase or adsorbed to the surface temporarily deactivates the attacked surface rather efficiently, but this deactivation is lost upon heating (evaporation of the water) and

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presupposes that the length of the flooded zone be constant to always cover the attacked zone. Numerous attempts to achieve online analysis of compounds in water were described by the group of Brinkman.65,66 Direct injection of water or water-​ containing eluents into a PTV injector in solvent split mode was compared to indirect transfer via solid-​phase extraction (SPE).67 Following SPE, organic compounds were transferred to a GC system with an organic solvent, but the removal of the residual water requires attention. Intermediate drying of the LC column with nitrogen (30 min) or drying the ethyl acetate used as eluent in a cartridge containing a drying agent (anhydrous sodium sulfate or silica) were proposed.68 A drying cartridge in the online system contained a 4 × 10 mm ID bed of silica and was heated out at 160°C to remove the absorbed water.69 In ref. 70, the transfer of RPLC eluents was optimized in a number of aspects. The eluent used consisted of an azeotropically boiling acetonitrile : water mixture (84 : 16). An early vapor exit was used to accelerate solvent evaporation from 20 to 175 µL min–​1. The pre-​column was coated with a thin (0.02 µm) film of Carbowax to achieve wettability and retention power to reduce losses of volatiles. To protect the NPD, a pressure-​balanced system was used to produce a gas flow away from the separation column through the vapor exit. Concurrent evaporation does not require wettability of the pre-​column, but the large amount of vapor formed and the high oven temperature needed limits this technique to high-​boiling analytes. To improve the retention of volatiles, co-​solvent trapping was introduced, adding a high boiling solvent, such as 5–​ 20% butoxyethanol.71 The rather small proportion caused a short pre-​column to be flooded. For the optimization of the conditions it was proposed to monitor the gas flow rate by the pressure drop over a restriction in the carrier gas supply line and record the peak of the co-​solvent at high attenuation.72,73 An alternative, packing of the PTV with a strongly r­ etaining material and transfer at high gas flow rate/​low temperature, using the Through Oven Transfer Adsorption Desorption (TOTAD) interface, is outlined in Section 8.5.3. Reversed-​phase HPLC eluents often contain ions that cannot be tolerated in GC. Online extraction into an organic solvent solves this problem.74 As an alternative, ion-​pair reagents were removed from an acetonitrile–​water eluent by an anion-​exchange micromembrane device. Efficiency was found to be 99.9%.75

8.5 Interfaces for Online Transfer Numerous options exist for interfacing HPLC to GC, and variations of them have been investigated. The Dualchrom 3000 instrument from around 1990 was equipped with two interfaces, an on-​column and a loop-​type interface for partially and fully concurrent evaporation, respectively. Presently, most new instruments use the Y-​interface; fewer are equipped with a PTV interface.

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8.5.1 The Y-​interface The Y-​interface is based on gas discharge and replaces the on-​column injector previously used as interface (see below), as it was found that the latter causes a small memory effect: around 1% of the sample could be transferred to the subsequent run by being pushed backwards into the transfer line.76 The Y-​interface joins the eluent pumped into the GC system from the HPLC column with the carrier gas in a press-​fit Y-​piece located above the GC oven (Figure 8.1). The carrier gas drives the liquid into the oven-​thermostatted pre-​column, where the evaporation starts. A press-​fit Y-​piece is preferred to a screw-​type T-​piece, as it has virtually no dead volumes. The transfer conditions determine whether fully or partially concurrent evaporation is implemented. Partially concurrent evaporation is performed as with the on-​column interface. With fully concurrent evaporation, the discharged vapors are diluted with carrier gas, which enables transfer at a lower oven temperatures (see above discussion about dew point).

8.5.2 PTV Injector For LC-​GC transfer, the PTV injector is often used in the same way as for large volume injection by syringe, the syringe needle being replaced by a transfer fused silica capillary (Figure 8.2C). To retain liquid, the injector liner, commonly of around 2 mm ID, is packed with, e.g., glass wool or a more strongly retaining material, like Tenax®. Carrier gas Transfer line from transfer switching valve

GC oven Expanding sample liquid Vapor/carrier gas mixture Uncoated precolumn Figure 8.1 The Y-​interface, bringing the carrier gas and the eluent driven by the HPLC pump together in a press-​fit Y-​piece. The eluent is evaporated in an uncoated pre-​column, either concurrently with introduction or in partially concurrent mode to obtain solvent trapping.

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Transfer may be performed by solvent splitting, large-​volume CSR splitless injection, or vapor overflow. In the most commonly used solvent splitting, the carrier gas drives the vapors through the split outlet (splitting meaning the discharge of the solvent vapors through the split outlet –​the solutes are transferred in the splitless mode, Figure 8.2). Conditions are selected to achieve partially or fully concurrent solvent evaporation in the packed chamber, optimizing the injector temperature and the gas flow rate through the split outlet accordingly. Automation may be achieved with a flow-​ through syringe with a side entrance (B in Figure 8.2). Alternatively, the HPLC effluent is fed through a flow cell (A), from which the syringe of the autosampler collects the fraction of interest and injects it into the PTV device.77 The injector liner is packed with inert materials or adsorbents.78 Larger volumes were transferred by multiple injection.79 The optimum liner diameter was investigated in ref. 80. For at once (rapid) injection, larger liners (3.5 mm ID) were preferred, because up to 150 µL of eluent could be retained. For speed-​controlled (slow) injection, the narrower bore liners were preferred, as the splitless transfer into the GC column was more efficient. A

B

C From LC

Up: to GC Down: to waste

Rotary valve

Waste

From LC

Restriction Transfer line Carrier gas

Septum purge

Carrier gas

Sample liquid

Flow cell

Evaporation of sample components

Evaporation of solvent From LC

Vapor/carrier gas mixture

1

Purged solvent vapors

Cool injector

Splitless transfer

2

Split outlet closed

Heated injector

Figure 8.2 A PTV injector used as LC-​GC interface: sampling of an LC fraction from a flow-​cell by an autosampler syringe (A), directing the LC fraction into the vaporizing chamber via a flow-​through syringe (B) or rotary valve and transfer line (C). 1, Evaporation of the solvent on the packaging material and discharge of the solvent vapors through the split outlet. 2, Evaporation and splitless transfer of the analytes.

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Online transfer through the PTV injector has the advantages that the flow rate through the split exit can be regulated and that the high final injector temperature may overcome the retention power increased by non-​volatile by-​products. Wettability of surfaces is not a prerequisite to retain liquid. Desorption of labile and high boiling compounds is more difficult, however, often resulting in discrimination against them. The control of solvent evaporation in the vaporizing chamber is tedious, as the chamber is strongly cooled by the heat consumed during the vaporization process. Furthermore, the retention and evaporation of the liquid in a packed bed is not as systematically organized as on a capillary wall, rendering solvent trapping less efficient and affecting the performance for volatile sample constituents.

8.5.3 Through Oven Transfer Adsorption Desorption Interface Transfer of large volumes of RPLC eluents was performed through a modified use of a PTV injector.81,82 In the first version, the GC column was dismounted for the release of the solvent vapors at high flow rates through the open bottom of the injector. Retention of volatile solutes in the injector was kept high by a combination of the packing (Tenax®) with a low injector temperature. The process probably involved mixed evaporation and solid phase extraction into the packing of the PTV injector. No recovery data were shown. The TOTAD interface enables to automate this system.83,84 The liner of the PTV injector is packed with about 2 cm of Tenax TA® kept in place by deactivated glass wool at both ends. A fused-​silica capillary directs the eluent from the transfer valve through the GC oven to the bottom of the vaporizing chamber. Solvent evaporation takes place in the packing at, e.g., 80°C and is supported by a large helium flow discharging the vapors through a stainless steel capillary mounted at the top of the injector. After complete solvent elimination, the gas flow is reversed and the analytes are thermally desorbed in backflush onto the GC separation column.

8.5.4 Vaporizer Chamber/​Pre-​column Solvent Split Interface Large volume on-​column injection or LC-​GC transfer generally provide the most accurate results, for the volatiles as well as the high-​boiling and labile compounds. However, they also have some weaknesses. For instance, on-​column techniques are sensitive to the deposition of non-​evaporating sample by-​products that increase the retention power in the uncoated pre-​ column (ruining the retention gap effect) or introduce adsorptive material. Further, they presuppose wettability of the column inlet to retain the liquid. Vaporizing chambers retain the non-​evaporating materials and reduce their effect on chromatography by temperatures which may be higher than in the oven. Finally, retention of liquid in a packing does not presuppose wettability. This brings up the idea of combining the advantages of the two approaches.

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Sample/​solvent evaporation and solvent–​solute separation could be performed in separate compartments and optimized individually.85 Vaporizing chambers offer advantages for evaporation with regard to non-​evaporating by-​products and retention of non-​wetting liquids. The preferred techniques for solvent/​solute separation are based on chromatographic retention in pre-​ column systems, either by solvent trapping (partially concurrent evaporation) or in stationary phase (fully concurrent evaporation). The flow path in an open tubular capillary pre-​column is well defined and the vaporization process reproducibly organized. Hence the split outlet of the injector could be replaced by a pre-​column system and an early vapor exit. An RPLC system was coupled online to a GC system via the vaporizer chamber/​pre-​column solvent split/​gas discharge interface. Water-​containing eluents were driven by the LC pump into a hot 1.1-​mm ID vaporizer chamber packed with Carbofrit. The vapors were removed concurrently through a short coated pre-​column (retaining pre-​column) and released by the early vapor exit. Losses of volatile compounds were minimized by an oven temperature a little above the dew point of the eluent (the temperature at which recondensation occurs) in order to maximize the retention power of the retaining pre-​column. The method was applied to the analysis of phthalates in drinking and surface waters. The detection limits, using MS, were 5–​10 ng L–​1.86 This transfer technique was also used for the analysis of pesticides in red wine.87 While the application described above was driven by the wettability problem (aqueous eluent), a similar solution was reached for filtering out high-​ boiling or non-​volatile material: a vaporizing chamber was added upstream of the on-​column system configured for partially concurrent eluent evaporation. Such a system was investigated in detail for the retention of triglycerides in a PTV injector.96 Vaporization in the injector and transfer into the pre-​column occur concurrently due to transfer accelerated by (partial) recondensation (CSR splitless injection; Section 8.2.1.1). The added injector has little influence on the process in the pre-​column and the conditions to be used: instead of being directly introduced into the uncoated pre-​column, the solvent undergoes an intermediate vaporization step and recondenses in the uncoated pre-​ column thermostatted somewhat below its dew point. The liquid expands into the flooded zone in the same way; reconcentration by solvent trapping and the retention gap effect are also the same. Using visual experiments with perylene observed under UV, technical details were optimized, such as the release of the LC eluent into the vaporizing chamber, the packing of the chamber and the installation of the pre-​column.88 The technique was applied to comprehensive two-​dimensional analysis (LC×GC) of compounds in recycled paperboard potentially migrating into food.89 Triglycerides, present in large amounts from printing inks, prevented reaching a sufficiently low detection limit in the respective fraction and were filtered out by the vaporizer. A PTV injector was used and the desorption temperature optimized for the separation between the components of interest and the triglycerides. Subsequently, the triglycerides were heated out through the split outlet.

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8.6.1 On-​column Injector The on-​column injector was used as interface for partially concurrent eluent evaporation over more than two decades.90 A fused-​silica capillary feeds the LC eluent from the transfer switching valve through the on-​column injector into the column inlet in the same way as the syringe needle for large volume on-​column injection (Figure 8.3, left). However, it remains there permanently, with backflush through the transfer switching valve (the same as for the Y-​interface). The use of the on-​column injector offers the advantage of easy conversion to syringe injection, which is useful for method development and validation: for instance, the same standard solution as injected into LC is injected directly into GC to verify complete transfer from LC. The drawback compared to the Y-​interface is the (small) memory effect mentioned above: during transfer, wetting forces suck liquid backwards into the narrow space between the transfer capillary and the pre-​column.83 When the transfer is stopped and the transfer capillary backflushed, some of this liquid is pushed backwards into the transfer line, where the solvent evaporates and deposits the solute material. This solute material is then rinsed into the GC with the transfer of the subsequent sample. A modification of the on-​column interface was described in ref. 91: an 800 µL fraction was transferred with fully concurrent evaporation onto a 10 m × 0.53 mm ID uncoated pre-​column connected to an early vapor exit. A CO2-​ cooled cryogenic cold trap downstream of the vapor exit assembly focused the solutes. Counter flow of carrier gas from the side of the separation column (“mid-​point T assembly”) ensured that no solvent vapors entered the separation column. This technique did not find further use, presumably because it is unnecessarily complicated.

Carrier gas

From LC On-column injector

From LC Eluent driven by LC pump

Eluent driven by carrier gas

Carrier gas Loop

Vaporizing chamber

GC oven Expanding sample liquid

GC oven

Vapor/carrier gas mixture

High boiling components

Uncoated precolumn

Vapor pressure stops liquid Vapor, volatiles Uncoated precolumn

Wire Vapor pressure and wire stop liquid

GC oven Vapor, volatiles Uncoated precolumn

Figure 8.3 Three LC-​GC interfaces (simplified illustrations): on-​column interface (left), loop-​type interface (middle), wire-​interface (right).

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8.6.2 Loop-​type Interface The loop-​type interface was developed for concurrent evaporation in overflow mode.92 It consists of a switching valve incorporating a loop of an internal volume corresponding to the fraction volume to be transferred to GC (Figure 8.3, center). The LC eluent is driven through this loop to waste. When the elution of the fraction from the HPLC column is complete, the fraction is in the loop and the valve is switched. Now, the carrier gas drives the plug of liquid into the uncoated pre-​column mounted into the switching valve. The uncoated pre-​ column is of about 3 m length, accounting for occasional delayed evaporation “shooting” liquid deeper into the pre-​column. The evaporation proceeds in self-​ regulated manner. The carrier gas pushes the plug of liquid into the oven-​thermostatted uncoated pre-​column, but the liquid is stopped by the vapor pressure of the solvent. To this end, the vapor pressure (determined by the oven temperature) must be at least equal to the carrier gas inlet pressure.93 As the solvent evaporates, the plug advances until it is consumed. Then the carrier gas flow resumes and chromatography starts. Initially this interface was applied without early vapor exit. To accelerate the discharge of the solvent vapors through the column, the carrier gas pressure was increased, which was automated by a combined pressure and flow regulation: as the gas flow rate during evaporation is virtually stopped, the flow regulator automatically increases pressure to the level set by the pressure regulator behind it.100 Because there was no early exit, no volatile solute material was lost, but early eluted substances formed broad peaks, as the solvent vapors drove the first evaporated solute material deep into the separation column before the last came in. Later, the early vapor exit was introduced, which usually requires a retaining pre-​column between the uncoated pre-​column and the vapor exit. It commonly consists of a 2–​3 m × 0.32 mm ID capillary coated with the same or similar retention power (phase ratio) as the separation column.94 The vapor exit is closed just after the last solvent vapors leave the exit. Closure was automated using the pressure drop resulting from the combined flow and pressure regulation as a signal. With the early vapor exit, the most volatile solutes are lost. Potential loss can be checked in the chromatogram: because the initial band of the solutes reaching the vapor exit is as long as the retaining pre-​column, e.g., 3 m, the peaks are severely broadened. Sharp peaks indicate that there was no loss. With pentane as eluent, solutes eluted from the GC system above about 120–​140°C could be analyzed quantitatively. During transfer, solutes are advanced only by the solvent vapors (overflow), which is the minimum gas volume possible. However, the oven temperature needs to be somewhat above the boiling point at the inlet pressure (for the transfer of the heat consumed by vaporization), which reduces retention for volatiles. Even though never directly compared, performance with regard to volatiles seems inferior to that of the Y-​interface used in gas discharge (see Section 8.3.5).

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8.6.3 Wire Interface No solvent trapping is possible in concurrent eluent evaporation, but phase soaking can be exploited, i.e., increased retention by swelling of the stationary phase in the retaining pre-​column with eluent. Near the dew point, the increase in retention power easily amounts to a factor of 5, which reduces the losses through the early vapor exit and expands the applicability of concurrent eluent evaporation to more volatile analytes. At the end of solvent evaporation, this extra retention automatically collapses and releases the volatile solutes, calling for a swift closure of the vapor exit. Phase soaking cannot be exploited with the loop-​type interface, because oven temperatures must be significantly above the dew point of the eluent to transfer the heat consumed by solvent evaporation from the oven atmosphere through the capillary wall to the evaporation site. However, evaporation in a separate chamber kept at a higher temperature than the oven enables transfer at oven temperatures close to the dew point and activates phase soaking. Various types of vaporizing chambers have been investigated visually, primarily with regard to “shooting” liquid by violent evaporation, flooding of the chamber by cooling and pressure shocks pushing vapors backwards into the carrier gas supply. High temperatures are needed for the transfer of the heat consumed by evaporation. Fused silica capillaries into which a tightly fitting piece of polyimide-​free fused silica capillary was inserted were suitable for easy solvents, such as alkanes, but packed beds were needed to achieve smooth evaporation of more difficult solvents like dichloromethane or methanol–​water mixtures.95 Using the wire interface, the LC pump drives the fraction into an uncoated capillary mounted in the transfer valve. This capillary is introduced into the GC oven through a heated block, such as a detector base block or vaporizing injector. To prevent “shooting” liquid, a piece of stainless steel wire closely fitting into the capillary (e.g., of 0.22 mm OD in a 0.32-​mm ID capillary) is inserted in the heated region (Figure 8.3, right).96 Inside the oven, the capillary was usually connected to an uncoated and a coated pre-​column, but a retaining pre-​column (usually 3 m × 0.32 mm ID) is sufficient if there is no “shooting” liquid spreading solutes. The technique proceeds in overflow mode: during transfer, the carrier gas is interrupted by a valve and the liquid forwarded by the LC pump. To keep the pressure drop to the solvent vapor exit low, 0.53-​mm ID pre-​columns were used. For a 500-​µL LC fraction in pentane introduced at 400 µL min–​1, a well-​ optimized oven temperature (48°C) enabled complete recovery of n-​dodecane, which is not possible with the loop-​type interface. The vaporizing chamber has some (although limited) capacity to retain involatile material, which prevents contamination of the oven-​thermostatted pre-​column. No problems with adsorption or catalytic activity of the metal surface were encountered. Cortes used a similar introduction technique, pumping small LC fractions (from packed capillary columns) into the GC instrument while the carrier gas was stopped, although without wire to prevent “shooting” liquid.16 Depending

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on the oven temperature related to the dew point of the solvent, transfer occurred in concurrent evaporation/​overflow mode or by the conventional retention gap technique (no partially concurrent evaporation).

8.6.4 Swing Interface The swing interface was proposed as a technique for introducing large volumes of water-​containing liquids.97 Vaporization of the transferred HPLC fraction and solvent–​solute separation are performed in separate compartments, as this enables independent optimization of the two steps. Vaporization occurs in a permanently hot chamber packed with, e.g., Carbofrit. The vapors are released through a retaining pre-​column and a second injector that is of PTV type (Figure 8.4), initially kept at a temperature just high enough to prevent solvent recondensation. The analytes are separated from the solvent vapors in a cascade of increasing retention power: high boiling solutes are retained in the oven-​thermostatted retaining pre-​column, the more volatile components by a packed bed of several sorbents of increasing retention power situated in the PTV injector. For elution, the gas flow is introduced from the PTV injector and the PTV injector is heated up to desorb the solutes from the heated packed bed through the retaining pre-​column into the separation column. Introducing 500 µL water : methanol 1 : 1, methyl esters from the C9 fatty acid could be quantitatively analyzed.105

Carrier gas 1

Transfer line

Carrier gas 2

Vaporizing chamber packed with Carbofrit permanently hot

SVE

Packed trap top: increased retention power

Retaining precolumn Separation column

Figure 8.4 Swing interface for non-​wetting LC eluents : the fraction is vaporized in a hot chamber (left) and the vapors released through a cool PTV-​type injector shown on the right. The solutes are then discharged into the separation column by carrier gas introduced into the second injector and heating this chamber.

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8.7 LC-​GC Instrumentation An LC-​GC instrument with a Y-​interface is schematically shown in Figure 8.5. An autosampler injecting up to several 100 µL into the LC system is needed, preferably also suitable for direct GC injection for method development (checking the recovery of the pretreatment). For injection into the GC system, the injection rate should be adjustable to be similar to the transfer rate. Current autosamplers may be additionally used as a robot for sample pretreatments, such as automated extraction or epoxidation as applied for the determination of mineral oil aromatic hydrocarbons in food.98 The HPLC column is installed in a backflush valve to enable cleaning of the column by a stronger solvent in reversed flow direction. An auxiliary valve may be useful for a more complex pre-​separation, such as LC-​LC column switching. Preferably, there are three HPLC pumps, two for an eluent gradient and one for the backflush solvent. The pumps must be suitable to accurately deliver eluent at low flow rates (50–​500 µL min–​1), including volatile solvents, such as pentane or the often even more volatile pentane mixtures with modifiers. Some instruments use syringe pumps for this reason. Internal dead volumes should be small to avoid delays in the eluent gradient at low flow rates. If no pump for the backflush solvent is available, this solvent can also be introduced through a loop constantly filled from a pressurized solvent container and a restriction regulating the flow rate.98 Usually a UV detector is installed to monitor the elution from the HPLC column. It should have a minimum dead volume in the connections and the detector cell in order to avoid band broadening and delays. Also, transfer tubing should be short and of narrow bore, even though capillary tubing below 0.25-​mm ID did not prove suitable because of frequent plugging.

Waste

FID

Vapor exit

Carrier gas

UV detector

tv

Y-piece

bfv

aux

iv

LC column

Autosampler

HPLC pumps

Separation column

Uncoated precolumn

a

b

c

iv: injection valve bfv: backflush valve aux: auxiliary valve tv: transfer valve

Figure 8.5 Basic version of an LC-​GC instrument equipped with a Y-​interface and a long uncoated pre-​column for the retention gap technique.

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The Dualchrom 3000 instrument (Carlo Erba, designed around 1990) had a feature called “peak detection”: to further improve the accuracy of the fraction window and render it less dependent on drifts in HPLC retention times, it could be programmed so that the fractionation started with a predetermined delay on a peak detected shortly before. This feature (e.g., used for an application mentioned in Section 8.10.7) might no longer be available. The transfer valve directs the eluent either to waste or to GC. After the transfer is completed, it switches the transfer line to a restriction providing a small flow of carrier gas rinsing this line outwards. The flow rate is regulated by a fused-​silica capillary (e.g., 50 cm × 0.05 mm ID) or a needle valve (capillaries are preferred because the small flow rates are difficult to measure). The gas chromatograph needs ducts for the vapor exit and the pre-​column to join the Y-​piece outside the oven. It should also be equipped with injectors allowing on-​column injection (simulating on-​column transfer for method development) and vaporizing transfer (used as vaporizing chamber/​ pre-​ column solvent splitting interface). A PTV injector with the option of an on-​ column insert combines both functions. For the parallel analysis of two fractions from the same LC run, such as of saturated and aromatic mineral oil hydrocarbons (MOSH and MOAH), some GCs are equipped with two pre-​column/​column systems and two detectors. The T-​piece connecting to the vapor exit may consist of a press-​fit Y-​piece. Dead volumes of metal T-​pieces are critical. The combination of a 0.53-​mm ID pre-​column with a 0.25-​mm ID separation column allows insertion of the separation column into the pre-​column by a few millimeters (it should not enter further, as this becomes the main resistance against the gas/​vapor flow rate during transfer). This involves a metallic T-​piece with ferrules and screws. The valve of the early vapor exit must not contain plastic parts that are swollen by solvents such as dichloromethane and should be warmed to prevent solvent recondensation. In the off position, the valve is switched to a restriction capillary providing a small purge flow, preventing solvent vapors expanding backwards into the separation column (resulting in an elevated, disturbed baseline). The flow rate must be as low as possible, because during analysis the gas contains sample material and the split ratio at the T-​piece changes with programmed oven temperature (reduced column flow at constant inlet pressure), resulting in discrimination against solutes eluted at high temperatures. The restriction usually consists of a 50–​100 cm × 0.05 mm ID fused-​ silica capillary; a smaller bore, such as 0.025-​mm ID, would enable further reduction of the flow rate, but risks being plugged by condensed solvent. The latter can be avoided by warming the resistance capillary, for instance in an aluminum cage warmed by the valve.

