Smart and Sustainable Food Technologies 9811917450, 9789811917455

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Shalini Sehgal Barinderjit Singh Vasudha Sharma   Editors

Smart and Sustainable Food Technologies

Smart and Sustainable Food Technologies

Shalini Sehgal • Barinderjit Singh • Vasudha Sharma Editors

Smart and Sustainable Food Technologies

Editors Shalini Sehgal Department of Food Technology Bhaskaracharya College of Applied Sciences, University of Delhi New Delhi, India

Barinderjit Singh Department of Food Science and Technology I. K. Gujral Punjab Technical University Kapurthala, Punjab, India

Vasudha Sharma Department of Food Technology Jamia Hamdard New Delhi, India

ISBN 978-981-19-1745-5 ISBN 978-981-19-1746-2 https://doi.org/10.1007/978-981-19-1746-2

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

Approaches in food processing are evolving with time to complement sustainable food security and much required food safety. In this changing scenario, the role of digitization is rapidly expanding and provoking essential changes to advance food processing practices in a smart and sustainable way. Various digital technologies are revolutionizing and breaking barriers in the food chain, from production to consumption, especially in minimizing food loss and wastage. In future scenarios, novel technologies like 3D printing of food, image processing, integration of artificial intelligence, machine learning, blockchain and smart packaging are going to be the key elements in ensuring a safe and sustainable food system. Apart from this, climate change is another grave matter of concern in the global scenario. Therefore, robust and resilient adaptation and employing smart agricultural interventions are the need of the hour. This not only needs the right technology, rather due capacity building, regulations and policies are to be brought in place to promote efficient transformation in agricultural practices. This comprehensive book contains four sections: the first section covers the recent smart food production innovations such as precision agriculture, indoor vertical farming, automation, robotics, livestock technology, modern greenhouse practices, artificial intelligence and blockchain. The second section provides the current knowledge and developments related to the recent smart technologies in manufacturing pertaining to various food sectors, non-thermal food preservation technologies, food packaging and 3D printing. The third section covers smart technologies such as biosensors to ensure food safety in the supply chain. Finally, the fourth section covers topics relevant to the minimization of waste and maximization of co-product recovery in food processing; upcycling technologies in food and sustainable value stream mapping in the food industry. I am extremely happy and congratulate the editors Dr. Shalini Sehgal, Dr. Barinderjit Singh and Dr. Vasudha Sharma for seeing the need for such a book and producing this book “Smart and Sustainable Food Technologies”. This is a welledited book, and its material will be a great resource for graduate students, scientists and technocrats. I would say, the book is a genuine preview of the next 15 years timeline as far as advancements in food processing technologies are concerned. v

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Moreover, this book is an ideal resource for policymakers to draft policies anticipating the future advancement in food processing. Overall, the content provided in this book is highly scientific with most updated information and advancements made in the field of food technology and food supply chain. Without any reservation, I strongly recommend this book to food technologists, food engineers, students and faculty in food processing technologies and policymakers. I wish all the success to the book and all editors and authors. Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD, USA

Kasiviswanathan Muthukumarappan

Preface

This book covers the smart technologies applicable in food production, food manufacturing, food packaging, food monitoring and surveillance, food supply chain and food waste management along with coproduct recovery. This book consists of four sections: the first section consists of chapters primarily focusing on the recent smart food production innovations such as precision agriculture, vertical farming, automation, robotics, smart livestock technology, modern greenhouse practices, artificial intelligence and blockchain technology that dramatically increase the quality of raw materials for the food industry and also the use of tools to forecast issues that affect food and agriculture on the planet. The second section provides the current knowledge and developments related to the recent smart technologies in manufacturing pertaining to various food sectors, non-thermal food preservation technologies, 3D printing and food packaging, developed for the food manufacturing industries that improve the organoleptic and nutritional quality, enhance chemical and microbial safety, as well as cost-effectiveness and convenience of processed foods. The third section covers smart technologies to ensure food safety in the supply chain, monitoring and surveillance of food contamination and neural network approach for risk assessment. The fourth section covers topics relevant to the minimization of waste and maximization of co-product recovery in food processing, upcycling technologies in food and sustainable value stream mapping in the food industry. This section covers both general and practical knowledge and information about the current and potential waste treatment methods that help food technologists, environmental and agricultural engineers/scientists in industries and governmental entities in their quest to improve food and agricultural waste management and generate value from waste. In conclusion, the book provides the most updated information and advancements made in the field of food technology and food supply chain using IoT. New Delhi, India Punjab, India New Delhi, India

Shalini Sehgal Barinderjit Singh Vasudha Sharma

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Contents

Part I

Smart Farming for Food Production

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Smart and Sustainable Food Production Technologies . . . . . . . . . . Anuj Kumar, Shantanu Kumar Dubey, R. Sendhil, A. K. Mishra, Uma Sah, Truptimayee Suna, and Ramesh Chand

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Smart Technologies in Livestock Farming . . . . . . . . . . . . . . . . . . . . Amandeep Singh, Y. S. Jadoun, Parkash Singh Brar, and Gurpreet Kour