8.8 HPLC Pre-​separation The pre-​separation in the liquid phase is restricted to eluents suitable for GC. It requires the absence of salts and for most transfer systems also of water,

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which normally means using normal-​phase HPLC. The fraction volumes need to be such that they can be handled for transfer by the interface used. Normally, HPLC columns of 2-​mm ID are used, as fraction volumes are typically between 200 and 1 000 µL. The eluent flow rates are in the order of a few 100 µL min–​1, which is in the range of the evaporation rates with 0.53-​mm ID pre-​columns and an early vapor exit. Many NPLC-​GC methods developed and routinely used so far are in the food area and mostly have to cope with substantial amounts of lipids, or deal with fats and oils themselves. As they target minor components and mostly use FID (due to the lack of standards for calibration of the response), sensitivity is often an issue, which means injecting as much sample material (including triglycerides) as possible. In most applications, the solutes of interest are eluted before the triglycerides. Only a limited proportion of the column (perhaps half) can be loaded by the triglycerides, because the part retaining the triglycerides is largely deactivated and the pre-​separation almost exclusively occurs in the remaining part. The capacity of silica gel columns, particularly the column volume flooded by the triglycerides, was determined experimentally and depends on the mobile phase.99 Using a minor amount of dichloromethane as modifier of pentane or hexane, usually 20 mg fat or oil was injected into a 25 cm × 2 mm ID silica gel column, estimating that approximately a third of the column served for retaining the triglycerides. The capacity tends to be lower for derivatized silica gels. Isolation of trace components eluted immediately after the triglycerides proved more tedious, since large peaks tend to tail in HPLC: an amount of triglycerides is eluted with a delay that still produces massive peaks in the GC systems or even overloads them to the extent that the peaks of the solutes of interest are distorted. Part of this tailing is due to the injector, switching valves and the connections to the column. It can be reduced by backflushing this part.100 The use of size exclusion chromatography (SEC) as a pre-​separation step is described in Sections 8.10.12 and 8.10.17. As mentioned above, a number of approaches of coupling RPLC to GC have been shown to be feasible, but the authors are not aware of such a method having become routine.

8.9 Summarized Description of the Two Preferred Transfer Techniques 8.9.1 Partially Concurrent Evaporation with the Y-​interface For samples with constituents of interest eluted at or near the oven temperature during transfer, solvent trapping is a prerequisite, which entails condensed eluent in an uncoated pre-​column. Usually conditions are selected that provide a substantial proportion of concurrent evaporation in order to reduce the amount of liquid to be retained by the pre-​column or shorten the pre-​column.

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Figure 8.6 shows the three key steps of the process schematically. The eluent is introduced into an uncoated pre-​column and forms waves and plugs driven forward by the carrier gas and the vapors, eventually spreading the liquid as a layer on the capillary wall. (1) The eluent evaporates at the rear of the layer, saturating the carrier gas; the vapors are largely driven out of the vapor exit. (2) As the evaporation proceeds, components of low volatility are deposited onto the dry pre-​column wall. The volatiles evaporate together with the eluent, but are retained again by the liquid ahead. Shortly before the last eluent is vaporized, the vapor exit is closed, leaving a small purge flow. The rest of the eluent is discharged through the separation column. The volatiles are released at the time and from the spot when/​where eluent evaporation is completed and start chromatography in the separation column as sharp bands. (3) Upon increase of the column temperature, higher-​boiling substances migrate through the pre-​column, but are stopped again at the entrance of the coated column by the retention power of the stationary phase, where they wait until the temperature is further increased and enables their chromatography (roughly 100°C above the temperature allowing migration through the uncoated pre-​column). Usually 0.53-​mm ID uncoated pre-​columns are used to increase the internal surface per length that retains liquid as well as the flow rate discharging solvent vapors. Furthermore, they decrease the pressure drop to the vapor exit: a strong pressure drop promotes eluent evaporation toward the front of the eluent layer and compromises solvent trapping. Pre-​columns of 7–​10 m are suitable for the transfer of 300 to at least 1 000 µL. Eluents are mostly based on hexane with modifiers, as pentane would require inconveniently low oven temperatures. Typical flow rates are 200–​300 µL min–​1. Evaporation conditions are selected considering the required proportion of concurrently evaporating solvent. For instance, the capacity to retain liquid of a 10 m × 0.53 mm ID uncoated pre-​column used at the gas flow rates typical with an early vapor exit is around 200–​250 µL.55 For fractions of up to this volume, any conditions not resulting in fully concurrent evaporation are suitable, including transfer rates far exceeding the evaporation rate. If this volume is exceeded, the transfer rate and the evaporation rate must get closer to each other, whereby the larger the fraction, the larger must be the proportion of eluent evaporated during transfer. The minimum evaporation rate is derived from the proportion of eluent that must be evaporated concurrently with the transfer to avoid overloading of the pre-​column. For instance, for a fraction of 1 000 µL around 80% of the eluent must evaporate concurrently, which is still readily feasible. First, the evaporation rate is determined, most conveniently by a relatively rapid introduction of a volume of eluent not overloading the pre-​column. The carrier gas/​vapors leaving the vapor exit are ignited and the time is measured up to when the flame turns from bright yellow to almost invisible/​slightly blueish (hydrogen as carrier gas) or goes out (helium). The volume introduced divided by the period of yellow flame equates the evaporation rate. Now the LC flow rate may need to be adjusted to the evaporation rate or the GC conditions (inlet pressure during transfer and/​or oven temperature)

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Carrier gas

Solvent vapor exit

Uncoated precolumn

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Solvent vapor 1

Separation column

Volatile solutes retained by solvent trapping Closure of vapor exit

2

Restriction

High boiling components spread over whole flooded zone

Increasing oven temperature

3

Reconcentration of high boiling components by the retention gap effect

Figure 8.6 The main steps of LC-​GC transfer by partially concurrent eluent evaporation in an uncoated pre-​column: (1) eluent spreading into the pre-​column with some concurrent evaporation; (2) moment for closing the vapor exit: the last portion of eluent evaporates and releases the volatile analytes, whereas high-​boiling material remains at least partially spread on the dry pre-​column wall; (3) upon increase of the oven temperature, the high-​boiling constituents move through the uncoated pre-​column and are reconcentrated at the entrance of the separation column.

adjusted to get the evaporation rate closer to the LC flow rate. In the next step, an eluent volume corresponding to the LC-​fraction of interest is transferred at the intended flow rate and the duration of the yellow flame measured. This duration should somewhat exceed the duration of the transfer to confirm remaining condensed eluent that performs solvent trapping. If the pre-​column is overloaded, this would be visible by liquid sputtering into the flame. The duration between the end of the transfer and the extinction of the yellow flame as a proportion of the total duration of the yellow flame can be used as an indication of the proportion of flooding eluent (eluent not evaporated concurrently). The closure of the vapor exit is then programmed 1–​2 s before the flame is out. Also the width of the solvent peak in GC may be used as an indicator of the appropriateness of the transfer conditions. Usually it should be in the order of 2–​3 min. Narrower ones may be the result of fully concurrent evaporation or late closure of the vapor exit, i.e., a loss of solvent trapping, whereas broader solvent peaks indicate too early closure of the vapor exit and too much solvent discharged through the column. Additionally, flooding into the separation

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column causes broad solvent peaks, but it is recognized by solute peaks being distorted by spreaded initial bands. Programming of the oven temperature may start at the end of eluent evaporation. When the uncoated pre-​column is relatively large, the solutes must be provided the time for the transfer to the separation column at a low enough temperature to achieve efficient reconcentration at the entrance of the separation column. Otherwise peak broadening or distortion is observed.101 It means avoiding high program rates and low gas velocity in the pre-​column. Gas velocities in the pre-​column are low when the inlet pressure is high, because the compressed gas moves slowly. Inlet pressures are particularly high when helium is used as carrier gas. At this point, a discussion held in the late 1990s should be mentioned. Initially, the pre-​column system was commonly composed of a 0.53-​mm ID uncoated pre-​column (e.g., 10 m long) and an approximately 3 m × 0.32 mm ID capillary coated as or similar to the separation column. This “retaining pre-​ column” installed between the uncoated pre-​column and the solvent vapor exit was thought to enable closing the vapor exit slightly after solvent evaporation is completed, hence temporarily retaining volatile solutes in the stationary phase (swollen by phase soaking). However, this is neither of substantial effectiveness at the high gas velocities with the open vapor exit, nor really necessary. As a drawback, the 0.32-​mm ID capillary acts as a restriction, slowing the vapor discharge and, hence, the evaporation rate. The situation did not really seem to be as simple as that. Basically, solvent trapping should retain the volatiles up to the last moment of solvent evaporation, and closure of the exit 1–​2 s before the end of solvent evaporation should prevent losses. However, sometimes losses were noted. Use of a “pre-​solvent” was proposed, i.e., 30–​50 µL solvent introduced ahead of the LC fraction to form a layer performing solvent trapping for solutes potentially escaping from the layer of the HPLC fraction.102 This prompted a deeper investigation of the solvent trapping process in the uncoated pre-​column and the usefulness of a retaining pre-​column.103,104 It was concluded that the losses that had sometimes been observed were due to the pressure drop across the flooded zone in the uncoated pre-​column, causing some solvent to evaporate at its front end. It was not the stationary phase, but the smaller internal diameter of the retaining pre-​column that helped reducing these losses by reducing the pressure drop over the flooded zone. The usefulness of a “pre-​solvent” seems questionable because the liquid is mixed in the pre-​column, with the HPLC fraction largely “running over” the layer of “pre-​solvent”. Today retaining pre-​columns are no longer used, because the losses are small, if significant at all.

8.9.2 Concurrent Eluent Evaporation with the Y-​interface For concurrent eluent evaporation with gas discharge, using the Y-​interface, the evaporation rate in the pre-​column must at least slightly exceed the transfer rate (Figure 8.7). In critical applications including rather volatile solutes,

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the two rates should be close together, as it is the volume of carrier gas and vapors that drives the volatile components through the pre-​column and eventually through the vapor exit. Often eluents are based on pentane with modifiers such as dichloromethane or methyl tert-​butyl ether (both resulting in azeotropic evaporation at a temperature clearly below the pentane boiling point). This enables transfer at low oven temperatures and elevated inlet pressures (increasing the required oven temperature), which improves the amenability of concurrent evaporation to the analysis of volatiles and accelerates transfer. The pre-​column is the evaporation chamber and needs to be long enough to transfer the heat, consumed by solvent evaporation, from the oven into the pre-​column at the given temperature gradient across the capillary wall. The evaporation zone is at a temperature below the oven temperature, with the difference being smaller the longer is the flooded zone (or capillary surface area through which the heat is transferred). A pre-​column of around 1 m × 0.53 mm ID is recommended if the oven temperature is adjusted close to the minimum necessary. Retention in the pre-​column can be optimized by a thin film of stationary phase. As there may be some spreading liquid, but also because the pre-​ column is commonly wider than the separation column (such as 0.53 versus 0.25 mm ID), some reconcentration at the entrance of the separation column (retention gap effect) is necessary, as achieved by a phase ratio (proportional to column diameter and inversely proportional to film thickness) corresponding to about three times of that in the separation column. If wettability is critical, e.g., using dichloromethane or methyl tert-​butyl ether on dimethyl polysiloxane, a stationary phase providing better wettability can be chosen, such as OV-​1701 (7% cyano, 7% phenyl dimethyl polysiloxane). The stationary phase needs to be well immobilized (often called “bonded”) to avoid stripping by moving liquid. As an alternative, a short uncoated pre-​column is combined with a retaining pre-​column with an optimum phase ratio similar to the separation column (approximately doubled film thickness if a 0.53-​mm ID pre-​ column is combined with a 0.25-​mm ID separation column). Evaporation rates typically amount to several 100 µL min–​1, as the pre-​ column system can be short, consisting either of a single slightly coated piece (30–​100 cm × 0.53 mm ID) or a combination of an uncoated with a coated pre-​ column (about 2 m × 0.53 mm ID). Gas flow rates leaving the vapor exit are in the order of 200–​500 mL min–​1, possibly influenced by the restriction in the T-​piece to the vapor exit (for instance, the distance the separation column is inserted into the pre-​column). This is suitable for the transfer of HPLC eluent at around 300 µL min–​1. The evaporation rate cannot be determined by a rapid injection of eluent as easily as with partially concurrent evaporation, because the pre-​column is too short to retain an amount of eluent reflecting the transfer situation. Instead, the duration of the yellow flame after stopping transfer may be used. Starting with transfer of a few 100 µL at a rate estimated to be below the evaporation rate, it is observed that the yellow flame is out almost immediately

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Carrier gas

Solvent vapor exit

Coated precolumn

Solvent vapor + volatiles 1

High boiling components remain at evaporation site Closure of vapor exit after complete solvent evaporation

Separation column

Restriction

2 Figure 8.7 Fully concurrent eluent evaporation in a 0.53-​mm ID pre-​column with a thin coating. The vapor exit is closed 1–​2 s after the end of solvent evaporation.

after stopping transfer. Then the transfer rate is increased stepwise until a significant delay in the extinction of the flame is noted. When the rate further exceeds the evaporation rate, liquid driven out of the exit will cause the flame to be disturbed and flickering. This point should be avoided, if the stationary phase in the separation column is not immobilized, as some liquid is likely to be driven into the separation column. Closure of the vapor exit occurs shortly (1–​2 s) after the end of eluent evaporation (Figure 8.7). The moment is not really critical, because with some more seconds of delay little solute material is lost that was not already lost during solvent evaporation.

8.10 Applications An overview on the various LC-​GC methods developed in the past is given in the form of short summaries, referring to the literature for details. The selection was guided by applications of broader interest or with interesting ideas and technical solutions.

8.10.1 Mineral Hydrocarbons in Food and Related Samples Presently the most widely used LC-​GC method determines the sum of or fractions of the mineral oil saturated hydrocarbons (MOSH) and the mineral oil aromatic hydrocarbons (MOAH).105,106 The HPLC step is used to isolate the MOSH and the MOAH fractions from all other sample constituents, such as lipids. Separation of these classes of compounds is achieved on silica gel with a high internal surface area (small pores), using standards marking the edges of the fractions. The MOAH are focused in a fraction of about 450 µL by

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applying a steep gradient (30% dichloromethane). Transfer mostly occurs by partially concurrent evaporation using the Y-​interface, but also a rapid MOSH analysis in vegetable oils was described using the PTV interface with solvent splitting.107 The performance of the two interfaces was compared and no difference was found.108 Both MOSH and MOAH fractions mainly form broad humps, with two consequences: a clean baseline is needed for quantification and sensitivity is relatively low. Mostly FID is used, which is of modest sensitivity, but enables quantitation without the need for calibration of response. The detection limit is around 1 mg kg–​1 food. A great amount literature on the occurrence of MOSH and MOAH was reviewed in ref. 109. The method was also used in animal experiments,110,111 and for the determination of mineral hydrocarbons in human tissues.112,113 For certain applications, n-​alkanes of plant origin with more than 20 carbon atoms (part of the leaf waxes) must be removed to enable the determination of the MOSH. This was achieved by inserting a column packed with activated aluminum oxide into the online system, between the silica gel column and the transfer valve, using n-​hexane as eluent.114 This column must not come into contact with even only slightly polar solvents, because it would be deactivated. The retained n-​alkanes can be removed in backflush with isooctane. The method for MOSH is also suitable for the analysis of oligomers from polyolefins115 and hot melts.116 For the separation of saturated and monounsaturated olefins, a small amount of silver nitrate was added to the silica gel.117 In 1997, a complex LC-​GC method was developed to determine the MOAH content in foods, particularly those rich in fat, and to pre-​separate them according to ring number.118 The first step isolated the MOAH from the lipids, the MOSH and other food constituents. To achieve sufficient sensitivity, up to 200 mg of fat or oil (diluted to 500 µL with hexane) was injected into a silica gel column of sufficient capacity to retain the lipids in the first part (25 cm × 4.6 mm ID). The MOAH were eluted with 6 mL pentane/​10% dichloromethane at 800 µL min–​1. Because neither this fraction volume nor the composition of the eluent (dichloromethane) were suitable for the next steps, the eluent was removed in a small on-​line evaporator incorporating a 5 cm × 1 mm ID steel tube packed with gently silylated silica gel kept at 40°C.119 Evaporation was concurrent with transfer, applying vacuum at the outlet. While the first HPLC column was backflushed, the reconcentrated MOAH were transferred to the second column, of dimensions 10 cm × 4.6 mm ID, packed with an amino-​ derivatized silica gel. Fractionation according to number of aromatic rings, using pentane at 600 µL min–​1, was monitored by UV at 254 nm. Up to 1600 µL were transferred to GC by concurrent eluent evaporation. Today, similar results could be obtained by LC-​GC×GC.120

8.10.2 Analysis of Mineral Oil Products In 1985, before solvent vapor exits were used, gasoline was separated into saturated and aromatic hydrocarbons using a 10 cm × 2 mm ID silica gel column

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–​1

and pentane at a flow of 50 µL min . The saturates were transferred in the forward mode and the aromatics subjected to backflush. Fractions of 95 and 100 µL in volume, respectively, were introduced into the GC system by the retention gap technique.121 The method was updated for fuels and further elaborated to achieve separation of MOAH by ring number on an amino silicate column.122 A review on the LC-​GC analysis of fuels can be found in ref. 123. The (largely alkylated) MOAH in batching oil for jute bags, a rather crude mineral oil product, were separated by ring number on an amino silica columns and transferred to GC by concurrent evaporation using the loop-​type interface.124 A similar analysis, including more volatile constituents, was described using partially concurrent evaporation.125 Sulfur-​ containing compounds in mineral oil were determined by pre-​ separation on a 25 cm × 2 mm ID amino silica column, using n-​heptane as mobile phase, followed by transfer to GC with sulfur chemiluminescence detection by partially concurrent evaporation.126 An extremely complex technique was applied for the characterization of hydrocarbon mixtures.127 In a first step, SEC was used for the removal of high molecular mass material. As the mobile phase, tetrahydrofuran, was not suitable for the subsequent normal-​phase HPLC step and the fraction volume was too large, the eluent was evaporated in a capillary pre-​column system in a GC oven using partially concurrent evaporation with a co-​solvent (4% n-​decane, selected to not disturb the subsequent HPLC separation). The solutes were then cold-​trapped at the outlet and fed into the HPLC system, from where fractions were subjected to GC analysis using partially concurrent evaporation. The system was also used for the analysis of additives in polymers,128 and advocated for other applications to avoid GC contamination with non-​ evaporating sample materials.

8.10.3 Environmental Contaminants In 1987, an LC-​GC-​ECD application was described that determined the sum of the polychlorinated biphenyls (PCBs) in fish using the on-​column interface and the retention gap technique without vapor exit.129 An LC-​GC-​ECD method for single PCB congeners used concurrent evaporation with the loop-​type interface.130 Non-​, mono-​and di-​ortho-​substituted PCBs were analyzed in human blood plasma. Non-​ortho-​components were isolated from the bulk of the PCBs on a dinitroanilino-​propyl silica column and transferred to a GC-​MS instrument by concurrent evaporation. The online coupling served to lower the detection limit and partly automate the sample clean-​up step.131 The presence of polychlorinated biphenyls, dibenzodioxins and dibenzofurans in fly ash was investigated using a 40 cm × 0.32 mm ID packed capillary HPLC column at a flow rate of 12 µL min–​1. Transfer to a GC-​MS instrument was performed by using concurrent co-​solvent evaporation using a loop-​type interface and a 3 m × 0.32 mm ID uncoated pre-​column.132 Polychlorinated biphenyls and dibenzofurans were isolated through HPLC by using two

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15 cm × 4 mm ID RP18 columns in series, which enabled the detection by low-​resolution  MS.133 A method for the analysis of polycyclic aromatic hydrocarbons (PAHs) in vegetable oils involved pre-​separation on a 10 cm × 2 mm ID silica gel column, pentane/​2% MTBE and transfer through the loop-​type interface (300 µL). Detection was by MS. The pressure profile in the ion source during transfer was measured by the interface transducer. Because the vacuum seemed to collapse during passage of the solvent vapors, it was recommended to use a narrow-​bore capillary in the GC-​MS interface.134 However, the authors did not take into account that the device measuring the vacuum is calibrated for carrier gas rather than solvent vapors and the pressure might even have decreased. Hence, unless the mass spectrometer automatically shuts down, there is no concern. Alkylated, oxygenated and nitrated PAHs were analyzed in urban air particulate matter using LC-​ GC-​ MS and transfer with partially concurrent evaporation.135 An LC-​GC method for the analysis of polycyclic aromatic nitrogen heterocyclics (PANHs) from extracts of personal samplers trapping particulate matter used a 5 cm × 4 mm ID dimethyl aminopropyl silica column and concurrent eluent evaporation with the loop-​type interface. At the start of the elution of the carbazoles, the column was backflushed to get a broad range of solutes into a relatively small fraction.136 This presupposed an initial removal of more polar constituents by SPE. Compared to corresponding off-​line methods, sensitivity was increased by a factor of 50–​100 and the analysis time was greatly shortened. Other LC-​GC-​MS methods have been described for the determination of PAHs in urban dust and diesel particulate matter.137,138

8.10.4 Determination of Food Irradiation Foods are irradiated for microbial decontamination and to prevent sprouting. The analytical detection of irradiation may be of interest for control, as irradiation must be labeled. The detection is often based on transformation products of lipids, such as olefins cleaved from the glycerides by loss of one or two carbon atoms, in the latter case with the introduction of an additional double bond (analogous to the McLafferty rearrangement).139 Hence, a double bond may be added to those already present in the fatty acids, resulting in olefins with up to four double bonds. In 1989, an LC-​GC method was developed comprising all hydrocarbons formed by irradiation, using a silica gel column of low retention power with hexane/​3% methyl tert-​butyl ether to combine the hydrocarbons into a fraction of 200 µL. Transfer was performed by partially concurrent eluent evaporation with the on-​column interface.140 As the samples frequently contained interfering hydrocarbons, particularly MOSH, a method was developed to selectively detect the dienes as markers.141 To maintain the selectivity of the separation, two HPLC columns were used: on a first 10 × 2 mm ID silica gel column of low retention power, all hydrocarbons

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were isolated from the raw extracts. They were transferred to a 25 cm × 2 mm ID column packed with a silica gel of high retention power (Lichrospher 60). Only the first column was backflushed in order to preserve the high retention power of the second. As a direct UV detection of the dienes was not possible, the edges of their fraction were marked by 1-​hexene and 1,5-​hexadiene, which could be added in large amounts, as they were eluted from GC together with the solvent. The mobile phase was hexane. Using optimized conditions, the two main components, hexadecadiene (from oleic acid) and heptadecadiene (from linoleic acid), could be transferred using concurrent eluent evaporation with the loop-​type interface. This enabled selective detection even in foods heavily contaminated with MOSH, such as fish. Detection of radiation-​induced hydrocarbons in sponge cake prepared with irradiated egg was disturbed by fat-​associated compounds. Online LC-​GC provided the separation efficiency to remove these interferences as well as the low detection limit required.142

8.10.5 Sterenes in Edible Oils Sterenes are dehydroxylated sterols formed upon raffination of edible oils. They are analyzed to detect the raffination of oils declared as unrefined, but also for the detection of cheaper oils added to expensive oils, such as seed oils in extra-​virgin olive oils (admixed oils must be refined to remove their flavor). The composition of the sterenes is indicative of the added oil.143,144 Sterenes are C28 or C29 tetracyclic hydrocarbons with 2–​3 double bonds. Their isolation from oils directly injected into the HPLC column is similar to that of olefins in extracts of irradiated foods: using silica gel columns with a strong retention of olefins (small pore size) and pentane or hexane as eluent, they are eluted after the saturated hydrocarbons, but clearly before the lipids. Owing to their elution from the GC column at around 250°C, transfer by concurrent evaporation does not even require well-​optimized conditions. Sterenes form complex mixtures. Because mass spectra are of modest selectivity and hardly any standards are commercially available, pre-​separation by number and position of double bonds is of interest. The elution of different sterene types can be monitored in LC using UV detection at 235 nm.145

8.10.6 Sterols in the Unsaponifiable Fraction of Edible Oils Sterols are widely analyzed for the control of the authenticity of edible oils. The standard methods involve saponification of esters to remove the glycerol esters and to liberate the sterols esterified with fatty acids. From the extract of the soap solution, the sterols are isolated by preparative thin layer chromatography (TLC) or SPE.146 As the standard methods are labor-​and solvent-​intensive, an alternative method providing the same results was developed.147 The extraction from a soap solution is avoided, as it is not only tedious, but also an important source of inaccuracy. To this end, oils and fats are transesterified instead of

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saponified (in 15 min at room temperature), liberating the sterols from their esters. The fatty acid methyl esters are co-​extracted with the sterols, but separated from them in HPLC, with this replacing preparative TLC or SPE. An HPLC process on a 25 cm × 2 mm ID silica gel column, with hexane/​ 1% isopropanol as eluent, separates the different types of sterols, not only the dimethyl from the methyl and desmethyl sterols, but also the Δ5 from the Δ7 sterols. By multiple transfer (loop-​type interface), several types can be analyzed from the same HPLC pre-​separation. The relative standard deviation of the results is reduced to a few percent, which is not even achievable by conventional GC injection alone.163 The LC-​GC-​FID method was validated for olive oils.148 It was applied for the determination of vegetable oils and fats added to milk fat, using β-​sitosterol as marker.149 Separate analysis of the fatty alcohols and sterols in olive oil from the same HPLC pre-​separation was achieved filling successively two sample loops in a loop-​type interface.150

8.10.7 Isomerization of Δ7 sterols The following analysis is reported as an example of chromatography driven to high performance for HPLC pre-​separation, adjustment of the transfer window and GC analysis. It deals with isomers of sterols providing unspecific mass spectra and not being available as standards, i.e., requiring FID for quantitative determinations using other hydrocarbons for response calibration. High oleic acid sunflower oil was used for adulterating olive oils due to the similar fatty acid composition. With the intention to render it “invisible”, it may be desterolized. In this process, Δ5 sterols are dehydrated to sterenes, but Δ7-​sterols are isomerized to Δ8(14)-​sterols.151 Using a 25 cm × 2.1 mm ID silica gel column, Δ8(14)-​stigmastenol, the isomerization product of Δ7-​stigmastenol was eluted between Δ5-​stigmastenol (β-​sitosterol) and Δ7-​stigmastenol with a just about complete separation. When using an OV-​61 (33% phenyl) GC column, Δ8(14)-​stigmastenol was eluted just after Δ5-​stigmastenol. As Δ5-​stigmastenol is present in far larger amounts than Δ8(14)-​stigmastenol, it tends to overload the GC column, obscuring the peak of interest, which is why the transfer window must be accurate. This was achieved in “peak detection” mode: as the absolute retention time in HPLC was not sufficiently stable, the transfer window was regulated relative to the peak of the Δ5-​sterols (mainly Δ5-​stigmastenol) that was eluted shortly before; specifically, transfer was automatically started with a delay on the onset of this peak.

8.10.8 Minor Components in Edible Oils The so-​called “minor components” in edible oils, mainly fatty alcohols, wax esters, tocopherols, sterols and sterol esters, are a rich source of information on the type of oil, as well as on its quality.152,153 The minor components vary strongly in polarity; in HPLC, they form a broad window, ranging from before to clearly after the di-​and triglycerides. To enable their separation from the

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glycerol esters, the polar analytes must be derivatized to be eluted together with the compounds of low polarity and before the triglycerides. In a first method, the alcohols were esterified with pivalic acid (2,2-​dimethyl propionic acid), efficiently shielding the resulting ester function.154 Such an approach enabled the incorporation of all constituents in a 750-​µL HPLC fraction. Being of low volatility, transfer by concurrent evaporation is robust. Transferring a 1-​mL volume (from a 10 cm × 4.6 mm ID HPLC column) resulted in relative standard deviations below 5%,155 substantially better than achievable with alternative (manual) methods. Later, acylation was replaced by silylation.156 The concern that injection of a massive amount of silylation reagent into a silica gel column would cause its selectivity to be lost turned out to be unfounded. The same method was used for the distinction of Robusta and Arabica coffee through the content of Δ5-​avenasterol and 16-​O-​methylcafestol157 as well as for the analysis of the sterol esters in cocoa butter.158 Tocopherols in margarine were analyzed injecting margarine solutions into a 10 cm × 2.1 mm ID silica gel column followed by transfer to GC using the loop-​type interface with concurrent evaporation.159 The concentration of free and esterified sterols in vegetable oil fatty acid methyl esters used as diesel fuel was determined after silylation of the sample, LC pre-​separation and transfer by concurrent eluent evaporation.160

8.10.9 Methyl-​, Ethyl-​and Wax Esters in Olive Oil The determination of wax esters as well as methyl-​and ethyl esters of fatty acids in olive oils combines two aspects of interest dealing with the quality of the olives used for the production of olive oils.161 Waxes consisting of long-​ chain alcohols esterified with fatty acids are located in the skin of the olives. From a fresh, rather hard olive, low amounts of these waxes are extracted into the oil, but the extractability increases when the olives get softer and particularly when they are overripe. High wax ester contents may, therefore, stand for mild oils (mature olives), but also for deficient oils (degrading olives). Still far higher contents are found in the solvent-​extracted oils from pomace (“sansa” oil). Among the wax esters in the range of C38 to C46, those with straight-​chain alcohols have to be distinguished from terpene esters, primarily fatty acid esters of phytol and geranyl geraniol, as the latter are inside the olive fruit and not indicative of extraction from the skin.162,163 Ethyl esters are formed from ethanol generated by fermentation. Fermentation occurs in degraded olives, such as olives attacked by insects. Methanol originates from metabolized pectin upon collapsing cell compartmentation in overripe fruits. During processing of the olives, these alcohols are largely removed with the water, but part of them transesterifies lipids already in the fruits forming fatty acid ethyl-​and methyl esters that are lipophilic and remain in the oil. Such components of interest were isolated from the directly injected oil (25 mg filled up to 1.5 mL with hexane) using a 25 cm × 2 mm ID silica gel

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column with pentane/​4% methyl tert-​butyl ether as mobile phase. This pre-​ separation enabled separate analysis of the fatty acid methyl-​and ethyl esters, as well as resolution of the wax esters by number of double bonds; however, because these compounds are well separated in GC, all components were usually combined in a single fraction of 600 µL. In the mobile phase, pentane was preferred to hexane in order to enable concurrent eluent evaporation for the ethyl-​and methyl esters (on-​column interface with gas discharge). A more detailed analysis of the waxes was achieved by LC-​GC-​MS, as well as offline LC fractionation followed by GC×GC.164 Verification of the performance of the method described above in each gas chromatogram was achieved by standards added to the samples: a more volatile compound (methyl heptadecanoate) enabled checking for losses during concurrent evaporation and two standards marked the beginning and end of the HPLC fraction.165

8.10.10 Nervonic Acid in Meat-​derived Foods Nervonic acid (15-​tetracosenoic acid) is a fatty acid characteristic of tissues of the central nervous system, analyzed within the context of the bovine spongiform encephalopathy (BSE; “mad cow disease”). An LC-​GC method was used because of high resolution and automated sample preparation.166 Lipids in food extracts were converted to methyl esters (methanol/​HCl) and injected into a 1 cm × 4 mm ID silica column mounted in a backflush valve, followed by a 25 cm × 4.6 mm ID gel permeation column, using 1% ethyl acetate in hexane as mobile phase. Transfer of a 400-​µL fraction was performed by partially concurrent evaporation using a Y-​type interface.

8.10.11 Epoxidized Soybean Oil Epoxidized soybean oil (ESBO) is used as a plasticizer and stabilizer of poly(vinyl chloride) (PVC). To measure its migration into food, the dominant fatty acid, diepoxy linoleic acid, is analyzed as a methyl ester, from which it is extrapolated to the total ESBO content. An LC-​GC method was designed involving transesterification directly in the homogenized food under conditions generating fatty acid methyl esters without significant saponification (1 min at ambient temperature).167 From the supernatant in hexane, the diepoxy fatty acids (the diepoxy linoleic acid and the internal standards) were isolated using a 25 cm × 2 mm ID HPLC column packed with cyano-​derivatized silica gel (preferred to silica gel because of the more stable retention properties). Pentane/​20% methyl tert-​butyl ether was the mobile phase at a flow of 300 µL min–​1. A fraction of about 300 µL was transferred by concurrent evaporation (compounds eluted at 250–​260°C) using the on-​column interface. Diepoxy fatty acids produce two well-​separated peaks of stereoisomers. The first eluted isomer of diepoxy methyl linoleate was measured using the virtually co-​eluted second isomer of the internal standard, diepoxy methyl eicosadienoate. Relative standard deviations were in the range 1–​3%.

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The method was designed with verification elements, namely internal standards to check the performance of the critical steps for every analysis.181 Saponification during transesterification and poor extraction of the methyl esters were detected by verification standards added after working up the sample. The most critical step concerned the LC transfer window. As the retention times of the solutes of interest in the sample could not be verified by the UV detector, they were controlled by GC, using the diastereomers eluted at the edge of the relevant LC fraction and not used for the quantitative determination.

8.10.12 Pesticide Residues In 1990, online LC-​GC experiments were performed with a 30 cm × 0.25 mm ID packed capillary HPLC column for the analysis of a pesticide metabolite. Such an approach reduced the fraction to be transferred to the GC instrument by what was called “stopped-​flow introduction” to 16 µL.168 In 1991, an attempt was made to replace the conventional offline gel permeation chromatography (GPC)-​GC method for pesticide residues in food by SEC-​GC.169 A 25 cm × 3 mm ID SEC column packed with a crosslinked polystyrene of 10 nm nominal pore size was used with cyclohexane : ethyl acetate (1 : 1) at 80 µL min–​1. A 400-​µL fraction was transferred by partially concurrent eluent evaporation. The method was applied to the determination of organophosphorus pesticides in fruits using flame photometric detection (FPD).170 The tailing peak of the glycerides (mainly triglycerides) was a problem: the first pesticides were eluted shortly after the glycerides. Even though hardly visible in the liquid chromatogram, the glycerides tailed into the pesticides to an extent that disturbed the GC analysis. As the injection valve was the main source of delayed glycerides elution, it was bypassed and backflushed during SEC using press-​fit Y-​pieces with minimal dead volumes.108 For samples with a high fat content, the capacity of the SEC column limits sensitivity: beyond 1.5 mg fat, the 25 cm × 3 mm ID SEC column was overloaded, resulting in a rapidly broadened peak of the glycerides and disturbance during the GC run. With a larger SEC column to increase capacity, more eluent rinses the system, but the fraction volume grows proportionally. An improved method for organophosphorus pesticide analysis, derived from a previous one,171 used a 25 cm × 4.6 mm ID SEC column with an azeotropic mixture of methyl acetate : cyclopentane (33 : 67; to increase volatility) to which 7% n-​nonane was added for co-​solvent trapping.172 Transfer occurred by mixing the eluent and the carrier gas in a T-​piece, a type of Y-​interface, with an eluent flow rate reduced to 150 µL min–​1, presumably to enable transfer at a lower oven temperature (77°C) and better retention of volatiles. The co-​ solvent facilitated the transfer of a 1.3-​mL fraction into a 10 m × 0.53 mm ID uncoated pre-​column and rendered the method more robust. The lifetime of the pre-​column was given as about 90 runs. Clean-​ up of some organochlorine and pyrethroid insecticides was achieved by coupling the ASPEC unit (Automated Sample Preparation with

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Extraction Columns, a robot-​like autosampler) to GC-​ECD. Water extracts in hexane were pre-​separated on cartridges containing 100 mg silica gel. Transfer to GC involved concurrent evaporation, for which conditions had to be carefully optimized to prevent losses of volatiles. A pyrethroid insecticide was analyzed by LC-​GC-​ECD with fully concurrent evaporation.174 Organochlorine pesticides are eluted from LC over a broad range of retention times, resulting in broad fraction windows. To render the transfer process feasible, a 5 cm × 1 mm ID column was used, resulting in a 240-​µL fraction that was introduced into the GC system by partially concurrent evaporation and the retention gap technique.175 Such an approach enables automation, but no use can be made of the high separation efficiency. p,p′-​Dichlorodiphenyl dichloroethylene (DDE) and PCBs in adipose tissues were determined by LC-​GC-​ECD. The online method enabled the efficient use of the small amount of sample available (a large aliquot could be injected into the HPLC instrument) and reduced the risk of sample contamination.176 Some pesticides in olive oil were analyzed by reversed phase LC-​GC, using the TOTAD interface.177 The PTV injector was packed with a 1 cm plug of Tenax® and kept at 90°C during the transfer process. A 5 cm × 4.6 mm ID HPLC column was used with a methanol : water 70 : 30 (v/​v) mixture at 2 mL min–​1, reduced to 0.1 mL min–​1 during transfer.

8.10.13 Migration of Trimellitic Acid into Food Trimellitic acid (benzene 1,2,4-​tricarboxylic acid; TMA) and its anhydride are used as curing agents for manufacturing “epoxy anhydride” coatings for food cans. TMA is added to bisphenol-​A-​diglycidyl-​ether-​type resins as a crosslinker.178 Trimellitic acid and its anhydride are authorized s­ ubstances for plastics (EU Regulation 10/​2011) with a migration limit of 5 mg kg–​1 food. In reality, TMA is added to coatings as esters, such as ethylene glycol or hexane diol derivatives. This reduces the toxicity of TMA (sensitization potential) during handling and increases the solids in the coating solution (less solvent is needed). However, being a phthalic acid with an additional carboxyl group, the esters might be more toxic than the free acid (toxicity of phthalic esters is due to one of the ester groups not being hydrolyzed179), i.e., the esters migrating from can coatings are not necessarily covered by the safety assessment of the acid or anhydride. As they migrate from the coatings into food contains the TMA moiety in many forms, the sum was determined by saponification of the migrate followed by the formation of ethyl esters.180 These esters were analyzed by online LC-​GC, using a 10 cm × 2 mm ID cyano silica gel column with pentane/​4% methyl tert-​butyl ether and concurrent evaporation with the on-​column interface. Quantification was based on the use of diphenoxy benzene as internal standard. Performance of the method was verified by additional standards, namely dibutyl phthalate to check saponification and chloro phthalic acid to control the extraction from the saponified extract and esterification.

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8.10.14 Flavor Compounds In 1990, an LC-​GC method was described for the analysis of alkanes in citrus essential oils. Transfer to the gas chromatograph involved concurrent evaporation without a vapor exit, but during transfer the carrier gas pressure was increased by combined pressure/​flow regulation to accelerate the discharge of the solvent vapors.181 The coupling of HPLC with GC was used to reconcentrate components of interest in bergamot oil, detected through Fourier-​transform infrared spectroscopy (FTIR).182 Online LC-​GC-​MS was also used for the pre-​separation of bergamot essential oil into compound classes.183 The advantage of pre-​ separating complex mixtures for GC-​MS analysis was shown for neroli essential oil that was separated in a fraction eluted forward and one in backflush.184 It was confirmed that using partially concurrent evaporation with a vapor exit did not cause losses of volatile compounds, such as tricyclene, α-​thujene or α-​pinene. The analysis of stereoisomers of flavor compounds in complex matrices is often performed with two-​dimensional GC, isolating a compound on a first achiral column (heart-​cutting) and separating its enantiomers on a second, chiral one. An HPLC process may replace the first GC separation, enabling the injection of crude extracts that could not be introduced into a GC injector without previous clean-​up. Further, groups of compounds that are eluted closely together in HPLC, such as a series of γ-​lactones, can be analyzed in a single GC run. The chirospecific analysis of γ-​lactones in fruits was based on the use of a 12.5 cm × 2 mm ID silica gel column with hexane/​methyl tert-​butyl ether 65/​35% v/​v.185,186 A fraction of 550 µL was transferred by partially concurrent evaporation and the on-​column interface. As an alternative, RPLC (5 cm × 4.6 mm ID; methanol gradient) coupled to GC by the TOTAD interface was described.187

8.10.15 Pharmaceutical Products Apart from integrating sample clean-​up into automated analysis, an efficient HPLC pre-​separation may enable the use of less selective GC detectors, such as FID. The determination of diethylstilbestrol (DES, a hormone used to accelerate animal growth) in bovine urine was of interest because its application was illegal. In 1986, an LC-​GC-​FID method (using outdated technology) was developed that achieved about the same detection limit as the previously used GC-​MS method.188 For similar considerations, an LC-​GC method was developed by a pharmaceutical company for broxaterol in plasma.189 An immunoaffinity pre-​column with antibodies for the extraction of β-​19-​ nortestosterone from urine was online coupled to a 10 cm × 2 mm ID reversed-​ phase column for reconcentration. A 75-​µL fraction with ethyl acetate was transferred to the GC column by partially concurrent eluent evaporation.190

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A sophisticated method was developed for the identification of degradation products of a drug.191,192 The products were online derivatized (silylated, brominated or methylated) by admixing the reagents through an additional loop in a loop-​type interface (using a 25-​µL loop for the reagents behind the 500-​µL loop for the sample) and reaction in the flooded column inlet. Reversed-​phase LC was used, the effluent of which was online extracted with dichloromethane using an extraction coil and a phase separator. Derivatization by benzoylation was also shown to be possible during the online extraction and used to enhance the extraction yield. The coupling of RPLC with GC was used for the determination of morphine, codeine, heroin, dihydrocodeine and ethyl morphine in urine.193 The eluent was exchanged to an organic solvent by continuous liquid–​liquid extraction followed by phase separation with a sandwich-​type phase separator. A loop-​ type interface was used for analyte transfer and online derivatization (silylation). The same method was used for the determination of beta-​blockers in human serum and urine.194

8.10.16 Organic Compounds in Water Organophosphorus pesticides in water were extracted onto three 0.5-​mm thick/​4.2-​mm diameter membrane disks, eluted with ethyl acetate (after drying the disks with nitrogen for 15 min), and transferred to GC-​NPD by partially concurrent eluent evaporation.195 Phenols in water were analyzed through acetylation in the water, followed by SPE using a 1 cm × 2 mm ID cartridge packed with a styrene–​divinylbenzene copolymer.196 After drying of the cartridge with nitrogen (30 min, 50 mL min–​ 1 ), the phenols were transferred to the GC instrument with 100 µL ethyl acetate, using the retention gap technique. A method for detecting pesticides, PAHs, phthalates, alkyl phenols and other endocrine-​disrupting compounds was described, enriching the compounds from water (to which 50% methanol was added to avoid adsorption problems; 15 mL) on a 10 cm × 2 mm ID column packed with a styrene–​divinylbenzene copolymer.197 After rinsing, this column was dried (nitrogen, 15 min) and eluted with ethyl acetate. Transfer to GC was carried out by multiple injection (3 × 100 µL) into a Tenax®-​packed PTV injector in the solvent split mode. Using MS, detection limits were between 1 and 36 ng L–​1 water. Performance with transfer through the PTV injector was found to be better than with an on-​ column interface, particularly for samples with a high load of salts. Sediments have been extracted with hot pressurized water and the analytes of interest retained in a small column packed with Tenax®.198 After drying with nitrogen, PAHs were transferred to the GC system using 780 µL pentane/​ethyl acetate (9/​1 v/​v) through the on-​column interface with partially concurrent eluent evaporation. Fairly volatile compounds, such as naphthalene, could be analyzed. Accumulation on Tenax® and complete online transfer increased the sensitivity of the method. The use of HPLC enabled an increase in the

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sample amount analyzed and added selectivity for the analysis of brominated flame retardants in sediments.199 A reversed-​ phase method for pesticides in water was described using Empore extraction disks, sampling 1 L of water at 50–​65 mL min–​1 and elution with 3 × 5 mL dichloromethane. After evaporation of the solvent, the extracts were preseparated on a 25 cm × 4.6 mm ID column with methanol/​water. The transfer of 500–​1 400 µL fractions was performed by using the TOTAD interface.200

8.10.17 Polymers and Additives Polymer additives were analyzed using microcolumn SEC (30 cm × 0.25 mm ID fused-​silica capillary packed with a gel). From polymer extracts, a volume of 200 nL was injected. Fractions of around 6 µL were transferred onto a 5 m × 0.32 mm ID uncoated GC pre-​column.201 A GPC-​GC method was proposed to simultaneously determine the molecular mass distribution of polymers and the concentration of additives. After the determination of the mass distribution by conventional GPC, the fraction containing the additives was transferred to the GC column by the PTV interface. An effluent splitter was used to reduce its volume.202 For the compositional analysis of polymers, SEC was online coupled to pyrolysis GC-​MS. Multiple fractions were transferred to the GC system through a side-​port syringe. After solvent elimination, thermal desorption and pyrolysis was performed in a PTV injector, enabling the analysis of additives and the pyrolysis products.203

8.10.18 Comprehensive Two-​dimensional  LC-​GC For complex mixtures, an HPLC pre-​separation into several fractions may be helpful. The relatively sharp signals obtained by high resolution HPLC causes little overlapping, i.e., splitting of a component into adjacent fractions. Furthermore, as there is no significant band broadening in the HPLC column while the eluent flow is stopped, this separation process may be interrupted while the GC analysis is running, enabling the GC analysis of consecutive fractions in a sequential manner. To exploit the resolution by HPLC, transferred fractions should be narrower than the HPLC peaks. In analogy to MS, this could be termed “GC scanning of HPLC”. This would, however, mostly end up with extremely long analysis times. Mostly, the pre-​separation is more targeted, limited to a range of compounds of interest, isolating these from a sample matrix and preseparating those in HPLC which are not adequately separated by GC. The literature presents examples for both types of approaches. Total LC×GC analysis was described for the triglycerides of edible oils and fats.204 Oils were separated on a silica gel column loaded with 10% silver nitrate or a diol-​modified silica gel column. Fractions of 0.5 or 1 min were automatically collected, from which 20–​50 µL were injected into the GC system by

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the on-​column technique. The result was presented as an Excel bubble plot. Comprehensive two-​ dimensional chromatography for lipid analysis was reviewed by the same group in ref. 205. Class-​type analysis of mineral oil hydrocarbons by LC×GC-​time-​of-​flight (TOF) MS is described in ref. 206. The HPLC step was carried out on a 25 cm × 4.6 mm ID amino silica gel column, with 6-​s slices (80 µL) transferred to GC by a PTV-​type interface.207 The resolving power of LC-​GC×GC for mineral oil analysis has also been highlighted.208,209 Phenolic resins used as crosslinkers for epoxy can coatings are made of phenols, formaldehyde and butanol. They consist of complex mixtures that have been analyzed by various combinations of techniques, including offline LC×GC.210 Of a resin made of phenol and cresol, 12 HPLC fractions were subjected to GC analysis. In total, more than 1 000 peaks were counted.211 The direct GC analysis of substances formed by pulsed-​light decontamination of polypropylene films struggles with the strongly dominating oligomers, resulting in detection limits which are too high. The use of LC×GC separating the oligomers and fractionating the other, more polar substances in five fractions brought the detection limit to the level of substances potentially migrating at the threshold of toxicological concern (TTC) of genotoxic compounds.212 Similar detection limits are reached by GC×GC.213 The analysis of substances potentially migrating from recycled paperboard packaging into foods has to deal with a similar problem: the mineral oil hydrocarbons have to be separated to enable the detection of the other substances at sufficiently low concentrations. This was again achieved using LC×GC or GC×GC.214 The use of LC×GC tends to be time-​consuming, as a normal GC run including oven cooling easily takes an hour. This duration was reduced using high speed GC: an LC process lasting 5–​10 min was scanned with GC runs taking seconds. Water was the mobile phase, simplifying online process analysis and field monitoring applications. Separations of volatiles, such as benzene, toluene, ethyl benzene, o-​xylene (BTEX), or chloroform and methylene chloride were presented.215 Speed will have to be paid for by sensitivity: the system must be miniaturized, resulting in low amounts to be loaded onto the HPLC system and small fractions analyzed by GC.