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Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in the Management of Aquaculture Production Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. K. Das, D. K. Meena, Akankshya Das, and A. K. Sahoo

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Smart and Automatic Milking Systems: Benefits and Prospects . . . Suvarna Bhoj, Ayon Tarafdar, Mukesh Singh, and G. K. Gaur

Part II

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Smart Food Manufacturing

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Smart Technologies in Food Manufacturing . . . . . . . . . . . . . . . . . . 125 Rahul Vashishth, Arun Kumar Pandey, Parinder Kaur, and Anil Dutt Semwal

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Non-thermal Food Preservation Technologies . . . . . . . . . . . . . . . . . 157 Ravneet Kaur, Shubhra Shekhar, Sahil Chaudhary, Barinderjit Singh, and Kamlesh Prasad

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3D Printing: Technologies, Fundamentals, and Applications in Food Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Mohammed A. Bareen, Jatindra K. Sahu, Sangeeta Prakash, and Bhesh Bhandari

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Smart Food Packaging Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Aastha Bhardwaj, Nitya Sharma, Vasudha Sharma, Tanweer Alam, and Syed Shafia

Part III

Smart Food Safety in Food Supply Chain

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Smart Monitoring and Surveillance of Food Contamination . . . . . . 263 Shalini Sehgal, Sunita Aggarwal, Ashok Saini, Manisha Thakur, and Kartik Soni

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Neural Network Approach for Risk Assessment Along the Food Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Uma Tiwari

Part IV

Sustainable Food Waste Management and Coproduct Recovery

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Waste Minimization and Management in Food Industry . . . . . . . . . 309 Rahul Kumar, Vasudha Sharma, and Maria Jose Oruna-Concha

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Co-Product Recovery in Food Processing . . . . . . . . . . . . . . . . . . . . 341 Abhay Tiwari, Garima Singh, Kanika Chowdhary, Gaurav Choudhir, Vasudha Sharma, Satyawati Sharma, and Rupesh K. Srivastava

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Upcycling Technologies in the Food Industry . . . . . . . . . . . . . . . . . 367 Rubeka Idrishi, Divya Aggarwal, and Vasudha Sharma

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Sustainable Value Stream Mapping in the Food Industry . . . . . . . . 393 Himanshi Garg and Soumya Ranjan Purohit

About the Editors

Shalini Sehgal is presently working as an Associate Professor in the Department of Food Technology at the Bhaskaracharya College of Applied Sciences (University of Delhi), India. Dr. Sehgal holds a Doctorate in Dairy Microbiology from National Dairy Research Institute (N.D.R.I), Karnal, India. She has 23 years of experience in the field of academics and research along with administration. She has been awarded the Award of Excellence in Food Technology by AATSEA, Thailand, and Society of Applied Biotechnology, India, and Best Teacher Award by the State Government of Delhi, India. She is the former Director, Quality Assurance and Food Fortification at Food Safety and Standards Authority of India (FSSAI), GOI. Her area of interest is Food Safety, and she is trained in Laboratory Quality Management Systems (as per ISO 17025), HACCP Implementation, IS 22000: Food Safety Management System and Food Safety and Food Hygiene. Also she has expertise in Container Integrity and undergone training by USFDA at their Alameda Lab, California, USA. She has worked as National Food Safety Consultant with WHO and also undertaken projects on safety aspects of street foods, fresh produce and probiotics. Dr. Sehgal has authored two books in the area of Chemical and Microbiological testing of Food and chapters on different areas of Food Microbiology and Food Safety. She has published her research work in journals of repute. She has introduced the concept of Better Process Control School in India for the food industry professionals in collaboration with USFDA, India office. Dr. Sehgal has been instrumental in introducing Food xi

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About the Editors

Safety as a core subject across India. Currently, She is President, AFSTI, Delhi Chapter, and member of Technical and Regulatory board of AIFPA along with board of studies of different universities.

Barinderjit Singh earned a B.Tech. (Food Technology), M.Tech. (Food Engineering and Technology), Ph.D. (Food Technology) and MBA (Operation Management). Apart from this, he also holds to his credit PG Diploma in Dairy Technology, Diploma in Export Management and Certificate, Diploma, Advance Diploma in the French language. He has more than 14 years of work experience in both academics and the food industries. Currently, he is working as an Assistant Professor (Food Technology) in the Department of Food Science and Technology at I. K. Gujral Punjab Technical University, Punjab, India. He has contributed over 54 scientific papers in different national/international journals and conferences in the area of food science/technology/engineering. Currently, he also served as Vice President of the ICASR: International Council of Applied Science Research, International Centre for Research and Innovation, Eudoxia Research Centre, India.

Vasudha Sharma is working as Assistant Professor in the Department of Food Technology, Jamia Hamdard, New Delhi, India. Dr. Sharma obtained her PhD in Food Process Engineering from IIT Kharagpur and has more than 10 years of teaching and applied research experience in food processing technologies. She has published widely in the areas of functional foods, non-dairy probiotics, process optimization and nanobiosensors for food safety. Dr. Sharma has guided more than 50 M.Tech. students for their dissertation and has seven Ph.D. students’ in progress. She has over 20 research publications, 12 book chapters, one book, five popular articles and three patents (filed) to her credit. Currently, Dr. Sharma has Indian Council of Medical Research (ICMR) and National Project Implementation Unit (NPIU)-TEQIP-funded research projects in the area of non-dairy functional probiotic product development.