8.11 Conclusions For a few applications, such as food contamination with mineral oil hydrocarbons, online LC-​GC analysis has been established as the principal method, integrating sample preparation into an automated method, improving the performance of the pre-​separation and reducing/​avoiding risks of sample contamination during pretreatment. However, as shown in the literature, there would be many more applications for which the technique could improve the analysis in terms of selectivity, sensitivity and costs. The main obstacle seems to be to become acquainted to the handling of relatively large volumes of liquid in GC.

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The review on the many online LC-​GC techniques investigated in the past provides a rich source of ideas to be kept in mind for possible further exploration. The potential of online coupling of different chromatographic techniques and possibly auxiliary steps of sample treatment, such as extraction and derivatization, has certainly not been fully exploited. There are good reasons to promote such developments: for instance, there are still many samples for which satisfactory compositional analysis is technically not feasible, such as for the safety assessment of the substances migrating from food contact materials.

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86. T. Hyötyläinen, K. Grob, M. Biedermann and M.-​L. Riekkola, J. High Resolut. Chromatogr., 1997, 20, 410. 87. T. Hyötyläinen, K. Jauho and M.-​L. Riekkola, J. Chromatogr. A, 1998, 813, 113. 88. M. Biedermann and K. Grob, J. Chromatogr. A, 2013, 1281, 106. 89. M. Biedermann and K. Grob, J. Chromatogr. A, 2013, 1272, 106. 90. F. Munari and K. Grob, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1988, 11, 172. 91. C. G. Chappell, C. S. Creaser and M. J. Shephard, J. Chromatogr., 1991, 626, 223. 92. K. Grob and J.-​ M. Stoll, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1986, 9, 518. 93. K. Grob, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1987, 10, 297. 94. K. Grob and T. Läubli, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1987, 10, 435. 95. U. Boderius, K. Grob and M. Biedermann, J. High Resolut. Chromatogr., 1995, 18, 573. 96. K. Grob and M. Bronz, J. Microcolumn Sep., 1995, 7, 421. 97. E. Pocurull, M. Biedermann and K. Grob, J. Chromatogr. A, 2000, 876, 135. 98. M. Nestola and T. C. Schmidt, J. Chromatogr. A, 2017, 1505, 69. 99. K. Grob, I. Kaelin and A. Artho, J. High Resolut. Chromatogr., 1991, 14, 373. 100. K. Grob and I. Kaelin, J. High Resolut. Chromatogr., 1991, 14, 451. 101. K. Grob and S. Kuhn, J. Chromatogr., 1984, 301, 1. 102. T. Hankemeier, S. P. J. van Leeuwen, R. J. J. Vreuls and U. A. T. Brinkman, J. Chromatogr. A, 1998, 811, 117. 103. E. Boselli, B. Grolimund, K. Grob, G. Lercker and R. Amadò, J. High Resolut. Chromatogr., 1998, 21, 355. 104. E. Boselli, K. Grob and G. Lercker, J. High Resolut. Chromatogr., 1999, 22, 327. 105. K. Grob, M. Lanfranchi, J. Egli and A. Artho, J. AOAC, 1991, 74, 506. 106. M. Biedermann, C. Munoz and K. Grob, J. Chromatogr. A, 2017, 1521, 140. 107. P. Q. Tranchida, M. Zoccali, G. Purcaro, S. Moret, L. Conte, M. Beccaria, P. Dugo and L. Mondello, J. Chromatogr. A, 2011, 1218, 7476. 108. G. Purcaro, M. Zoccali, P. Q. Tranchida, L. Barp, S. Moret, L. Conte, P. Dugo and L. Mondello, Anal. Bioanal. Chem., 2013, 405, 1077. 109. K. Grob, 2018, Food Addit. Contam., 2018, 35, 1845. 110. L. Barp, M. Biedermann, K. Grob, F. Blas-​Y-​Estrada, U. C. Nygaard, J. Alexander and J.-​P. Cravedi, Sci. Tot. Environ., 2017, 575, 1263. 111. L. Barp, M. Biedermann, K. Grob, F. Blas-​Y-​Estrada, U. C. Nygaard, J. Alexander and J.-​P. Cravedi, Sci. Tot. Environ., 2017, 583, 319. 112. L. Barp, C. Kornauth, T. Würger, M. Rudas, M. Biedermann, A. Reiner, N. Concin and K. Grob, Food Chem. Tox., 2014, 72, 312. 113. K. Grob, J. Agric. Food Chem., 2018, 66, 6968. 114. K. Fiselier, D. Fiorini and K. Grob, Anal. Chim. Acta, 2009, 634, 102.

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115. S. Biedermann-​Brem, N. Kasprick, T. Simat and K. Grob, Food Addit. Contam. A, 2012, 29, 449. 116. M. Lommatzsch, M. Biedermann, K. Grob and T. J. Simat, Food Addit. Contam. A, 2016, 33, 473. 117. M. Lommatzsch, T. J. Simat, M. Biedermannm and K. Grob, J. Chromatogr. A, 2015, 1402, 94. 118. S. Moret, K. Grob and L. S. Conte, Z. Lebensm. Unters. Forsch., 1997, 204, 241. 119. S. Moret, K. Grob and L. S. Conte, J. Chromatogr. A, 1996, 750, 361. 120. M. Biedermann and K. Grob, J. Chromatogr. A, 2015, 1375, 146. 121. F. Munari, A. Trisciani, G. Mapelli, S. Trestianu, K. Grob and J. M. Colin, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1985, 8, 601. 122. A. Trisciani and F. Munari, J. High Resolut. Chromatogr. 1994, 17, 452. 123. G. W. Kelly and K. D. Bartle, J. High Resolut. Chromatogr., 1994, 17, 390. 124. K. Grob, M. Biedermann, A. Caramaschi and B. Pacciarelli, J. High Resolut. Chromatogr., 1991, 14, 33. 125. J. Beens and R. Tijssen, J. Microcolumn Sep., 1995, 7, 345. 126. J. Beens and R. Tijssen, J. High Resolut. Chromatogr. 1997, 20, 131. 127. J. Blomberg, E. P. C. Mes, P. J. Schoenmakers and J. J. B. van der Does, J. High Resolut. Chromatogr. 1997, 20, 125. 128. J. Blomberg, P. J. Schoenmakers and N. van den Hoed, J. High Resolut. Chromatogr., 1994, 17, 411. 129. K. Grob, E. Müller and W. Meier, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1987, 10, 41. 130. H. Hyvönen, T. Auvinen, M.-​L. Riekkola and K. Himberg, J. Microcolumn Sep., 1992, 4, 123. 131. E. Grimvall, C. Östman and U. Nilsson, J. Sep. Sci., 1995, 18, 685. 132. K. J. Welch and N. E. Hoffman, J. Liquid Chromatogr., 1993, 16, 307. 133. A. Scilingo, N. Gigantiello, F. Guidugli and F. Munari, Organohalogen Compounds, 1994, 19, 55. 134. J. J. Vreuls, G. J. de Jong and U. A. T. Brinkman, Chromatographia, 1991, 30, 113. 135. A. C. Lewis, R. E. Robinson, K. D. Bartle and M. J. Pilling, Environ. Sci. Technol., 1995, 29, 1977. 136. P. Tollbäck, H. Carlsson and C. Östman, J. High Resolut. Chromatogr., 2000, 23, 131. 137. A. Christensen, C. Östman and R. Westerholm, Anal. Bioanal. Chem., 2005, 381, 1206. 138. C. Bergvall, Anal. Bioanal. Chem., 2006, 384, 438. 139. F. Drawert and B. Beck, Z. Lebensm. Unters. Forsch., 1974, 155, 282. 140. M. Biedermann, K. Grob and W. Meier, J. High Resolut. Chromatogr., 1989, 12, 591. 141. M. Biedermann, K. Grob, D. Fröhlich and W. Meier, Z. Lebensm. Unters. Forsch., 1992, 195, 409.

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142. G. Schulzki, A. Spiegelberg, K. W. Bögl and G. A. Schreiber, Radiat. Phys. Chem., 1995, 46, 765. 143. A. Lanzon, A. Gert and T. Albi, Grasas y Aceites, 1989, 40, 385. 144. K. Grob, M. Biedermann, M. Bronz and C. Mariani, Riv. Ital. Sostanze Grasse, 1995, 72, 49. 145. K. Grob, M. Biedermann, A. Artho and J. P. Schmid, Riv. Ital. Sostanze Grasse, 1994, 71, 533. 146. ISO 18609:2000(en). www.iso.org/​obp/​ui/​#iso:std:iso:18609:ed-​1:v1:en 147. M. Biedermann, K. Grob and C. Mariani, Fat Sci. Techn., 1993, 95, 127. 148. F. Lanuzza, G. Micali and G. Calabrò, Riv. Ital. Sostanze Grasse, 1995, 72, 105. 149. W. Kamm, F. Dionisi, C. Hischenhuber, H.-​G. Schmarr and K.-​H. Engel, Eur. J. Lipid Sci. Technol., 2002, 104, 756 150. F. Lanuzza, G. Micali and G. Calabrò, J. High Resolut. Chromatogr., 1996, 19, 444. 151. M. Biedermann, K. Grob and C. Mariani, Riv. Ital. Sostanze Grasse, 1995, 72, 339. 152. K. Grob and T. Läubli, HRC & CC,  J. High Resolut. Chromatogr. Chromatogr. Commun. 1986, 9, 593. 153. K. Grob, A. M. Giuffré, U. Leuzzi and B. Mincione, Fat Sci. Techn. 1994, 96, 286. 154. K. Grob, M. Lanfranchi and C. Mariani, J. Chromatogr., 1989, 471, 397. 155. K. Grob and M. Lanfranchi, J. High Resolut. Chromatogr. 1989, 12, 624. 156. A. Artho, K. Grob and C. Mariani, Fat Sci. Techn., 1993, 95, 176. 157. W. Kamm, F. Dionisi, L. B. Fay, C. Hischenhuber, H.-​G. Schmarr und K. H. Engel, Lebensmittelchemie, 2002, 56, 65. 158. W. Kamm, F. Dionisi, L.-​B. Fay, C. Hischenhuber, H.-​G. Schmarr, and K.- H. Engel, J. Chromatogr. A, 2001, 918, 341. 159. G. Micali, F. Lanuzza and P. Currò, J. High Resolut. Chromatogr., 1993, 16, 536. 160. C. Plank and E. Lorbeer, J. Chromatogr. A, 1994, 683, 95. 161. M. Biedermann, A. Bongartz, C. Mariani and K. Grob, Eur. Food Res. Technol., 2008, 228, 65. 162. C. Mariani and E. Fedeli, Riv. Ital. Sostanze Grasse, 1986, 63, 3. 163. C. Mariani, Riv. Ital. Sostanze Grasse, 2017, 94, 139. 164. M. Biedermann, P. Haase-​Aschoff and K. Grob, Eur. J. Lipid Sci. Techn., 2008, 110,1084. 165. K. Grob, J. Chromatogr. A, 2007, 1150, 93 166. R. Barcarolo, A. Bau, J. B. Moreno, B. Dimitrova and E. Anklam, J. Sep. Sci. 2003, 26, 1347. 167. A. Fankhauser-​ Noti, K. Fiselier, S. Biedermann-​ Brem and K. Grob, J. Chromatogr. A, 2005, 1082, 214. 168. H. J. Cortes, E. L. Olberding and J. H. Wetters, Anal. Chim. Acta, 1990, 236, 173. 169. K. Grob and I. Kälin, J. Agric. Food Chem., 1991, 39, 1950.

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170. M. De Paoli, M. T. Barbina, R. Mondini, A. Pezzoni, A. Valentino and K. Grob, J. Chromatogr., 1992, 626, 145. 171. J. J. Vreuls, R. J. J. Swen, V. P. Goudriaan, M. A. T. Kerkhoff, G. A. Jongenotter, U. A. T. Brinkman, J. Chromatogr. A, 1996, 750, 275. 172. G. A. Jongenotter, M. A. T. Kerkhoff, H. C. M. Van der Knaap and B. G. M. Vandeginste, J. Sep. Sci., 1999, 22, 17. 173. G. R. van der Hoff, S. M. Gort, R. A. Baumann and P. van Zoonen, J. High Resolut. Chromatogr., 1991, 14, 465. 174. F. Modeste, M. Caude and P. Devaux, J. High Resolut. Chromatogr., 1996, 19, 535. 175. R. van der Hoff, R. A. Baumann, P. van Zoonen and U. A. T. Brinkman, J. High Resolut. Chromatogr., 1997, 20, 222. 176. S. M. Gort, G. R. van der Hoff, R. A. Baumann, P. van Zoonen, J. M. Martin-​ Moreno and P. van’t Veer, J. High Resolut. Chromatogr., 1997, 20, 138. 177. R. Sanchez, A. Vazquez, D. Riquelme and J. Villen, J. Agric. Food Chem., 2003, 51, 6098. 178. P. Oldring, Waterborne & Solvent Based Epoxies and their End Used Applications, SITA Technology, London, 1996. 179. EFSA (European Food Safety Authority), 2005, www.efsa.europa.eu/​en/​ efsajournal/​pub/​243. 180. A. Fankhauser-​Noti and K. Grob, Food Addit. Contam., 2004, 21, 711. 181. G. Micali, F. Lanuzza, P. Currò and G. Calabrò, J. Chromatogr., 1990, 514, 317. 182. G. Full, G. Krammer and P. Schreier, J. High Resolut. Chromatogr., 1991, 14, 160. 183. L. Mondello, K. D. Bartle, P. Dugo, P. Gans and G. Dugo, J. Microcolumn Sep., 1994, 6, 237. 184. L. Mondello, P. Dugo, K. D. Bartle, B. Frere and G. Dugo, Chromatographia, 1994, 39, 529. 185. H.-​G. Schmarr, A. Mosandl and K. Grob, Chromatographia, 1990, 29, 125. 186. A. Artho and K. Grob, Mitt. Gebiete Lebensmittelunters. Hyg., 1990, 81, 544. 187. G. P. Blanch, M. L. Ruiz del Castillo and M. Herraiz, J. Chromatogr. Sci., 1998, 36, 589. 188. K. Grob, H. P. Neukom and R. Etter, J. Chromatogr., 1986, 357, 416. 189. V. Gianesello, E. Brenn, G. Figini and A. Gazzaniga, HRC & CC, J. High Resolut. Chromatogr. Chromatogr. Commun., 1988, 11, 99. 190. A. Farjam, J. J. Vreuls, W. J. G. M. Cuppen, U. A. T. Brinkman and G. J. de Jong, Anal. Chem., 1991, 63, 2481. 191. J. Ogorka, G. Schwinger, G. Bruat and V. Seidel, J. Chromatogr., 1992, 626, 87. 192. P. Wessels, J. Ogorka, G. Schwinger and M. Ulmer, J. High Resolut. Chromatogr., 1993, 16, 708. 193. T. Hyotylainen, H. Keski-​Hynnila and M.-​L. Riekkola, J. Chromatogr. A, 1997, 771, 360.

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194. T. Hyotylainen, T. Andersson and M.-​L. Riekkola, J. Chromatogr. Sci., 1997, 35, 280. 195. P. J. M. Kwakman, J. J. Vreuls, U. A. T. Brinkman and R. T. Ghijsen, Chromatographia, 1992, 34, 41. 196. D. Jahr, Chromatographia, 1998, 47, 49. 197. L. Brossa, R. M. Marcé, F. Borrull and E. Pocurull, J. Chromatogr. A, 2003, 998, 41. 198. T. Hyötyläinen, T. Andersson, K. Hartonen, K. Kuosmanen and M.-​L. Riekkola, Anal. Chem., 2000, 72, 3070. 199. K. Kuosmanen, T. Hyötyläinen, K. Hartonen and M.-​L. Riekkola, J. Chromatogr. A, 2001, 943, 113. 200. M. Perez, J. Alario, A. Vazquez and J. Villen, Anal. Chem., 2000, 72, 846. 201. H. J. Cortes, G. E. Bormett and J. D. Graham, J. Microcolumn Sep., 1992, 4, 51. 202. N. Kobayashi, H. Arimoto and Y. Nishikawa, J. Microcolumn Sep., 2000, 12, 501. 203. E. R. Kaal, G. Alkema, M. Kurano, M. Geissler and H.-​G. Janssen, J. Chromatogr. A, 2007, 1143, 182. 204. H. G. Janssen, W. Boers, H. Steenbergen, R. Horsten and E. Flöter, J. Chromatogr. A, 2003, 1000, 385. 205. H.-​G. Janssen, H. Steenbergen and S. de Koning, Eur. J. Lipid Sci. Technol. 2009, 111, 1171. 206. S. de Koning, H. G. Janssen and U. A. T. Brinkman, J. Chromatogr. A, 2004, 1058, 217. 207. S. de Koning, H. G. Janssen, M. M. Deursen, U. A. T. Brinkman, J. Sep. Sci., 2004, 27, 397. 208. R. Edam, J. Blomberg, H.-​ G. Janssen and P. J. Schoenmakers, J. Chromatogr. A, 2005, 1086, 12. 209. F. Adam, F. Bertoncini, D. Thiébaut, S. Esnault, D. Espinat and M. C. Hennion, J. Chromatogr. Sci., 2007, 45, 643. 210. M. Biedermann and K. Grob, LWT—Food Sci. Technol., 2006, 39, 633. 211. M. Biedermann and K. Grob, LWT—​Food Sci. Technol., 2006, 39, 647. 212. R. Castillo, M. Biedermann, A. M. Riquet and K. Grob, Polym. Degrad. Stab., 2013, 98, 1679. 213. M. Biedermann, R. Castillo, A.-​M. Riquet and K. Grob, Polym. Degrad. Stab., 2013, 99, 262. 214. M. Biedermann and K. Grob, J. Chromatogr. A, 2013, 1293, 107. 215. W. W. C. Quigley, D. G. Fraga and R. E. Synovec, J. Microcolumn Sep., 2000, 12, 160.

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Gas Chromatography–​ Olfactometry: Principles, Practical Aspects and Applications in Food Analysis M. STEINHAUS Leibniz-​Institute for Food Systems Biology at the Technical University of Munich, Lise-​Meitner-​Straße 34, 85354 Freising, Germany Email: [email protected]

9.1 Introduction Consumers’ food selection is based on a large variety of aspects. Consumers expect food to be good-​looking, fresh, healthy and free of contaminants. Food should be easy to handle, store and prepare. Some consumers attach importance to environmental aspects such as organic and sustainable production, others to religious purity aspects such as kosher and halal, or to ethical aspects such as fair trade. Above all, however, consumers want their food to be tasty.1 From the consumers’ perspective, the sensory characteristics constitute the most important quality parameter of food. Sensory properties account for the pleasure we experience during consumption and thus determine the hedonic value of food. Nevertheless, the different human senses do not contribute equally to the hedonic value of food. Basically, sensations of vision, audition, Food Chemistry, Function and Analysis No. 16 Advanced Gas Chromatography in Food Analysis Edited by Peter Q. Tranchida © The Royal Society of Chemistry 2020 Published by the Royal Society of Chemistry, www.rsc.org

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Figure 9.1 Human senses contributing to the hedonic value of food.

mechanoreception, thermoception, nociception, proprioception, gustation and olfaction may contribute to the overall sensory impression perceived during food consumption (Figure 9.1). Among them, however, olfaction is undoubtedly the most important. Olfaction is a “chemical” sense, located in the nasal cavity (Figure 9.2). There, we find the olfactory epithelium, which in humans includes approximately 10 million olfactory receptor neurons. Yet before a food is consumed, odor-​ active compounds may evaporate from it and enter the nasal cavity with the inhaled air through the nostrils (orthonasally). Together with the visual properties, these orthonasal odor properties provide the first sensory impression and raise expectations regarding the full sensory profile during consumption. During chewing, we predominantly perceive stimuli from mechanoreception, thermoception, nociception, proprioception and gustation. Audition may play an important role while consuming crunchy food, such as crisps and crackers. Olfaction is typically not directly involved, because during chewing, the oral cavity is closed in the front by the lips and in the rear by the soft palate, the velum. During swallowing, however, odor-​active compounds are deposited at the walls of the pharynx. Reflexive exhalation following each swallowing process subsequently transfers these compounds with the exhaled air from the rear into the nasal cavity (retronasally), causing an intense odor event.2,3 Direct retronasal odor perception is also possible, provided the amount of food in the oral cavity is rather small. Then, the velum may be opened deliberately and movements of the tongue provoke the exchange of air between the oral and nasal cavities. This happens, for instance, during wine degustation. On the molecular level, the perception of odor-​active compounds involves G-​ protein-​coupled receptors located in the membrane of the olfactory receptor cell’s cilia, hair-​like structures that protrude from the dendrite into the mucus covering the olfactory epithelium (cf. Figure 9.2). The binding of a sufficient number of odor-​active molecules to the olfactory receptors of an individual

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Figure 9.2 The human olfactory system.

cell, via activation of the G proteins, starts an intracellular reaction cascade which finally leads to the depolarization of the cell membrane. Voltage-​gated ion channels then propagate the depolarization as frequency-​encoded action potentials via the axon to the olfactory bulb in the brain. In humans, ~400 different olfactory receptor proteins are involved in odorant detection, but in each individual olfactory receptor cell only one type is expressed. The axons of olfactory receptor cells with the same type of receptor protein enter the brain via the cribriform plate and converge in the olfactory bulb in spherical structures called glomeruli. There, the olfactory sensory neurons make synaptic contacts with mitral cells, thereby creating a topographic map of a peripheral receptor activation pattern. The mitral cells finally relay this pattern to higher levels of the brain.4–​9 On the level of the olfactory bulb, each type of odor is thus characterized by an activation pattern that combines the information of all ~400 different olfactory receptor cell types. The mitral cells relay this pattern to higher levels of the brain where the information is further processed and evaluated. The complexity of this pattern accounts for the high information content of olfactory percepts and thus the importance of odor-​active compounds for the overall sensory properties of foods. This becomes evident when the sense of smell is temporarily lost. Most people have already experienced such a situation during a severe cold, when nasal congestion and excessive mucus production block the access to the olfactory epithelium for odor-​active compounds released from food. Then any food seems to “taste” more or less the same and the enjoyment during consumption is clearly reduced. In summary, odor-​active compounds are key parameters for the overall quality of food and thus important analytical targets for food chemists working in quality assurance and product development. How can we discover which compounds in a given food are actually odor-​active? First of all, odor-​active compounds need to be volatile. Sufficient volatility is a prerequisite to allow

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the compounds to evaporate from the food into the ambient air and reach the olfactory epithelium in the nose. Volatility is a necessary but not a sufficient criterion. To be odor-​active, a substance must additionally be able to bind to at least one of the ~400 types of olfactory receptor proteins and activate it. This typically requires functional groups as well as hydrophobic ­regions in the molecule. For this reason, the majority of odor-​active compounds are rather lipophilic. Binding constants of the odorant/​receptor protein complexes vary considerably, resulting in extreme differences in the odorant-​specific threshold values, i.e., the concentrations that need to be e­ xceeded, before an odor can be detected. The diversity of odor thresholds is illustrated in Table 9.1. The table shows the odor threshold values of some odor-​active compounds occurring in food as determined in aqueous solution. Data span a concentration range of more than 10 orders of magnitude. To conclude, a substance is odor-​active provided it is (1) volatile, (2) able to bind to an olfactory receptor protein and (3) present at a concentration ­exceeding its specific odor threshold value. As a consequence, most compounds in foods are not odor-​active. Even in the volatile fraction, only a minor percentage is present in odor-​active concentrations. Therefore, attempting to first identify and quantify as many volatiles as possible in a given food and then, in the second step, differentiate between the odor-​active and the odorless ones by comparing their concentrations to their odor thresholds is neither reasonable nor promising. On the one hand, much effort would be wasted on the characterization of odorless compounds. On the other hand, the high overall dynamic range of human olfaction (>1010 as detailed in Table 9.1), would inevitably result in odor-​active trace compounds being overlooked. The alternative is an activity-​guided screening approach that allows to reliably differentiate between odor-​active and odorless volatiles before time and effort

Table 9.1  Odor threshold (OT) values of selected food odorants. Odorant

Odor quality

OT value (µg kg–​1)a

Odor-​active e.g., in …

Ethanol 2-​Methylpropanoic acid Acetic acid Butan-​1-​ol 4-​Methoxy-​2,5-​dimethylfuran-​ 3(2H)-​one 1,8-​Cineole 3-​(Methylsulfanyl)propanal

Alcoholic Cheesy Vinegar-​like Malty Caramel-​like

990 000 60 000 5 600 590 56

Rum10 Parmesan11 Vinegar11 Cempedak12 Mango13

Eucalyptus-​like Cooked potato-​like Popcorn-​like Cucumber-​like Sulfury, burnt Meaty

4.0 0.43

Basil14 Cooked potato15 Bread crust16 Cucumber17 Curry leaves18 Meat19

2-​Acetyl-​1-​pyrroline (2E,6Z)-​Nona-​2,6-​dienal (1R)-​1-​Phenylethanethiol 2-​Methylfuran-​3-​thiol a

0.053 0.0045 0.00054 0.000030

OT values were orthonasally determined in aqueous solution by the ASTM forced-​choice ascending concentration series method of limits.20

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are invested on structure assignment and quantitation. This approach is gas chromatography–​olfactometry (GC-​O).

9.2 The Principle of Gas Chromatography–​Olfactometry Simply speaking, GC-​O means to use the human nose as GC detector. The online odor evaluation of gas chromatographic effluents was first reported by Day et al. as early as 1957.21 In 1964, Fuller et al. described the first elaborate GC-​O system that allowed a trained person to assess the odor of compounds eluted from the GC column in parallel to a second conventional detector.22 This basic setup is still used today, although instead of packed columns and a thermal conductivity detector (TCD) used by Fuller et al., nowadays open tubular fused-​silica capillary columns and more sensitive second detectors such as a flame ionization detector (FID) or a mass selective detector (MSD) are employed in GC-​O. In its simplest form, a GC-​O system thus consists of a gas chromatograph, an injector, a column and a splitting system that divides the column effluent into two parts, one of which is directed to a conventional GC detector (e.g., an FID or MSD), whereas the other one is directed to a heated exit serving as “sniffing port”, where the “sniffer” performs the olfactory evaluation (Figure 9.3). Additionally, a recording system is required which records the chromatogram based on the signal of the second detector. This can be an analog recorder as already used by Fuller et al. or a computer with an appropriate software. Optionally, a separate or integrated system for the recording of the olfactory evaluation may be applied. To perform a GC-​O screening, an aliquot of the volatile fraction previously isolated from a food is applied via the injector onto the GC column and the temperature program of the oven is started. During the analysis, the sniffer places his nose above the sniffing port. Whenever he perceives an odor, the odor quality is recorded together with the retention time of the event. Combining these data with the chromatogram derived from the signal of the second detector results in a GC-​O chromatogram. In Figure 9.4, a GC-​O/​FID

Figure 9.3 Configuration of a basic GC-​O/​FID system.

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Figure 9.4 A GC-​O chromatogram obtained by analysis of the volatile fraction of curry leaves.

chromatogram obtained from the analysis of the volatile fraction of curry leaves, a South Asian seasoning herb, is shown. The chromatogram exemplifies some typical features characterizing GC-​O screening analyses. On the one hand, many peaks of the FID signal, among them also intense ones, are not associated with an odor (e.g., peaks at ~5.1 min and ~14.9 min). Only a minority of the more intense peaks coincide with an odor (e.g., the peak at ~3.5 min). On the other hand, odors appear at positions where there is little or no FID signal (e.g., peaks at ~5.5 min and ~15.7 min). These observations reflect the huge difference in the specificity of the human nose and the FID. An FID detects all organic compounds typically present in the volatile fraction of food with more or less the same sensitivity,23 whereas the human nose, as already discussed before (cf. Table 9.1), is highly specific and shows extreme differences in its sensitivity toward different odorants. The fact that highly potent odorants such as those eluted at ~5.5 min and ~15.7 min in the example depicted in Figure 9.4 are reliably detected even though their amounts are too small to generate a visible response of the second detector is the unique advantage of GC-​O. As a next step, the information included in a GC-​O screening chromatogram is transferred to a table. At this stage, each odor-​active compound is characterized by its retention time and its odor quality. To make the retention data more independent from instrument parameters, retention times are translated to retention indices (RIs).24,25 Provided a linear temperature program is used, the RI of an odorant can be calculated from its retention time and the retention times of adjacent n-​alkanes as obtained by GC-​O after coinjection of the food volatiles and a mixture of homologous n-​alkanes. Table 9.2 shows the outcome of this approach after its application to the volatile fraction of curry leaves (cf. Figure 9.4). In summary, a GC-​O analysis of the volatile fraction isolated from a food constitutes a special case of an activity-​guided screening. In most other cases,

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Table 9.2  Most odor-​active compounds in the volatile fraction of curry leaves. Odorant

Odor quality

RI (FFAP)a

Odorant 1 Odorant 2 Odorant 3 Odorant 4 Odorant 5 Odorant 6 Odorant 7 Odorant 8 Odorant 9

Resinous Grassy Geranium leaf-​like Eucalyptus-​like Grassy Cooked potato-​like Citrusy Cucumber-​like Sulfury, burnt

1 031 1 151 1 157 1 222 1 378 1 458 1 546 1 580 1 601

a

RI on a free fatty acid phase (FFAP) column.

an activity-​guided screening approach includes the preparative fractionation of a compound mixture such as a plant extract followed by assessment of the desired activity (e.g., a pharmacological activity) in the individual fractions. Additionally, purification steps (e.g., solvent removal) are usually required between fractionation and activity assessment. Thus, typically fractionation and activity assessment are chronologically separated. By contrast, a GC-​O screening includes activity assessment (in this case odor assessment) inline with separation (in this case gas chromatography) –​an extraordinary advantage of the method.

9.3 GC-​O:  Practical Aspects Purchasing a ready-​to-​use system from a single source is a convenient option, but not a necessity to establish GC-​O in the laboratory. In principle, any type and brand of GC can be converted into a GC-​O system by installing a splitting system in order to divide the column effluent and a sniffing port for its odor evaluation. The GC-​O system should be located in a separate and quiet room sufficiently isolated from any external odor sources. Further important aspects that need to be considered to allow for meaningful results refer to sample introduction, column parameters, effluent splitting and sniffing systems, and to the sniffer in its role as human GC detector.

9.3.1 Sample Introduction For GC-​O screenings, samples are usually introduced by manual injection. Autosamplers do not provide any significant advantage as the sniffer needs to be present at the instrument anyway. Helium is typically used as the carrier gas in GC-​O. Hydrogen has also been employed.27 The use of hydrogen provides better separation efficiency, but requires enhanced security measures and further adjustments in GC-​O/​MS instruments. Nitrogen exhibits clearly lower separation efficiency than hydrogen and helium and is therefore rarely used in GC-​O.

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A crucial component of the GC-​O equipment is the injector as inappropriate injection techniques may lead to the degradation of sensitive odorants and the formation of odor-​active artifacts. To avoid such effects, the thermal impact during injection is to be minimized. The best option is therefore a cold on-​column injector that allows to deposit the sample from the tip of the syringe needle directly into an uncoated pre-​column serving as retention gap inside the oven. Using this in-​oven on-​column injection technique with solvent extracts includes solvent vaporization in the pre-​column under mild conditions by choosing a low GC oven start temperature. No heating step is needed to transfer the volatiles from the injector to the column inside the oven. Such an in-​oven on-​column injection system is available, for example, from Thermo Fisher Scientific. Other cold on-​column approaches are based on an injection into the pre-​column inside a programmed temperature vaporizing (PTV) injector. This in-​injector on-​column approach overcomes some problems associated with non-​on-​column techniques such as discrimination of high boiling compounds, but essentially requires heating of the injector after injection in order to transfer the volatiles from the section of the pre-​ column inside the injector, to the section of the pre-​column inside the GC oven. To minimize the thermal impact on sensitive analytes when using the in-​injector on-​column approach for GC-​O screening experiments, the temperature gradient applied for injector heating should be low. The best option is to program the injector temperature in parallel to the oven temperature, thus simulating an in-​oven on-​column injection. Application of high heating rates in PTV injection, no matter if in combination with cold on-​column injection or injection into a liner, can lead to thermal compound degradation and artifact formation. The same applies for classical split and splitless injections, where constant elevated injector temperatures are employed. The impact of the injection approach on compound degradation and artifact formation is illustrated in Figure 9.5. Injection of a solution of linalyl acetate in dichloromethane by the in-​oven on-​column approach at 40°C oven start temperature followed by a gradient of 6°C min–​1 resulted in a single peak in the relevant chromatogram region corresponding to linalyl acetate (Figure 9.5 A). When the same solution was subjected to PTV injection in the splitless mode at an injector heating rate of 100°C min–​1, small amounts of artifacts became visible in the chromatogram (Figure 9.5 B). Clearly more artifacts were formed when under otherwise equal conditions, the injector heating rate was increased to 900°C min–​1 (Figure 9.5 C). However, even the small amounts of artifacts formed at low injector heating rates may have a vital impact on GC-​O results, provided the compounds formed exhibit low odor thresholds and therefore are highly odor-​active even at trace levels. Reactions frequently observed in hot injectors are eliminations and rearrangement reactions.28,29 In the case of linalyl acetate (cf. Figure 9.5), major artifacts were identified as myrcene, cis-​β-​ocimene, trans-​β-​ocimene, neryl acetate and geranyl acetate (Scheme 9.1). In summary, the injection method of choice for GC-​O screenings is cold on-​column injection, either using the in-​oven on-​column injection approach

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Figure 9.5 Influence of the GC injection technique on artifact formation for linalyl acetate.

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Scheme 9.1 Major thermal artifacts formed from linalyl acetate by application of hot injection techniques.

or the in-​injector on-​column approach in combination with a PTV injector and low injector heating rates. Approaches involving high injector heating rates or constant elevated injector temperatures are not suitable for GC-​O screenings.

9.3.2 Column Parameters Standard columns for the GC-​O screening of food volatiles are open tubular fused-​silica capillaries. Column dimensions are a compromise between separation efficiency, analysis time, and peak width. Columns of 25 or 30 m length, 0.25 or 0.32 mm internal diameter and a film of 0.25 µm thickness are most widely used. Longer columns enhance the analysis time for only little increase in separation efficiency. Diameters greater than 0.32 mm reduce the separation efficiency without any significant benefit. Diameters below 0.25 mm lead to very narrow peaks, thus the duration of an odor stimulus at the sniffing port becomes too short. The sniffer can only perceive an odor-​ active compound eluting at the sniffing port while inhaling, but not during exhalation. Given a normal respiratory rate of ~15 breaths per minute, odor-​ blind periods of ~2 s occur during GC effluent sniffing. Thus, the peak width in GC-​O should be ≥4 s in order to reliably detect an odor-​active compound eluted from the column at the sniffing port. Therefore, GC effluent sniffing does not match well with fast GC and with the second dimension of comprehensive two-​dimensional GC. A film thickness of 0.25 µm is applicable for most GC-​O approaches. A thicker film provides better retention and separation efficiency, but increases bleeding and thermal impact on odor-​active compounds. Thicker films are therefore mainly applied in GC-​O when the extremely highly volatile

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fraction of food is to be screened for odorants, typically in combination with headspace sampling. The gentler and therefore better alternative to achieve an increase in the retention of odor-​active food compounds is to decrease the GC oven start temperature below ambient temperature by using active oven cooling (cf. Section 9.4.5). The stationary phases used for GC-​ O screenings include polysiloxanes as well as polyethylene glycols. To facilitate structure elucidation (cf. Section 9.6) and resolve major co-​elution problems, often a GC-​O screening using a non-​polar polysiloxane phase is combined with a GC-​O screening using a polar polyethylene glycol-​based phase. Most widely used non-​polar phases are pure dimethylpolysiloxane (trade names e.g., CP-​Sil 5, DB-​1, HP-​ 1, OV-1, RTX-​1, SE-​30, SPB-​1, ZB-​1) and dimethylpolysiloxane with 5% phenyl groups (trade names e.g., CP-​Sil 8, DB-​5, HP-​5, OV-​5, RTX-​5, SE-​54, SPB-​5, ZB-​5). Common polar phases are pure polyethylene glycol (Wax phases) and 2-​nitroterephthalic acid modified polyethylene glycol (free fatty acid phase, FFAP). Different from the majority of other GC phase types, FFAP phases also yield good peak shapes for some crucial acidic compounds such as the short-​chain carboxylic acids butanoic acid, 2-​methylbutanoic acid and 3-​methylbutanoic acid and the cyclic enoloxo compounds 4-​hydroxy-​ 2,5-​dimethylfuran-​3(2H)-​one and 2(5)-​ethyl-​4-​hydroxy-​5(2)-​methylfuran-​ 3(2H)-​one,30,31 all of which are common odor-​active compounds in food.32 Likewise, base-​deactivated polyethylene glycol phases can be useful in case amines or basic N-​heterocyclic compounds play a major role as odor-​active compounds in the investigated food. Polysiloxane phases are more stable than polyethylene glycol phases. Maximum operating temperatures of polysiloxane columns are typically beyond 300°C, whereas polyethylene glycol columns must not be heated usually above 250°C to avoid thermal decomposition. Furthermore, polyethylene glycol phases are susceptible to oxidation, leading to losses of stationary phase. As a consequence, RIs on polyethylene glycol columns show a drift to lower values with increasing age, whereas RIs on polysiloxane columns remain nearly unchanged. A clear advantage of polyethylene glycol-​based columns, however, is that they allow to separate some structurally related odor-​ active compounds which are co-​eluted when using non-​polar polysiloxane columns. Examples are shown in Table 9.3. Hexanal and (3Z)-​hex-​3-​enal are grassy-​smelling compounds frequently found in odor-​active amounts in food.32 Using a non-​polar DB-​5 column, their RIs are too close to allow their separate detection at the sniffing port. Furthermore, hexanal often occurs in higher amounts than (3Z)-​ hex-​3-​enal, although the latter compound could contribute more to the grassy smell, because with 0.12 μg per kg water, its odor threshold value is clearly lower than that of hexanal (2.4 μg kg–​1).33 Careless interpretation of GC-​MS data could therefore lead to a false structure assignment. Another compound often erroneously identified as odor-​active in foods when only a non-​polar column is used for GC-​O screening, is oct-​1-​en-​3-​ol. Complementary GC-​O analysis using a polar column typically shows that a minor amount of the extremely

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Table 9.3  S  tructurally related food odorants and their RIs on different stationary GC phases. RI (FFAP)

RI (BGB-​176)a

797 800

1 141 1 080

–​ –​

977 980

1 298 1 449

–​ –​

Smoky Smoky

1 259 1 261

2 271 2 273

1 473 1 524

Cheesy Cheesy

–​ –​

1 669 1 669

1 139 1 158

Cheesy

–​

1 669

1 163

Odorant

Odor quality

(3Z)-​Hex-​3-​enal Hexanal

Grassy Grassy

1-​Octen-​3-​one 1-​Octen-​3-​ol

Mushroom-​like Mushroom-​like

4-​Propylphenol 3-​Propylphenol 3-​Methylbutanoic acid (2S)-​2-​Methylbutanoic acid (2R)-​2-​Methylbutanoic acid

RI (DB-​5)

BGB-​176 is a β-​cyclodextrin-​based chiral  phase.

a

potent oxidation product oct-​ 1-​ en-​ 3-​ one (odor threshold value 0.016 µg kg–​1) causes the characteristic mushroom-​like odor, whereas oct-​1-​en-​3-​ol, although present in much higher amounts, due to its high odor threshold value (45 µg kg–​1) is odorless.33 Nevertheless, even a combination of a non-​ polar phase and a polar phase cannot resolve all kinds of co-​ elution problems. With the couples 3-​propylphenol and 4-​propylphenol and 2-​methylbutanoic acid and 3-​methylbutanoic acid, Table 9.3 shows two examples of compounds that show co-​elution on both column types. Separation, however, can be achieved by using cyclodextrin-​based chiral columns. This illustrates that chiral phases not only allow to differentiate between enantiomers, but in many cases also provide superior separation of positional isomers. The sensitivity of chiral columns to stationary phase degradation and their low maximum operating temperatures, however, exclude them as standard columns for GC-​O screenings and limit their use to critical separation issues and to the determination of the enantiomeric ratios of chiral odorants (cf. Section 9.6). Between a cold on-​column injector and an analytical column of a GC-​O system, an uncoated but deactivated fused silica capillary of ~2–​5 m is typically inserted as a pre-​column. First, the pre-​column protects the main column from contamination with non-​volatile compounds. From time to time, cutting off the first ~10 cm may be used to remove any non-​volatiles previously deposited on the pre-​column. Second, the pre-​column serves as retention gap to allow narrowing of the starting bands by the solvent effect.34 Third, a 0.53-​mm ID pre-​column can be used to combine 0.32-​or 0.25-​mm ID analytical columns with modern septum-​based cold on-​column injectors that require stable syringes with larger needle diameters (e.g., 0.47 mm). Only older septum-​free in-​oven cold on-​column injectors with manual valves can

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be operated with thinner syringe needles of 0.23 mm diameter and thus thinner pre-​columns. The preferred way to connect the pre-​column to the main column, with a low dead volume, is through single-​use quartz connectors a with tapered internal diameter and deactivated surface. During a GC-​O analysis, the column temperature is controlled by the GC oven program. The oven start temperature should be set as low as possible to ensure sufficient retention and separation of highly volatile food odorants. Without active oven cooling, typically start temperatures of 35–​40°C are employed. Temperatures as low as these additionally allow to exploit the solvent effect34 for compound focusing if samples are introduced diluted in organic solvents, such as diethyl ether (b.p. 35°C) or dichloromethane (b.p. 40°C), both of which are commonly used to extract odor-​active compounds from food (cf. Section 9.4.3). To complete solvent evaporation on the pre-​column, the start temperature is usually kept for 2 min. Then, the temperature is typically raised at a constant rate. A linear temperature program allows for the simplified calculation of the retention indices of odor-​active compounds from their retention times and the retention times of adjacently eluted n-​alkanes by linear interpolation.24,25 A low oven heating rate increases separation efficiency and peak width, the latter reducing the risk to miss an odor-​active compound during an exhaling phase; however, it also increases total run time. As a compromise, often oven heating rates of 4–​6°C min–​1 are applied, which results in run times of 30–​45 min when using typical column dimensions.

9.3.3 Effluent Splitting and Sniffing Systems Effluent splitting is accomplished by a splitting system consisting of a splitting device located inside the GC oven and two uncoated but deactivated fused-​ silica capillaries which direct the effluent parts to the sniffing port and to a second conventional detector such as an FID or an MSD, respectively. A simple and low-​cost splitting device is a Y-​shaped surface-​deactivated quartz glass splitter. Such splitters are readily available from suppliers of chromatography consumables; they are easy to handle and show low dead volumes. Capillary dimensions must ensure simultaneous detection at the sniffing port and at the second detector. In case the second detector is an FID, this is achieved by using two capillaries of the same diameter and length. The split ratio is then 1 : 1, which is suitable for most applications. The sum of the cross-​sectional areas should be equal to the cross-​sectional area of the analytical column to maintain the same linear carrier gas velocity. In case the second detector is an MSD, the capillary to the sniffing port ends at ambient pressure, whereas the capillary to the MSD ends in high vacuum. To maintain the desired split ratio and –​more important –​to ensure simultaneous detection at both detectors, the capillary dimensions need to be carefully adjusted. Flow calculators included in GC software packages or downloadable from the Internet are a helpful tool to find the appropriate values. Nevertheless, after the system has been set up, the

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simultaneous detection at the sniffing port and the MS should be verified at different oven temperatures by analysis of a mixture of odor-​active reference compounds. The sniffing port basically provides an outlet of the column effluent, a part intended for the odor evaluation by the GC sniffer at a spot somewhere outside the oven body. On the way from the splitter to the sniffer, an elevated temperature must be maintained to avoid recondensation and adsorption of odorants and to keep them in the gas phase. The simplest layout of a sniffing port is obtained by removing an unused injector or detector from the GC and inserting the capillary into the heated base until it is level with the top side. In order to allow the sniffer to enjoy a more convenient position, the heated zone can be extended further up by using a solid piece of metal with a small drill hole housing the capillary.35 A metal with a high thermal conductivity, e.g., aluminum, a good thermal connection to the heated base and a circular cross-​section to minimize thermal losses help to minimize the temperature difference between the bottom and top of the device. Nevertheless, the difference has to be considered when setting the temperature for the base. A temperature program can be used to reduce the thermal impact on the eluted odorants. The top of the device should have a smaller diameter than the rest to avoid burns of the upper lip. Further protection can be achieved by an insulating material covering the whole device or at least the top part. A very helpful tool is a small mirror placed in front of the sniffer that allows the optimum position of the nostrils during the whole GC-​O run to be maintained (Figure 9.6). The alternative to a passively heated sniffing port placed on a detector base on the top of the GC oven is to conduct the capillary via a hose with resistance heating from inside the oven to a place aside, where the sniffer can perform the GC-​O analysis in a more comfortable position. Commercially available sniffing port systems usually use this technique to spatially separate the sniffing area from the GC system. Currently (2018), such systems include the “Olfactory Detection Port” (ODP) from Gerstel, the “Olfactory Detector” (OFD) from SIM, the “Phaser” from GL Sciences, and the “Sniffer 9100” from Brechbühler. These systems additionally include sophisticated splitting devices allowing for split ratios different from 1 : 1 and the option to add a humidified make-​up gas to the capillary effluent to prevent drying of the sniffer’s nasal mucosa. However, the use of a make-​up gas unavoidably leads to a dilution of the odor-​active compounds, thus a decrease in sensitivity. The usefulness of make-​up gas humidification for sniffers’ comfort and performance is questionable.36 For the recording of the olfactory data acquired during a GC-​O analysis, approaches of diverse complexity can be used. The simplest option is to place a display indicating the GC retention time in view of the sniffer. Whenever the sniffer perceives an odor, he writes down the retention time together with the odor quality and an intensity rating. Alternatively, he speaks out odor quality and intensity rating and a second person notes the information

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Figure 9.6 A GC-​O system with a sniffing port consisting of a lathed aluminum device (A) which is mounted on a heated detector base next to the FID (B). The top of the device is covered by a PTFE (polytetrafluoroethylene) cap as insulation to protect the upper lip of the sniffer from burns. A mirror (C) is used to exactly position the nose.

down together with the retention time. After the GC run, the collected data are integrated into the chromatogram of the second detector, either manually or electronically. Another option is to connect an analog recorder to the signal exit of an FID and place it in front of the sniffer. Then, position, odor quality and intensity of each odor percept can directly and online be written into the paper chromatogram (an illustration can be found in the Internet37). Modern commercially available sniff data handling solutions are based on electronic online recording of retention time, odor quality and intensity of the odor percepts during GC-​O analysis. Input devices include PC keyboard (SIM), mouse (GL Sciences, SIM), pen tablet (SIM), touchscreen (Brechbühler, GL Sciences, SIM), joystick (Gerstel), slider (Brechbühler) and microphone (Brechbühler, Gerstel, GL Sciences, SIM). The microphone option can be combined with a voice-​to-​text software that allows integration of the spoken words as text into the chromatogram. However, speaking unavoidably influences the respiratory rhythm of the sniffer and hence may disturb the recognition of a closely following odorant. An alternative that avoids speaking is the Aroma Palette included in the GL Sciences sniff data recording software. When an odor is perceived during a GC-​O run, the sniffer with the PC mouse selects

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an odor from a palette of previously defined descriptors displayed together with the chromatogram on a PC screen (cf. Figure 9.6). A similar feature is the menu bar included in the SIM software.

9.3.4 The Sniffer in its Role as Human GC Detector The sniffer is an integral part of a GC-​O system and its performance vitally determines the success of a GC-​O study. Beginners in GC-​O tend to underestimate the importance of training and experience. A sniffer must be proficient in quickly recalling proper odor descriptors from a set of vocabulary constituting an unambiguous flavor language. This vocabulary needs to be learned and steadily trained. The odor descriptors combined in a flavor language should refer to odorous objects commonly faced in everyday life to allow a wide range of people to quickly get an idea on the odor behind it. Descriptors should neither be too specific (“like the backyard of my uncle”) nor too general (“herbal”). Each descriptor should be unequivocally defined on the basis of an aqueous solution of an odorous reference compound in a defined concentration. Reference compounds should be chosen according to their prevalence in the materials studied. Thus, when food is the basic area of interest, prevalent food odorants32 should be chosen as reference. The training of a flavor language can be accomplished by orthonasal testing of diluted aqueous solutions and by GC-​O of odorant mixtures diluted in an organic solvent. Such trainings additionally allow the sniffer to identify her/​his personal specific anosmia. Taking into account that with ~400, the number of different receptor proteins expressed in the olfactory epithelium is quite big (cf. Section 9.1), it is very likely that an individual shows a single-​nucleotide polymorphism (SNP) in at least one of the related genes, resulting in a non-​functional receptor and thus insensitivity to certain odorants. Therefore, GC-​O screenings must always be performed by

Table 9.4  Specific anosmia to important food odorants reported in humans. Odorant

Odor quality

Odor-​active e.g., in … Anosmia prevalence reported in humans (%)

β-​Ionone 5α-​Androst-​16-​en-​3-​one 3-​Methylbutanal 1,8-​Cineole (eucalyptol) (E)-​β-​Damascenone

Violet-​like Sweaty, urinous Malty Eucalyptus-​like Cooked apple-​like

Butane-​2,3-​dione Oct-​1-​en-​3-​one 3-​Methylbutanoic acid

Buttery Mushroom-​like Cheesy

Raspberries38 Pork (boar taint)41 Chocolate44 Basil14 Pasteurized apple juice47 Butter48 Mushrooms50 Cheese

4239,40 10–​4742,43 6–​3639,42,43 0–​2543,45,46 1039 0–​1046,49 546 346

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at least two sniffers with complementary olfactory abilities, i.e., a specific anosmia present in one sniffer must not be present in the other, and vice versa. Results of all sniffers are finally combined. Table 9.4 shows prevalent specific anosmia to selected compounds that typically occur in food in odor-​active amounts. Another important aspect is reproducibility. Even a highly experienced sniffer cannot produce a valid screening result with just one GC-​O run. For reliable data, GC-​O analysis of an individual sample must be repeated until the outcome is reproducible. When dealing with an unfamiliar food odor such as at the beginning of a new project, this may mean repeating the GC-​O analysis 20 times or even more.

9.4 Sample Preparation Techniques Preceding GC-​O Sample preparation prior to GC-​O analysis is a crucial step as inappropriate methods may lead to substantial qualitative and quantitative changes in odor-​ active compounds. The basic function of sample preparation is to remove non-​volatile material. Non-​volatile compounds would disturb the separation process on the GC column and in the long term destroy the performance of the stationary phase. The need to remove non-​volatiles before GC-​O is the inevitable price to be paid in this special activity-​guided approach for the benefit of the high separation efficiency of GC and the privilege of an online activity assessment. Changes induced by sample preparation for GC-​O analysis on the way from the food to the GC column may include the following four aspects: (1) discrimination of odor-​active compounds due to their (high or low) volatility, (2) discrimination of odor-​active compounds due to their (high or low) polarity, (3) chemical conversion of reactive odor-​active compounds, and (4) formation of odor-​active artifacts. An easy quality assessment of a sample preparation procedure is the continuous evaluation of the overall smell at the different work-​up levels by using the human nose. This allows one to quickly diagnose whether individual work-​up steps have led to substantial odor changes such as a loss of odor notes perceived in the starting material or the occurrence of additional odor notes that have not been present in the starting material. A major factor contributing to changes in the fraction of odor-​active compounds during work-​up is heat impact. Higher temperatures in particular can lead to the formation of potent odor-​active artifacts with a high sensory impact even in minute amounts. As far as possible, elevated temperatures must therefore be strictly avoided during sample preparation for GC-​O. Artifacts, however, may also be formed enzymatically or by oxidative reactions.

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9.4.1 Sample Homogenization Before separating the volatiles from the non-​volatiles, most foods need to undergo a crushing and homogenization process first. This can be omitted for homogeneous liquid foods and beverages such as fruit juice and beer. Soft, watery foods such as berry fruits or cooked vegetables can be homogenized with a hand or table-​top blender, if necessary under addition of some water. Solid and fibrous foods such as nuts and meat can be ground into fine powder, preferentially with a cryomill. Low temperatures facilitate the grinding process as materials become more brittle and prevent thermally induced artifact formation by frictional heat. Homogenization of food materials consisting of intact tissue may lead to the enzymatic formation of odor-​active compounds. Upon crushing of plant tissues, odorants can be formed from unsaturated fatty acids via the lipoxygenase pathway,51 among which grassy-​smelling (3Z)-​hex-​3-​enal is one of the most potent. The concentrations of lipoxygenase reaction products increase with the degree of grinding and with time, and may reach values that would never be reached during consumption.18,52,53 Additionally, side reactions may lead to losses of other odor-​active compounds such as thiols.18 Inhibition of enzymatic reactions is possible, e.g., by adding saturated aqueous calcium chloride solution before homogenisation.18,47,54 Then, however, formation of lipoxygenase products is completely suppressed and their odor contribution during consumption may be underestimated.

9.4.2 Steam Distillation To separate the volatiles in a food homogenate from the non-​volatiles, numerous methods are available. In the early days of food volatile analysis, steam distillation was widely used.55 Organic volatiles were obtained from the distillate by extraction with an organic solvent or could be recovered as supernatant “essential oil” in the distillate. A popular approach was the simultaneous distillation/​extraction procedure established by Likens and Nickersen.56,57 However, these classic approaches performed under atmospheric pressure involve temperatures of ~100°C, which inevitably led to odorant deterioration and thermal formation of odor-​active artifacts.57–​61 Particularly if the starting material has not undergone any heat treatment before (e.g., fresh fruit or herbs), the artifacts can easily be recognized when sniffing the distillate. The distillate odor would be completely different to the odor of the starting material. Such volatile isolates are inapplicable for a meaningful GC-​O screening. In some cases, however, steam distillation has been used to simulate cooking simultaneously with the volatile isolation.62 Nevertheless, the volatile fraction isolated in this way might be less representative than an isolate obtained after normal cooking followed by isolation of the volatiles under mild conditions. However, representative volatile isolates suitable for GC-​O screening can be obtained if the steam distillation is carried out under reduced pressure at room temperature.57,59,63

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A common disadvantage of all variants of steam distillation is that recoveries of highly polar odor-​active compounds is low.60,64

9.4.3 Solvent Extraction Methods Another traditional approach for the isolation of odor-​active compounds is solvent extraction. As most odor-​active compounds are rather lipophilic (cf. Section 9.1), they can effectively be extracted from foods by non-​polar organic solvents. A low boiling point of the solvent facilitates subsequent extract concentration and reduces the risk of compound degradation and artifact formation during the concentration process. Widely used solvents are diethyl ether (b.p. 35°C) and dichloromethane (b.p. 40°C). Chlorofluorocarbons such as trichlorofluoromethane (b.p. 24°C) and 1,1,2-​trichloro-​1,2,2-​trifluoroethane (b.p. 48°C) have been used occassionally, but are avoided today because of their ozone-​depleting potential. Pentane (b.p. 36°C) and isopentane (b.p. 28°C) are less effective for the extraction of relatively polar odor-​active compounds. In the case of more or less liquid aqueous samples such as beverages and fruit purées, solvent extraction can simply be done in a separatory funnel. Emulsions may be avoided by salt addition or if phase separation is enforced by centrifugation. Extremely stable emulsions such as those formed from fruits high in pectin can be treated with high amounts of anhydrous sodium sulfate until a homogeneous powder is obtained that can be transferred into a glass column for continuous extraction.35 Continuous extraction in a glass column is also the method of choice for fine food powders obtained by cryomilling. The cryomilled material is suspended in solvent and anhydrous sodium sulfate is added according to the water content of the sample, before the mixture is transferred to the column. To avoid local hot spots due to the heat of solution, anhydrous sodium sulfate must always be added after addition of the solvent and under vigorous stirring. When higher amounts of anhydrous sodium sulfate need to be added, cooling in an ice bath is useful. With sufficient solvent volume, the column extraction approach can be exhaustive.18 High extract volumes, however, may imply the need for a concentration before further work-​up steps. Vigreux columns can be used to remove a major part of the extraction volume, but the thermal impact on the extract, which not only contains volatiles but also non-​volatile lipophilic compounds such as triglycerides and carotenoids, may lead to artifact formation. Less solvent is needed when using a Soxhlet extractor,65 but the thermal impact on the sample is similar. From clear aqueous samples, extraction of odor-​active compounds can also be accomplished through solid-​phase extraction (SPE) using non-​polar adsorbents. After a washing step with pure water, the adsorbed compounds are desorbed by elution with a small amount of non-​polar organic solvent. Another alternative for the extraction of lipophilic compounds from foods is supercritical fluid extraction (SFE) with carbon dioxide. Whatever method of solvent extraction is used, the extract still contains non-​ volatiles that need to be removed before the gas chromatographic separation. In the early days of food volatile analysis, this was often not performed before

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the sample was introduced into the GC injector and hot injectors packed with e.g., glass wool were used to prevent non-​volatiles from entering the column. The high thermal impact, not only on the volatiles (cf. Section 9.3.1), but also on the non-​volatiles inevitably produces a plethora of odor-​active artifacts. Thus, for GC-​O analyses removal of the non-​volatile part in solvent extracts needs to take place before GC injection and any thermal impact on the sample must be avoided. A mild approach to separate the volatiles from the non-​volatiles in a solvent extract is sublimation under high vacuum conditions. The solvent extract is placed in a round-​bottom flask and frozen by means of liquid nitrogen. Then the flask is connected to high vacuum and the cooling process is interrupted. The sublimated volatiles are recovered in traps that are cooled by liquid nitrogen.66,67 Using this “high vacuum transfer” (HVT) approach results in complete removal of non-​volatiles, without thermally formed artifacts.68 The obtained distillates are highly representative. However, recoveries of high boiling odor-​ active compounds are low. Better recoveries are achieved if the solvent extract is introduced into the round-​bottom flask in small portions at room temperature by means of a dropping funnel.69,70 The spray generated when the extract portion enters the high vacuum allows for effective vaporization of volatiles, but implies the risk of small droplets of non-​volatile material being transferred into the distillate. To avoid this, a specially designed interface must be inserted between the flask and the traps. This device forces the stream of solvent vapor to repeatedly change its direction so that the major number of droplets containing non-​volatile material would be deposited at the walls of the device.71 This idea was renewed with the development of solvent-​assisted flavor evaporation (SAFE), but handling was simplified by integrating the major parts of the equipment into a single glass device.72 Furthermore, the pathway of the volatiles between evaporation flask and cold traps of the SAFE device was designed double-​walled to allow temperature control and thus reduce recondensation of volatiles and increase yields. However, temperatures should not substantially exceed 40°C to avoid compound degradation and artifact formation. A disadvantage of SAFE is the price of the equipment, which is ~10 000 € for a high vacuum pump system and ~1 000 € each for the glassware. Furthermore, carryover of small amounts of non-​volatile material cannot be totally excluded, particularly when the extract is rich in lipids. Nevertheless, SAFE has become a major approach for the preparation of volatile isolates from foods for subsequent GC-​O screening. The representativeness of SAFE isolates can best be demonstrated by applying the approach to liquid materials such as juice or beer, without preliminary solvent extraction. The aqueous distillates are hardly distinguishable from the starting materials by olfaction. When aiming at a volatile isolate for GC-​O screening, however, solvent extraction with subsequent SAFE is preferred to the opposite order, SAFE and subsequent solvent extraction, because SAFE of aqueous samples, due to the higher boiling point, is more time-​consuming. Volatile isolates obtained by solvent extraction and SAFE normally require concentration before GC-​O screening. Concentration to volumes ≥ 1 mL

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can be accomplished by using Vigreux columns. Microdistillation devices73 or Kuderna–​Danish Concentrators,74 can be used for further concentration to volumes ≥ 100 µL. Smaller final volumes are rarely required. If necessary, they can be achieved according to the approach detailed by Dünges.75 Concentration in a stream of nitrogen is not a good alternative as it results in higher losses. When planning the work-​up, it should be considered that with decreasing volumes, handling of the concentrates becomes more difficult. Concentration after SAFE is always the better alternative to concentration before SAFE, even if it is more laborious, in case the extract needs to be divided into several portions before SAFE. As the non-​volatile fraction is still present in the extract before SAFE, the risk of artifact formation during concentration is much higher. In any case, a concentration step inevitably results in losses of highly volatile compounds with boiling points in the range of that of the solvent or below. To screen this particular fraction of a given food for odor-​active compounds, an additional approach such as GC-​O analysis of headspace samples is required. For edible oils and similar foods with high lipid content and low concentration of volatiles, thin film distillation76,77 is an alternative to SAFE. Under high vacuum, the starting material is administered in the form of a thin film on the inner surface of a moderately heated glass column. As the film moves down the column, volatiles evaporate from the film and recondense in cold traps. After the distillation has been finished, the condensed volatiles in the traps are taken up with a small amount of organic solvent.

9.4.4 Solvent-​free Extraction Approaches Totally solvent-​free approaches for the extraction of lipophilic compounds from foods are solid-​phase microextraction (SPME)78 and stir bar sorptive extraction (SBSE).79,80 Both methods are based on the adsorption of lipophilic compounds in the food to a polymer-​coated device (see Chapter 1). Subsequently, the adsorbed molecules are thermally desorbed and online introduced into a GC system. Using SPME, thermal desorption occurs directly in a hot GC injector. In the SBSE approach, release of the trapped compounds from the polymer-​coated stirring rod is accomplished in a thermal desorption device mounted upstream of the injector. In both cases, thermal formation of artifacts is unavoidable.61 Neither SPME nor SBSE extraction are therefore suitable for GC-​O screenings. The same applies for the thermodesorption of volatiles directly from foods. This can be done by introducing powdered solid food material or liquid material taken up by an inert adsorbent such as silica gel directly into a thermodesorption unit. In combination with GC-​O, such an approach can only be used if the simulation of a roasting process is desired.

9.4.5 Headspace Sampling Techniques A fundamentally different approach to separate the volatile compounds from the non-​volatiles in a food are headspace methods. As transfer from the food

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Figure 9.7 Configuration of a headspace GC-​O/​FID system.

into the ambient air is a basic prerequisite for any odor-​active compound (cf. Section 9.1), the combination of headspace sampling and GC-​O seems to be an obvious option to identify the crucial odorant compounds. Static headspace analyses are characterized by sampling of a defined volume of the gas phase inside a hermetically closed flask in which equilibrium has been achieved between a food sample and the gas phase. By using gas-​ tight syringes, the headspace sample is transferred from the flask to the GC system (see Chapter 1). Syringe heating can be applied to reduce adsorption of odor-​active compounds to the inner surface of the barrel and the plunger. The high sample volumes require special injection techniques to concentrate the volatiles before GC separation. This is typically achieved by cryotrapping. One option for the cryotrapping of headspace samples is depicted in Figure 9.7. The system includes a trap located inside the oven between the injector and the analytical column. During injection, the outlet valve is open to ensure a high carrier gas flow in the injector and the cold trap. Injected volatiles are thus transferred to the trap and cryofocused. After injection has been finished, the outlet valve is closed, trap cooling is switched off and the GC run is started. Active oven cooling allows for start temperatures below ambient temperature to ensure sufficient retention and separation of highly volatile food odorants such as methanethiol (b.p. 6°C), acetaldehyde (b.p. 20°C), ethanethiol (b.p. 35°C) and dimethyl sulfide (b.p. 37°C). An alternative to a cold trap inside the GC oven is a PTV injector with external cooling. During introduction of headspace samples, the injector is cooled and the excess of gas volume is released through the split exit. After injection has been finished, the split exit is closed, the injector is heated and the GC run is started. As already discussed (cf. Section 9.3.1), the heating rate must not be too high in order to avoid compound degradation and artifact formation. Solvent-​free extraction approaches such as SPME and SBSE can also be combined with headspace sampling and subsequent GC-​O analysis. Because no non-​volatiles are extracted, the risk of artifact formation during thermal desorption is reduced as compared to direct sampling from food, but not

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negligible. Furthermore, polymer extraction is more selective than solvent extraction and also depends on the type of polymer used. In contrast to static headspace approaches, dynamic headspace sampling involves purging the headspace above a food sample for a certain time, thus increasing yield and sensitivity. The collected volatiles are either cryofocussed, or concentrated on an adsorbent and subsequently thermodesorbed (see Chapter 1). For GC-​O screenings, cryofocusing is the better alternative, because the higher temperatures needed for the thermodesorption of the volatiles from adsorbent materials include a higher risk of compound degradation and artifact formation.

9.4.6 Sample Preparation Techniques –​Conclusion In summary, none of the methods discussed above represents an optimum sample preparation technique for GC-​O, as all of them have advantages as well as limitations. However, a successful compromise is the combination of the solvent extraction/​SAFE approach on the one hand with static headspace sampling on the other hand. The majority of odor-​active compounds in food are covered by the solvent extraction/​SAFE approach. The concentrations of the odor-​active compounds in solvent extracts, which determine the sensitivity of the approach, can easily be selected by adjusting the amount of starting material and the final isolate volume. Furthermore, solvent extracts allow the application of a plethora of fractionation approaches, which is an important advantage when it comes to structure assignment (cf. Section 9.6). The major drawback of the solvent extraction/​SAFE approach, namely the loss of highly volatile odorants during the concentration step following SAFE, can be compensated by the static headspace method, which in turn gives low yields for rather high boiling odorants and offers only limited possibilities for compound enrichment and fractionation. However, structure assignment of the highly volatile compounds is less demanding, as the structural variation is much more limited in this molecular weight range. Applicability and success of this twofold strategy has been demonstrated in numerous studies which finally led to the identification of the key odorants in the investigated food. Recent examples include studies on peanuts,81,82 and durian.83,84

9.5 Odorant Ranking Approaches in GC-​O The number of odor-​active compounds detected in a GC-​O analysis depends on the nature and amount of starting material, work-​up parameters such as the degree of concentration of SAFE isolates and the GC injection volume. For a meaningful GC-​O screening, these parameters should be adjusted in order to detect ~30–​60 odor-​active compounds in the run. A lower number increases the risk of overlooking important compounds, whereas a higher number may include many compounds which are of minor or no importance for the overall olfactory profile of the investigated food (cf. Section 9.7).

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Even if a reasonable number of compounds has been detected in the GC-​O screening experiment, it can be helpful to rank them according to their odor potency. A successful ranking allows concentration of the effort of structure elucidation (cf. Section 9.6), and all further steps toward the identification of the key odorants in the food under investigation (cf. Section 9.7), on the most potent compounds in the extract, first. If need be, less-​potent odorants may additionally be considered after reconstitution experiments (cf. Section 9.7.3) have shown that the model is yet incomplete. To assess the potency of the individual odor-​active compounds detected by GC-​O, numerous approaches have been suggested, which follow one of three basic principles: intensity assessment, determination of detection frequency or dilution to threshold.85,86

9.5.1 Odorant Ranking by Intensity Measurement At first sight, it seems to be the easiest option to rank the odor-​active compounds according to their odor intensity, as perceived during GC-​O. A simple method is to assess the maximum intensity perceived during elution of the odorant and note it after the end of the stimulus as a predefined value on a fixed scale (e.g., 1 = weak, 2 = moderate, etc.).12,87–​89 This approach has sometimes been referred to as “posterior intensity” measurement.90–​92 A more elegant method uses an electronic device to continuously assess the perceived intensity over the whole run. The device is, for example, a slider as commercially available from Brechbühler or a scroll bar included in a software product and operated by a computer mouse.27,93 The continuous assessment of the odor intensity at the sniffing port results in a chromatogram-​like response plot from which either peak heights or peak areas can be extracted as intensity measures for the individual odorants. Data from multiple sniffers may be combined. Some approaches for continuous intensity assessment were given specific names such as OSME (from the ancient Greek word ὀσμή for smell)94,95 and “finger span method”.27 A major problem in intensity measurement approaches is that the perceived intensity is not proportional to the concentration of the odor-​active compound. Instead, above the threshold concentration the intensity first increases with the logarithm of the concentration and then, at higher concentrations, reaches a saturation value.86 Different odor-​active compounds exhibit different slopes and different saturation values. The major drawback of an intensity rating thus is that no meaningful rating is possible for intense odorants. This, however, might be crucial, particularly when GC-​O is applied to get an insight into the compounds responsible for the odor difference between two or more samples.

9.5.2 Odorant Ranking by Detection Frequency Ranking approaches based on detection frequency use the percentage of panellists that detected a specific odorant during GC-​O as measure for its potency. Reported panel sizes range from 6 to 12.90,96–​100 The duration of the

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olfactory stimuli may be recorded as well, e.g., by pressing and holding a button during odor perception, and included into the potency assessment.98,99 A combination of detection frequency and intensity rating has also been reported.100 Regarding the evaluation of intense odorants, the detection frequency approach includes the same major drawback as intensity rating methods. Actually, in a detection frequency approach performed without stimili duration recording, rating of intense odorants is totally impossible, as they would all yield a detection frequency of 100% (end-​of-​scale effect86).

9.5.3 Odorant Ranking by Dilution to Threshold Ranking of odor-​active compounds following the dilution to threshold idea is based on a series of consecutive GC-​O analyses with stepwise reduction of the injected sample amount. The reduction factor is typically 2 or 3. If the odorant isolates are dissolved in an organic solvent, a dilution series is prepared to obtain dilutions of 1 : 2, 1 : 4, 1 : 8, etc. or 1 : 3, 1 : 9, 1 : 27, etc. of the initial odorant isolate, whereas injection volume is kept constant.101,102 When using the static headspace approach, the injected gas volume is stepwise reduced.103 If odor-​active compounds are sampled from the headspace by using SPME or SBSE, the sample amount entering the GC column can be controlled by adjusting the split ratio at the injector.104,105 In all cases, reduction of the injected sample amount is continued until the sniffer cannot detect any substance at the sniffing port in the whole GC run. An aroma extract dilution analysis (AEDA) is a dilution to threshold approach applied to organic solvent extracts.102 After the GC-​O analyses of the sequentially diluted extract, each odor-​active compound is assigned a flavor dilution (FD) factor, representing the dilution factor of the highest diluted sample, whose GC-​O analysis resulted in the detection of the compound at the sniffing port (Figure 9.8). For example, a substance that has been detected by GC-​O of the undiluted isolate as well as in the 1 : 2 and 1 : 4 diluted isolate, but not in the 1 : 8 diluted sample, would receive an FD factor of 4 such as odorants 2 and 5 in Figure 9.8. When AEDA data of multiple sniffers are combined, for each odorant the highest FD factor determined by any of the sniffers should be selected,26 rather than an averaged FD factor,83 to avoid underestimation of important odorants. An overestimation, on the other hand, is easily corrected

Figure 9.8 Principle of aroma extract dilution analysis: stepwise dilution, GC-​O analysis and FD factor calculation.

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by using the approaches for the substantiation of GC-​O data discussed in Section 9.7. An aroma extract concentration analysis (AECA)106 basically follows the same idea as an AEDA. However, instead of concentrating e.g., a SAFE distillate first and then preparing stepwise diluted samples from the concentrate, the distillate is made up to a defined volume and used for an initial GC-​O analysis. More diluted samples are prepared as in an AEDA, while the more concentrated samples are achieved by stepwise concentration of the distillate. For most odorants, the results of AECA and AEDA agree. However, AECA would result in more representative FD factors for odorants that undergo changes during distillate concentration due to evaporation, decomposition or artifact formation.70 In comparison to AEDA, AECA is rarely applied, most probably due to the higher effort needed for the preparation of the diluted samples. When applying the dilution to threshold approach to static headspace samples, FD factors can be calculated in a similar manner as detailed for AEDA. The FD factor of a compound is then obtained by dividing the starting volume by the lowest headspace volume, whose GC-​O analysis resulted in the detection of the compound at the sniffing port.83 For example, if a static headspace dilution analysis is started with an injection volume of 5 mL and a specific compound is detected at the sniffing port after injection of 5, 2.5, 1.25, 0.625 and 0.3125 mL, but not after injection of 0.15625 mL, its FD factor would be 5 mL /​0.3125 mL = 16. As previously mentioned, in SPME and SBSE dilution approaches, FD factors can be calculated from the split ratio.104,105 A CHARM analysis (CHARM = combined hedonic aroma response measurement) is a dilution to threshold method which not only records the presence of odor-​active amounts of a compound during GC-​O of serially diluted samples but additionally includes the duration of the olfactory stimuli into the calculation of so-​called CHARM values.101 Stimuli durations can be recorded by simply pressing and holding a (computer mouse) button. The advantage of CHARM values over FD factors is a better assessment of odorants showing badly shaped peaks such as broad and flat peaks or peaks with strong tailing. These odorants could be underestimated by FD factor calculation.86 The major advantage of dilution to threshold approaches over intensity assessment and detection frequency approaches is the better ranking of odor-​active compounds with high odor potency. This is of particular importance if GC-​O screening is used for the comparison of two or more samples. Practically, comparisons of different food variants are of high importance and include, for example, the comparison of different plant varieties,33,107,108 processing steps,109,110 and the comparison of a sample with an off-​flavor and a reference sample with flawless sensory characteristics111 (cf. Sections 9.8.2–​9.8.4). When the samples are subjected to identical work-​ups, FD factors and CHARM values of each odor-​active compound are proportional to its concentrations in the respective samples. For example, a comparative dilution to threshold approach such as a comparative AEDA (cAEDA)66 in parallel applied to extracts obtained from an off-​flavor sample and a reference sample without off-​flavor, could result in FD factors of 2048 and 256 for the most

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potent compound, respectively, clearly suggesting that this compound plays a major role for the off-​flavor. By contrast, detection frequency approaches would probably return no difference, because in both samples the compound was detected by 100% of the panelists. The same could apply for intensity measurement approaches, if the compound was yet present in both extracts beyond its saturation level. Advantages and disadvantages of different odorant ranking approaches have been discussed in numerous previous papers and reviews.31,85,86,91,112–​117 However, discussions were sometimes misleading. It was often presumed that an appropriate ranking approach should be able to provide reliable conclusions on the importance of individual odorants in the food material analyzed, preferentially with untrained panelists and within little time. Yet neither of these claims is reasonable. Panel training is definitely a key to success and even trained sniffers need some time to become familiar with new types of samples. Consequently, the time spent on the final odorant ranking experiment is marginal compared to the time needed for familiarizing the sniffers with GC-​O of the material (cf. Section 9.3.4). Moreover, reliable conclusions on the importance of odorants in the analyzed food are never possible with GC-​O alone. Regardless of what kind of odorant ranking approach is applied, GC-O results can only be considered as preliminary screening data which allow to identify candidate compounds for potential key food odorants, but necessarily need to be substantiated by further methods (cf. Section 9.7). Thus, all discussed ranking approaches can be useful if their general and individual limitations are adequately considered. Given their better performance in comparative approaches, dilution to threshold methods are clearly preferred in the field. Among them, AEDA is the most widely used (352 p ­ apers118), most probably because it does not necessarily require sophisticated data acquisition instrumentation.

9.6 Structure Assignment of Odorants Detected by  GC-​O As detailed earlier, the primary outcome of a GC-​O analysis is a list of odor qualities and associated retention indices (cf. Section 9.2, Table 9.2). Application of an odorant ranking approach (cf. Section 9.5) adds a numerical ranking parameter to each element in the list. The identity of the compounds responsible for the perceived odors, however, is still to be determined. An ingenuous GC-​O user may get the idea that using a mass spectrometer as second detector easily solves the problem. The mass spectrum obtained at the retention time of an odor event at the sniffing port would be compared to spectra compiled in a commercial database such as NIST 17,119 and good agreement would indicate successful structure assignment. In reality, however, it is quite unlikely to achieve a correct structure assignment by using this approach. It may happen, if the odorant is a major volatile. But owing to the high diversity of odor threshold values (cf. Section 9.1, Table 9.1), the odor-​active compounds are often minor or trace constituents in the volatile

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fraction. Thus, the mass spectrum obtained at the retention time of an odor event may be associated with a co-​eluted odorless compound and not with the compound accounting for the odor. Nevertheless, together with the retention index and the odor quality, the mass spectrum is an important parameter for the unequivocal structure assignment of odor-​active compounds. However, it is typically more productive to first compare the retention index and the odor quality of each odor detected during GC-​O with published data. As the number of odor-​active compounds in food is limited,32 this often already leads to sound structure proposals. Odor and retention data compiled in some commercial databases such as NIST 17,119 and VCF online,120 facilitate this approach. As a next step, structure proposals need to be confirmed by GC-​O analysis of reference compounds. Reference compounds that are not commercially available need to be synthesized and have their structures confirmed by nuclear magnetic resonance (NMR) experiments. The GC-​O analysis of reference compounds should be performed in direct comparison with GC-​O of the food volatile isolate, which means by consecutive runs on the same instrument. A meaningful comparison furthermore requires that substance amounts at the sniffing port are in the same range during GC-​O of the food volatile isolate and GC-​O of the reference compound, which probably necessitates analysis of the reference compound in different dilutions. In case GC-​O analysis of the reference compound confirmed the structure assignment, a second GC-​O comparison should be performed by using a stationary phase of different polarity (cf. Section 9.3.2) for further structure validation. Finally, GC-​ MS is used to further confirm the preliminary structure assignments based on retention index and odor, and to develop structure proposals for odorants for which such a preliminary structure assignment was not successful. To obtain overlap-​free mass spectra of the odor-​active compounds in the volatile food isolate, co-​eluted substances need to be separated. This can be achieved by different offline and online fractionation techniques. After fractionation, the individual odor-​active compounds are localized in the fractions by GC-​O before the fractions are subjected to GC-​MS. Offline fractionation approaches frequently applied include acid–​base extraction, liquid chromatography using silica gel and selective isolation of thiols by mercurated agarose gel. Online fractionation can be achieved by two-​ dimensional GC with heart-​cutting. Examples of use can be found in the literature: acid–​base extraction,33,83,121,122 silica gel chromatography,26,33,83,107,121,122 mercurated agarose gel26,83,107 and two-​dimensional GC with heart-​cutting.107 To increase the effect of fractionation, different approaches can be applied successively. For example, a first fractionation is achieved by acid–​base extraction, then the fraction containing the neutral compounds is subfractionated by silica gel chromatography and finally the subfraction containing the target compound is subjected to two-​dimensional GC with heart-​cutting. Two-​dimensional GC with heart-​cutting is a very powerful fractionation tool, extremely helpful particularly for the structure elucidation of previously unknown odor-​active compounds (see Chapter 6). A system applicable for this

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Figure 9.9 Configuration of a GC-​O/​FID-​GC-​O/​MS system for two-​dimensional gas chromatography with heart-​cutting employing a Deans’ switch as flow-​ switching device.

approach is exemplified in Figure 9.9. It basically consists of two gas chromatographs in series. The end of the column in the first gas chromatograph (B in Figure 9.9) is connected to a Deans’ switch123 (C) that transfers the effluent either simultaneously to an FID (G) and a sniffing-​port (H) or via a heated transfer line (I) and a cold trap (K) to the column in the second gas chromatograph (L). The columns in the first and second dimensions differ in polarity, e.g., a polar column such as a Wax or FFAP column is used in the first and a non-​polar column such as a dimethylpolysiloxane column or a dimethylpolysiloxane column with 5% phenyl groups is used in the second dimension, or vice versa. Using a splitter (M; cf. Section 9.3.3), the effluent of the second column is simultaneously transferred to a second sniffing port (N) and a mass spectrometer (O). In a first run, the food volatile isolate or a fraction thereof containing the target compound is separated on the first dimension while auxiliary gas 1 (D in Figure 9.9) of the Deans’ switch is off and auxiliary gas 2 (E) is on, thus the entire column effluent is transferred to the FID and the sniffing port. The retention time of the target compound is determined according to its detection at the sniffing port. Then, the sample is injected again. During a time frame of ~0.5 min starting shortly before elution of the target compound and ending shortly thereafter, auxiliary gas 1 is switched on and auxiliary gas 2 is switched off. This results in the transfer of a heart-​cut of the column effluent via the transfer line to the cooled trap located in the second GC oven. In the

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trap, the volatiles contained in the heart-​cut are cryofocused. Subsequently, the cryogenic gas supply (J) is switched off and the temperature program of the second GC oven is started. Thus, the trapped compounds including the target compound are re-​chromatographed in the second dimension, whereas the major part of potentially interfering compounds have been removed. Provided sensitivity and selectivity of the approach are sufficient, the mass spectrum of the target compound is obtained at the mass spectrometer exactly when its odor is detected at the sniffing port in the second dimension. If the sensitivity is insufficient, i.e., if no signal is obtained in the MS chromatogram, the amount of food used for the work-​up and/​or the degree of extract concentration must be adjusted. If the selectivity is insufficient, i.e., interfering matrix compounds are still present in the MS chromatogram, potential solutions include a reduced time frame for the heart-​cut, a different column combination for the first and second dimensions and further pre-​fractionation steps, prior to GC-​O/​FID-​GC-​O/​MS analysis. Once the expected mass spectrum has been obtained for an odor-​active compound, for which a preliminary structure assignment based on retention index and odor has been made, its identity is further confirmed by parallel GC-​MS analysis of the reference compound (same instrument, consecutive runs). For odor-​active compounds without preliminary structure assignment, a structure proposal is developed from the mass spectrum. At the best, a matching spectrum is found in a database such as NIST 17.119 If not, the compound probably has not been characterized before. Then, a structure proposal must be developed on the basis of the presumable molecular ion and the fragmentation pattern. To identify the molecular ion, an additional GC-​ MS analysis with a soft ionization approach, such as chemical ionization (CI), is often helpful. High-​resolution mass spectrometry (HRMS) can be used to determine the molecular formula of the unknown odorant. Further structural information can be derived from its GC retention behavior (size, polarity), its retention behavior during LC fractionation (polarity) and its odor quality (functional groups). Any structure proposal needs to be verified by GC-​O and GC-​MS of a structurally characterized reference compound as detailed above. Examples for the identification of novel odor-​active compounds can be found in Section 9.8.1 and in the literature.12,26,83,124 For chiral compounds, proper structure assignment must include the determination of the enantiomeric ratio, because enantiomers can significantly differ in their odor properties.125–​128 Today, the method of choice for analytical enantiomer separation of chiral odor-​active compounds is GC with a chiral stationary phase based on a substituted cyclodextrin (see Chapter 3). The chiral stationary phases available in the market differ in the size of the cyclodextrin (α, β, γ), the substitution of the hydroxy groups in 2, 3 and 6 position in the cyclodextrin (methyl, ethyl, phenyl, acetyl, tert-​butyldimethylsilyl) and the basic phase in which the cyclodextrin is dissolved (typically a polysiloxane). Application notes available on the manufacturer’s websites help to select the best column for prevalent chiral odorants. Otherwise, trial-​and-​error evaluation is required; however, for the majority of chiral odorants, β-​cyclodextrins

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yield the best results. A highly selective approach to determine the enantiomeric ratio of odor-​active compounds is the combination of two-​dimensional GC with heart-​cutting and MS detection (cf. Figure 9.9) with a chiral column in the second dimension (GC-​O/​FID-​enantioGC-​O/​MS). To determine the elution order of the enantiomers, at least one enantiomer must be at hand in sufficient enantiomeric purity. Examples for the determination of the enantiomeric ratio of odor-​active compounds can be found in the literature.12,26,83

9.7 GC-​O Data Interpretation and Substantiation 9.7.1 Limitations of GC-​O Results Although GC-​O analysis is a valuable tool for the activity-​guided screening for odor-​active compounds in food, care must be taken not to over-​interpret the results. In particular, GC-​O, even if combined with ranking approaches (cf. Section 9.5), does not allow unequivocal assessment of the contribution of individual compounds to the overall olfactory profile of a food. Two major factors contribute to this limitation. First, the volatile isolation method may cause qualitative and quantitative changes in odor-​active compounds (cf. Section 9.4). For example, the solvent extraction/​SAFE approach can lead to losses of high-​boiling compounds during SAFE,72 and highly volatile compounds are lost during distillate concentration. Organic solvents result in a more-​or-​less exhaustive extraction of odor-​active compounds regardless of their volatility and volatility modulating matrix effects in the native food, whereas GC-​O itself neglects the influence of compound volatility as compounds are totally volatilized when evaluated at the sniffing port. Static headspace techniques, on the other hand, include volatility and matrix effects, but discriminate high-​ boiling compounds due to adsorption phenomena. Adsorption could only be avoided by heat application; however, this would involve the risk of degradation of thermolabile compounds and formation of odor-​active artifacts. The second aspect that limits conclusions on the contribution of individual compounds to the overall olfactory profile of a food from GC-​O results is the fact that during GC-​O the odor-​active compounds are assessed individually, whereas during food consumption they are perceived as a mixture. In odorant mixtures, however, suppressive, additive and synthetic effects on the perception level considerably influence the relative importance of the individual odorants.13,84,129 Therefore, GC-​O results need to be substantiated by further assays, including the exact quantitation of the odor-​active compounds, calculation of odor activity values, odor reconstitution and omission experiments.

9.7.2 Odorant Quantitation and Calculation of Odor Activity Values The basis for further substantiation of GC-​O data is the determination of the exact concentrations of the odor-​active compounds in the food. If ranking approaches have been applied (cf. Section 9.5), quantitations can be focused

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Scheme 9.2 Exemplary stable isotopically substituted food odorants used as internal standards in quantitation assays.132–​135 Red dots indicate the positions of 13C atoms.

on the most potent odorants first. The need to apply mild sample preparation techniques in order to avoid artifact formation may result in low recoveries. Therefore, quantitation approaches that fully compensate for losses are an obliged choice (see Chapter 1). The gold standard for odorant quantitation in foods is the application of stable isotope dilution assays (SIDAs).66,130 In a SIDA, a stable isotopically substituted analog131 of the target compound is used as internal standard. As the commercial availability of such compounds is limited, most of them need to be synthesized first. Isotopical substitution is typically achieved by incorporation of deuterium or 13C-​atoms (Scheme 9.2). Deuteration is normally preferred, as it is cheaper and can be achieved by fairly simple synthetic approaches such as catalytic deuteration,52,134 reduction by metal deuterides,52,136 and enforced H/​D-​exchange.137 However, 13C-​ substitution is used for molecules, which include solely acidic hydrogen atoms such as butane-​2,3-​dione133 and 4-​hydroxy-​2,5-​dimethylfuran-​3(2H)-​ one132 (Furaneol®) to obviate H/​D-​exchange reactions during the work-​up. Incorporation of other isotopes than deuterium and 13C into stable isotopically substituted food odorants to be used in SIDAs is rarely applied. An example is hydrogen (34S)sulfide.138 The minimum degree of isotopical substitution is 2 in order to avoid interferences with the natural abundance of 13C. Degrees of isotopical substitution of 3 or 4 are suitable for most odorants including sulfur compounds.138 The isotopologue standard is added at the beginning of the work-​up. Then the mixture is homogenized until equilibrium has been reached between the target analyte and the added standard. Equilibration times differ and depend on the food matrix. Solid materials, even if powdered, require more time for equilibration than liquid foods (Figure 9.10). Once equilibration has been achieved, any loss of the analyte during further work-​up operations such as SAFE, fractionation and concentration is accompanied by a corresponding loss of the standard as their physical and chemical properties are virtually identical. Thus, the concentration ratio of analyte and standard remains constant. This ratio is finally determined by GC-​MS, utilizing the difference in the molecular weight between the two isotopologues for separate intensity measurement (Figure 9.11).

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Figure 9.10 Influence of the matrix on the time needed for equilibration of the target compound and the added isotopologue standard in a stirred mixture of the food material and the organic extraction solvent. Yellow line: quantitation of linalool in beer; green line: quantitation of 4-​ methyl-​4-​sulfanylpentan-​2-​one in hop powder.

Figure 9.11 Principle of a SIDA applied to the quantitation of odor-​active compounds in food (A, analyte; S, standard).

Chemical ionization MS is often preferred,18,84 because it typically provides intense molecular ions; however, electron ionization can also be applied, if the target molecule shows low fragmentation or if intense fragments still carrying the isotopic substitution are present.139 The concentration of each target compound is finally calculated from the area counts of the analyte peak, the area counts of the standard peak, the amount of starting material, and the amount of standard added, by employing a calibration line equation previously obtained from the analysis of analyte/​standard mixtures in known concentrations. Examples can be found in the literature.84,139,140 Stable isotope dilution assays even compensate for major losses, which makes the approach particularly valuable for the exact quantitation of highly reactive odor-​active compounds such as thiols.138,139 Quantitation of trace compounds

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typically requires pre-​fractionation operations to avoid co-​elution problems (cf. Section 9.6) and reduce background. Mass spectral interferences between analyte and standard complicate calculations, however, may be compensated by appropriate mathematical linearisation approaches.138,141 An alternative to SIDA for the compensation of work-​up losses during the quantitation of odor-​active compounds in food are standard addition approaches.142 However, different from SIDA, they require a work-​up procedure of high reproducibility. Another alternative is to completely avoid work-​ up losses by using exhaustive procedures. For example, the combination of exhaustive solvent extraction and direct GC injection without removal of non-​ volatiles has successfully been applied to quantitate odor-​active compounds among the major volatiles by GC-​FID.18,140 However, this approach is not applicable to minor and trace constituents. Another example of an exhaustive procedure is the combination of an exhaustive extraction with LC-​MS-​MS or extraction-​avoiding dilute-​and-​shoot LC-​MS-​MS. However, although the problem of work-​up losses can thus be solved, similar problems arise from the influence of matrix components on the ionization efficiency in LC-​MS quantitation approaches, which then again call for methods such as SIDA or standard addition. When finally the exact concentrations of the major odor-​active compounds in a food have been determined, they can be related to the odor thresholds of the compounds to allow conclusions on their individual odor activities. Odor thresholds can be determined according to the American Society for Testing and Materials (ASTM) procedure for the determination of odor and taste thresholds by a forced-​choice ascending concentration series method of limits.20 In brief, panellists are presented a series of 3-​alternative forced-​ choice (3-​AFC) tests, each including two samples with pure matrix (e.g., water, edible oil, hydroalcoholic solution, etc.) and a third sample containing the odorant dissolved in increasing concentrations in the same matrix. Panellists are asked to orthonasally identify the sample containing the odorant in each 3-​AFC test. Individual odor thresholds are then calculated for each panellist as a geometrical mean of the concentration of the lowest concentrated correctly assigned sample and the concentration of the highest concentrated incorrectly assigned sample. The geometrical mean of the individual thresholds yields the panel threshold. A quick and demonstrative method to relate the odorant concentrations to the respective odor thresholds is to simply calculate their ratio. The quotient of concentration and odor threshold as a measure of the odor activity of a compound in food was first proposed by Rothe and Thomas in 1963 and named with the German word Aromawert (aroma value).143 Later, the terms odor unit,144 odor value145 and flavor unit146 were suggested, even though the term odor activity value (OAV)147 is most widely used. The OAV of an odor-​active compound thus represents the factor the concentration of this compound in the food exceeds its odor threshold value. Odorants exhibiting an OAV < 1 do not normally contribute to the overall olfactory profile of the food. Odorants showing an OAV ≥ 1 may contribute to the overall olfactory profile, but not

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necessarily. Nevertheless, OAVs allow a better approximation of the relevance of an individual odor-​active compound for the overall olfactory profile than GC-​O data such as FD factors, because they are based on exact concentrations, include the influence of compound volatility and even consider matrix interactions, provided that an appropriate matrix was used for the threshold determinations.

9.7.3 Odor Reconstitution and Omission Experiments Although OAVs are a better tool to approximate the contribution of individual odor-​active compounds in a food to the overall olfactory profile than GC-​O data, interactions associated with the perception of odorant mixtures are still not considered. This requires the sensory evaluation of odor reconstitution models. Odor reconstitution, sometimes also referred to as odor recombination, aroma recombination, or as odor or aroma re-​engineering, in the first instance serves as proof of success for the preceding steps. Successful reconstitution particularly indicates that no odor-​active compound has been overlooked during screening. An odor reconstitution model is prepared from a model matrix mimicking the situation in the original food and the odor-​active compounds for which OAVs ≥ 1 have been calculated. The model matrix should at least reproduce water content, lipid content and pH of the original food, and if necessary also the concentrations of further major components that might influence the odorant release such as sugars, starch, etc. Using a sensory panel, the model is then orthonasally tested against the original food in a quantitative olfactory profile analysis also sometimes referred to as an aroma profile analysis. The intensities of appropriate olfactory descriptors taken from the trained flavor language (cf. Section 9.3.4) and previously defined by the panel using free choice profiling followed by an open discussion and a consensus decision are rated on an interval scale with anchors ranging from “not perceivable” to “intense”. In case of a successful odor reconstitution, the model clearly evokes the typical odor of the original food and the olfactory profiles are virtually congruent. An example of a successful odor reconstitution is shown in Figure 9.12, while further examples can be found in the literature.13,84 If the model clearly differs in its olfactory profile from the original food, most probably the selection of compounds for the quantitation experiments was too narrow and further odorants with lower rank (cf. Section 9.5) need to be included. After successful odor reconstitution, the contribution of the individual odor-​active compounds to the overall olfactory profile can be assessed by omission tests.148 In an omission test, a single odorant is omitted from the odor reconstitution model. This incomplete model is then tested against the complete model in a 3-​AFC test. If the test returns a significant difference, the omitted odorant has shown its relevance for the overall olfactory profile of the model and can thus be considered a key odorant in the analyzed food. The p-​value may be used as numeric approximation of its importance.

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Figure 9.12 Reconstitution of guava aroma: olfactory profile of the fruit material (left) and the aqueous reconstitution model including 13 odor-​active compounds in their natural concentrations (right).52

Omission tests have successfully been used to substantially simplify odor reconstitution models,13,52,84 but the approach also includes limitations.13 In most cases, 10–​20 odorants turned out to be sufficient to mimic the overall olfactory profile of a food, only in rare cases was the number of key odorants higher, but it was often considerably lower.32,84

9.8 Applications of GC-​O in Food Analysis 9.8.1 Using GC-​O to Discover Novel Odor-​active Compounds in Foods The total number of odor-​active compounds in food is surprisingly low. A recent review and meta-​analysis on OAV data of food odorants extracted from 119 papers revealed 226 odor-​active compounds with an OAV ≥ 1 in at least one of 227 different food samples.32 Considering the fact that >10 000 volatiles have been characterized in food so far, this means that only ~2% of the known food volatiles are odor-​active. Furthermore, the abundance of the individual odor-​active compounds in the food samples showed extreme differences. The 16 most abundant odorants exhibited OAVs ≥ 1 in more than 25% of the 227 food samples. These high-​abundant generalists included cooked potato-​ like smelling 3-​ (methylsulfanyl)propanal (methional; 54% abundance) and malty-​smelling compounds 2-​and 3-​methylbutanal (51%), followed by butane-​ 2,3-​ dione (buttery; 42%), (2E,4E)-​deca-​2,4-​ dienal (fatty; 41%), 4-​hydroxy-​2,5-​dimethylfuran-​3(2H)-​one (caramel-​like; 41%), hexanal (grassy; 40%), 3-​hydroxy-​4,5-​dimethylfuran-​2(5H)-​one (sotolon; soup seasoning-​like; 36%), oct-​1-​en-​3-​one (mushroom-​like; 35%), acetic acid (vinegar-​like; 29%), acetaldehyde (fresh; 29%), ethyl 2-​and 3-​ methylbutanoate (fruity; 28%), (2E)-​non-​2-​enal (fatty; 28%), vanillin (vanilla-​ like; 28%), 2-​acetyl-​1-​pyrroline (popcorn-​like; 26%), 2-​and 3-​methylbutanoic

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acid (cheesy; 26%), and butanoic acid (cheesy; 26%). On the other hand, there were compounds which were detected only in a small number of food samples or even only in a single one in amounts beyond their threshold value. Nevertheless, these low-​abundant specialists and individualists turned out to often significantly contribute to the specific sensory profile of the foods. Examples of such specialists and individualists include garlic-​like smelling diallyl disulfide in garlic,149 grapefruit-​like smelling 1-​p-​menthene-​8-​thiol in grapefruit,150 and coconut-​like smelling wine lactone in wine.151,152 Whereas generalists among the odor-​active compounds in food are typically derived from ubiquitous precursors such as amino acids, sugars and unsaturated fatty acids, the formation of specialists and individualists typically involves highly specific biochemical pathways leading to unique precursors, e.g., glucosyl (6E)-​6-​hydroxy-​2,6-​dimethylocta-​2,7-​dienoate, the precursor of wine lactone.153 In summary, the meta-​analysis resulted in valuable data on odor-​active compounds in the human diet; however, this cannot be considered as an ultimate result. The food samples evaluated covered a wide range of different types of food, but Western food and commercially important commodities were over-​ represented, while e.g., African and Asian foods were under-​represented and foods of little commercial turnover were hardly included. Nevertheless, it is unlikely that future research will vitally change our view on generalists, but it is most likely that numerous specialists and individualists are still awaiting discovery. Novel odorants may also be expected among compounds with low odor thresholds and among reactive compounds, because there is some chance that these compounds have been overlooked in previous studies. In any case, GC-​O will be the key tool to reveal these compounds. The assumption that particularly foods beyond the Western diet could be a rich source of novel odor-​active compounds has been substantiated by recent research on tropical fruits and herbs. Among the latter were curry leaves, that is the leaves of the curry tree Murraya koenigii syn. Bergera ­koenigii, which are widely used as seasoning herb in South Asian cuisines (cf. Section 9.2, Figure 9.4 and Table 9.2). Their overall olfactory profile is dominated by a strong sulfury and burnt odor note. Although numerous studies on curry leaf volatiles had been published and more than 60 compounds, among them mainly terpenoids and sesquiterpenoids, had been identified,154 the molecular basis of the unique sulfury and burnt odor remained completely unclear until recently. Finally, the application of GC-​O and an aroma extract dilution analysis to the volatiles isolated from curry leaves by solvent extraction and SAFE in combination with the structural assignment of the compounds with high FD factors revealed 1-​phenylethanethiol, linalool, α-​pinene, 1,8-​cineole, (3Z)-​hex-​3-​enal, 3-​(methylsulfanyl)propanal, myrcene, (3Z)-​hex-​3-​en-​1-​ol and (2E,6Z)-​nona-​2,6-​dienal as potent odorants (Table 9.5).26 Despite their odor potency, four of these nine compounds had not been identified in curry leaves before, confirming the strength of GC-​O methodologies for the identification of odor-​active compounds. Among the previously unknown compounds was sulfury and burnt-​smelling 1-​phenylethanethiol. Its unique odor and its high

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FD factor suggested that 1-​phenylethanethiol is the character impact compound of fresh curry leaf odor. To substantiate the assumed role of 1-​phenylethanethiol for the overall olfactory profile of curry leaves, the odorants depicted in Table 9.5 were quantitated and the concentration data were used to calculate OAVs. Separate OAVs were calculated for the enantiomers of the chiral curry leaf odorants 1-​ phenylethanethiol, linalool and α-​pinene. Their enantiomeric distribution had previously been determined by GC-​O-​enantioGC-​O/​MS with a chiral column in the second dimension.26 Results confirmed the crucial role of the 1-​ phenylethanethiol enantiomers for the overall olfactory impression of curry leaves (Table 9.6).18 Both isomers exhibited the same sulfury and burnt odor, but slightly differed in their odor thresholds values.26

Table 9.5  T  he FD factors of major odor-​active curry leaf volatiles as determined by AEDA.26 Odorant

Odor quality

FD factor

1-​Phenylethanethiol Linalool α-​Pinene 1,8-​Cineole (3Z)-​Hex-​3-​enal 3-​(Methylsulfanyl)propanal Myrcene (3Z)-​Hex-​3-​en-​1-​ol (2E,6Z)-​Nona-​2,6-​dienal

Sulfury, burnt Citrusy Resinous Eucalyptus-​like Grassy Cooked potato-​like Geranium leaf-​like Grassy Cucumber-​like

8 192 4 096 2 048 1 024 256 128 64 32 32

Table 9.6  The OAVs calculated for the major odor-​active curry leaf volatiles.18 Odorant

Odor quality

(3Z)-​Hex-​3-​enal (1S)-​1-​Phenylethanethiol (1R)-​1-​Phenylethanethiol (3R)-​Linalool Myrcene (1S,5S)-​α-​Pinene (3Z)-​Hex-​3-​en-​1-​ol (2E,6Z)-​Nona-​2,6-​dienal (3S)-​Linalool 1,8-​Cineole (1R,5R)-​α-​Pinene 3-​(Methylsulfanyl) propanal

Grassy 21 300 Sulfury, burnt 32.2 Sulfury, burnt 65.3 Citrusy 5 070 Geranium leaf-​like 27 600 Resinous 198 000 Grassy 3 090 Cucumber-​like 3.22 Citrusy 3 100 Eucalyptus-​like 1 450 Resinous 14 900 Cooked potato-​like 1.53

a

Concentration OT value (µg kg–​1) (µg kg–​1)a

OAV

0.12 180 000 0.00021 150 000 0.00054 120 000 0.087 58 000 1.2 23 000 170 1 200 3.9 790 0.0045 720 7.0 440 4.0 360 9.0 170 0.43 4

OT values were orthonasally determined in aqueous solution by the ASTM forced-​choice ascending concentration series method of limits.20

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Figure 9.13 Curry leaf and its key odorants (1R)-​and (1S)-​1-​phenylethanethiol.

In summary, 1-​phenylethanethiol was established as key odorant in curry leaves (Figure 9.13). 1-​Phenylethanethiol had not been reported in food before, even though it has been found as key odorant in the peel oil of Pontianak oranges, a product widely used as flavoring and fragrance material.136,155 At higher temperatures, 1-​phenylethanethiol undergoes elimination of hydrogen sulfide to form styrene.155 This probably explains why the compound has not been detected in older studies on curry leaf volatiles, which mainly utilized steam distillation for volatile isolation.26 Another tropical food which recently revealed previously unknown food odorants is cempedak, the fruit of Artocarpus integer (Thunb.) Merr., a tropical tree native to Southeast Asia. It is similar to jackfruit, which is the fruit of a closely related plant (Artocarpus heterophyllus Lam.) in the same genus. Whereas jackfruit is an important tropical fruit crop worldwide today, cempedak is rather locally grown in countries such as Indonesia, Malaysia, New Guinea and Thailand. Cempedak differs from jackfruit by having a smaller size, a softer flesh and a special odor. The overall olfactory profile of jackfruit combines sweet, fruity and malty notes. The olfactory profile of cempedak is similar, but includes an additional sulfury, smear cheese-​like odor note. To identify the odorants responsible for cempedak odor in general and the sulfury note in particular, the volatiles isolated by solvent extraction and SAFE from cempedak and jackfruit, respectively, were subjected to a comparative GC-​O analysis.12 Among the 33 odor-​active compounds in the GC-​O chromatogram, two compounds were detected in the cempedak volatile isolate but not in the isolate obtained from jackfruit and exhibited the characteristic sulfury, smear cheese-​like odor. A GC-​MS analysis followed by MS database search suggested the first eluted compound to be 2-​(methylsulfanyl)butane, which was confirmed by GC-​O and GC-​MS analysis of the commercially available reference compound. The second compound was proposed to be a homolog of 2-​(methylsulfanyl)butane with an additional methylene group. This assumption was based on the virtually identical odor quality, the shift in the retention index and the mass spectrum which showed an ion at m/​z 118 as most probable molecular ion. Furthermore, the fragmentation pattern in the mass spectrum well corresponded to the pattern observed in the spectrum of 2-​(methylsulfanyl)butane and particularly indicated the presence of a methylsulfanyl group, while excluding an ethylsulfanyl group. Consequently, the compound was 3-​methyl-​2-​(methylsulfanyl)butane or 2-​(methylsulfanyl)pentane. As neither compound was commercially available, they were synthesized from

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the corresponding alcohols and their structures were confirmed by NMR. Analysis of the synthesized compounds by GC-​O and GC-​MS in parallel to the fruit volatile isolate finally showed that the compound in cempedak was 2-​ (methylsulfanyl)pentane. 3-​Methyl-​2-​(methylsulfanyl)butane exhibited the same odor, but differed in RI and in the intensities of major fragments in the mass spectrum. The enantiomeric ratios of 2-​(methylsulfanyl)butane and 2-​(methylsulfanyl)pentane were determined by GC-​O-​enantioGC-​O/​MS. For this purpose, enantiopure compounds were synthesized as reference. For 2-​(methylsulfanyl)butane as well as for 2-​ (methylsulfanyl)pentane, both enantiomers exhibited the sulfury, smear cheese-​like odor, but somewhat differed in odor thresholds. In cempedak, the slightly less odor-​active (S)-​configured compounds predominated with 67% and 59%, respectively.12 The GC-​O data were finally substantiated by quantitation of major odor-​ active compounds in cempedak and jackfruit followed by odor reconstitution experiments. The results confirmed the specific role of 2-​(methylsulfanyl) butane and 2-​(methylsulfanyl)pentane in the olfactory profile of cempedak (Figure 9.14).156 Neither compound had not been reported in cempedak before and their role as food odorants was unknown, although 2-​(methylsulfanyl)butane had earlier been identified as a volatile in asafoetida157 and 2-​ (methylsulfanyl)pentane had been reported in fermented fish sauce.158 Further novel odor-​active compounds were recently discovered by application of GC-​O and AEDA to durian. Durian, the fruit of Durio zibethinus L., is another tropical fruit native to Southeast Asia. The fruit is infamous for its strong and sulfury odor which was shown to be associated with a series of 1,1-​dithio compounds, among them mainly dithiohemiacetals and dithioacetals.83 All of them were structurally derived from acetaldehyde or propanal on the one hand and hydrogen sulfide, methanethiol, ethanethiol and/​or propane-​1-​thiol on the other hand.83,159 Three compounds, namely 1-​(propylsulfanyl)ethanethiol, 1-​{[1-​(methylsulfanyl)ethyl]sulfanyl}ethanethiol and 1-​ {[1-​ (ethylsulfanyl)ethyl]sulfanyl}ethanethiol were reported for the first time in nature. Quantitation of odor-​active compounds with high FD factors allowed for reconstitution of durian odor.84,138 Omission experiments

Figure 9.14 Cempedak: whole fruit (A), opened fruit (B), single seed with edible aril (C) and key odorants 2-​(methylsulfanyl)butane and 2-​(methylsulfanyl) pentane.

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Figure 9.15 Durian: opened fruit (A), single seed with edible aril (B) and key odorants ethyl (2S)-​2-​methylbutanoate and racemic 1-​(ethylsulfanyl) ethanethiol.

finally revealed that two compounds when combined in their natural concentrations were sufficient to evoke the characteristic odor of durian. These key odorants were fruity-​smelling ethyl (2S)-​2-​methylbutanoate and roasted onion-​like smelling rac-​1-​(ethylsulfanyl)ethanethiol (Figure 9.15).84 Although 1-​(ethylsulfanyl)ethanethiol had been reported in durian before,160 neither its enantiomeric ratio nor its crucial role for the overall olfactory profile of durian had been known. Ethyl-​2-​methylbutanoate, on the other hand, was already suggested as major fruity odorant in early studies on durian odor.161,162 Additional compounds previously unknown as food odorants which have recently been revealed by application of GC-​O approaches include (1R,4S)-​ calamenene, 2-​ethoxy-​2-​methyloxane, 6-​methylheptanal and 7-​methyloctanal. trans-​Calamenene has frequently been reported in essential oils, but was not known as an odor-​active compound before it was recently identified with a clove-​like, herbaceous odor in Spondias mombin fruits.122 Synthesis of the enantiopure reference compounds revealed that it was pure (1R,4S)-​calamenene.140 Meanwhile, the compound has also been identified in curry leaves and hops.140 2-​Ethoxy-​2-​methyloxane was detected as potent fruity and almond-​like smelling odor-​active compound in fresh tomatoes.163 First, the basic structure of a 2-​ ethoxymethyloxane was derived from mass spectral data. Synthesis of potential candidate molecules followed by GC-​O analysis revealed the typical odor but differing RIs for 2-​ethoxy-​3-​methyloxane, 2-​ethoxy-​4-​methyloxane, 2-​ ethoxy-​5-​methyloxane and 2-​ethoxy-​6-​methyloxane and thus finally showed the compound in tomato to be 2-​ethoxy-​2-​methyloxane. 6-​Methylheptanal and 7-​ methyloctanal were identified among the less-​potent odor-​active compounds in curry leaves.26 Their mass spectral data in combination with their odor quality were misleading to octanal and nonanal in the first instance, exemplifying the importance of the RI as additional identification parameter.

9.8.2 Using GC-​O to Substantiate Varietal Aroma Differences As already discussed in Section 9.5.3, the combination of GC-​O with dilution to threshold approaches is a powerful tool to get an insight into the odor-​active

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compounds contributing to the odor difference between two or more samples analyzed in parallel. An important application is the comparative analysis of different plant varieties. The varietal impact on food aroma can be quite big and have a vital influence on the popularity of foods such as fruits, vegetables, herbs and spices. If the key odorants responsible for the aroma differences between varieties are known, they can be used to define breeding targets toward a food with improved consumers’ acceptance. This is of particular importance for fruit breeding. In the past, fruit breeding was focused on three major targets: yield, disease resistance and shelf life. Optimization of these three parameters often led to inferior aroma characteristics. On the other hand, varieties with superior aroma are often only of local economic importance, because they are deficient in yield, disease resistance and/​or shelf life. In collaboration with biochemists, the knowledge of the key odorants will allow identification of the crucial precursors and metabolic pathways involved in their formation and thus allow for targeted breeding trials which finally will result in varieties being good in yield, disease resistance, shelf life and aroma. A fruit with great varietal aroma differences is mango. This is most probably associated with its extraordinary long history of cultivation, believed to be of the range of 4 000 years. A recent study compared the odor-​active compounds in five mango cultivars of different olfactory profiles among which were Haden, characterized by a strong fruitiness, White Alfonso with a pronounced terpene-​like odor note, grassy-​smelling Praya Sowoy, Royal Special whose aroma was reminiscent of raspberries, and Malindi with a very balanced type of mango aroma.107 Application of a cAEDA revealed sweet and caramel-​like smelling 4-​hydroxy-​2,5-​dimethylfuran-​3(2H)-​ one as the compound with the highest FD factor in all five mango cultivars, thus suggesting that this compound vitally contributes to the cultivar-​independent sweet odor note in mango. 4-​Hydroxy-​2,5-​dimethylfuran-​3(2H)-​one had been reported in mangoes before, but its relevance for the overall olfactory profile of mangoes had not been recognized. On the other hand, the cAEDA results also revealed clear candidate compounds most likely accounting for the variety-​specific odor notes. For example, the extraordinary strong fruity odor of Haden was associated with high FD factors of fruity smelling esters, in particular ethyl butanoate and ethyl 3-​methylbutanoate, but also ethyl 2-​methylpropanoate and ethyl 2-​methylbutanoate. The terpene-​like odor note in White Alfonso coincided with high FD factors for terpeny smelling compounds (Z)-​β-​ ocimene and (E)-​β-​ocimene, whereas the grassy odor note in Praya Sowoy could be ascribed to the grassy-​smelling aldehydes (3Z)-​hex-​3-​enal and (3E)-​ hex-​3-​enal, while β-​ionone and dehydro-​β-​ionone presumably accounted for the distinct raspberry note in Royal Special mangoes (Figure 9.16). Another fruit with some varietal and interspecific aroma differences is kiwifruit. The dominating variety in the global market is the green-​fleshed Actinidia deliciosa ‘Hayward’, with an aroma mainly characterized by green and grassy notes. The economic relevance of Hayward kiwifruits is strongly associated with their excellent storage stability. On the other hand, there are kiwifruits with superior aroma but low economic relevance due to a much

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Figure 9.16 Odorants contributing to the specific odor notes in different mango varieties as revealed by application of GC-​O in combination with a cAEDA. Adapted from Ref. 107 with permission from American Chemical Society, Copyright 2014.

lower storage stability. Among these are cultivars of the hardy kiwifruit Actinidia arguta. The fruits of A. arguta are only of the size of a table grape and their skin is soft and edible. Depending on the variety, their aroma can be much more intense than that of Hayward kiwifruits and exhibit strong fruity and floral notes. Application of GC-​O to extracts obtained from fruits of three A. arguta varieties, namely ‘Ananasnaya’, ‘Bojnice’ and ‘Dumbarton Oaks’ (Figure 9.17) as well as from common Hayward kiwifruits recently resulted in a total of 53 odor-​active compounds.33 A cAEDA revealed differences between the individual kiwifruit varieties particularly in (3Z)-​hex-​3-​enal, ethyl butanoate, ethyl 2-​methylbutanoate, methyl benzoate and ethyl benzoate, whereas 4-​hydroxy-​2,5-​dimethylfuran-​ 3(2H)-​one was again identified as variety-​independent odor-​active compound (Table 9.7). Results suggested that a high concentration of grassy-​smelling (3Z)-​hex-​3-​enal in combination with a low concentration of fruity smelling esters characterized the typical olfactory profile of the Hayward kiwifruits, whereas the intense fruitiness of A. arguta kiwifruits was associated with high concentrations of ethyl butanoate and low concentrations of (3Z)-​hex-​3-​enal. In the A. arguta fruits of the variety Dumbarton Oaks high concentrations of ethyl 2-​methylbutanoate additionally contributed to the fruity note. The floral note in the aroma of Bojnice und Dumbarton Oaks fruits corresponded to odor-​active amounts of methyl benzoate and ethyl benzoate, neither of which were detected in the A. deliciosa ‘Hayward’ and A. arguta ‘Ananasnaya’ fruit extracts.

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Figure 9.17 Fruits of the Actinidia arguta cultivars Ananasnaya, Boynice and Dumbarton Oaks.

Table 9.7  T  he FD factors of major odor-​active volatiles in kiwifruits of different varieties as determined by a cAEDA.33 Odorant

A. deliciosa ‘Hayward’

A. arguta ‘Ananasnaya’

A. arguta ‘Bojnice’

A. arguta ‘Dumbarton Oaks’

HDMFa (3Z)-​Hex-​3-​enal Ethyl butanoate Ethyl 2-​methylbutanoate Ethyl benzoate Methyl benzoate

2 048 1 024 4 32