About the Editors

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Dr. Sharma has received the Centre for Quality and Food Safety (CQFS) Award 2021 on World Food Safety Day 2021 and has served as Member of Expert Committee for drafting academic curricula for Food Safety education in India. Dr. Sharma is also involved in several extension activities in the area of food safety. She is also currently, serving as the Vice President of AFSTI Delhi Chapter and Executive Member for WICCI Delhi Food Processing Council.

Part I

Smart Farming for Food Production

Chapter 1

Smart and Sustainable Food Production Technologies Anuj Kumar, Shantanu Kumar Dubey, R. Sendhil, A. K. Mishra, Uma Sah, Truptimayee Suna, and Ramesh Chand

Abstract The oldest vocation of human civilization has been farming. Over centuries, mankind has been dependent on earth for food but there have been several damaging effects of our ways of growing crops. The dysfunctional consequences have resulted in damage to soil, water, micro-flora, and fauna to a considerable extent. This led to the search for the alternative approaches which may transform the landscape of Indian agriculture so that sustainability may become its innate attribute. The approach of smart and sustainable food production has been conceptualized into the re-orientated use of land, water, carbon, nitrogen, and energy all of which has essentially the link with nature. Experiences have confirmed that the judicious and smart uses of these resources have resulted into not only the enhanced or even comparable level of resources’ productivity, the profitability has also moved upward and the sustainability of the production systems has been protected. This chapter highlights many of the smart technological options for production factor optimization and several end-to-end disruptive technologies and practices have been discussed. Moreover, the up-scaling and out-scaling mechanisms of those innovations are also deliberated. Keywords Smart agriculture · Sensors · Smart irrigation systems · Drones · Precision farming

A. Kumar · R. Sendhil · R. Chand ICAR-Indian Institute of Wheat and Barley, Karnal, Haryana, India S. K. Dubey (*) ICAR-Agricultural Technology Application Research Institute, Kanpur, UP, India A. K. Mishra · T. Suna ICAR-Indian Agricultural Research Institute, New Delhi, India U. Sah ICAR-Indian Institute of Pulses Research, Kanpur, UP, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_1

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Introduction

Agriculture is the pivot for ensuring global food security. Value added from the sector has reached 68% between 2000 and 2018, estimated at US$ 3.4 trillion and its share to the global gross domestic product is around 4% since 2000. It has generated employment to around 884 million, globally in 2019. In terms of global food production, the primary crops output has been estimated at 9.2 billion tonnes during 2018, which is about 50% more in comparison to 2020. Of the primary crops, sugarcane, maize, wheat, and rice account for around 50% of the world output (FAO 2020). Among the countries, notably India’s surge in food production, postIndependence, is largely attributed to the semi-dwarf rice and wheat varieties and has been the harbinger of the Green Revolution. Food production has increased by multifolds since 1950. In the past seven decades, food grains production has jumped from about 51 million tonnes (1950–1951) to 309 million tonnes (2020–2021) witnessing a quantum growth of 507.34%, largely attributed by productivity growth (357%), followed by acreage (33%) as evident from Fig. 1.1. Decade-wise analysis of food grains production in India (Table 1.1) indicates the magnitude of contributing factors (area and productivity). Except the area under food grains during the 1980s and 1990s, the rest all exhibited a positive change (compound annual growth rate) in the area as well as food grains productivity. The production growth was highest during the 1950s, largely attributed to productivity growth (2.26% per annum), and followed by area (1.94% per annum). In the subsequent decades, the growth rate witnessed a decline till 2009–2010 and then experienced an increasing trend. Barring the 1950s, the rest of the period showed less than 1% growth per annum in the area under food grains. Within the food grains, wheat is the only commodity that recorded a positive change in the area, production as well as productivity in the past seven decades, and also registered the highest

Fig. 1.1 Trends in food grains production for India. (Source: FAO 2020)

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Table 1.1 Growth in food grains production in India Period 1950–1951 to 1959–1960 1960–1961 to 1969–1970 1970–1971 to 1979–1980 1980–1981 to 1989–1990 1990–1991 to 1999–2000 2000–2001 to 2009–2010 2010–2011 to 2020–2021* Overall (1950–1951 to 2020–2021)

Compound annual growth rate (%) Area Production 1.94 4.25 0.52 1.85 0.46 2.07 0.23 2.78 0.08 2.09 0.29 1.90 0.27 2.07 0.18 2.38

Productivity 2.26 1.32 1.60 2.97 2.17 1.60 1.80 2.19

Fig. 1.2 Smart agriculture and its integrals (Bach and Mauser 2018)

output growth (Sharma et al. 2014). The gargantuan production in the food grains led to a status of surplus, by catapulting the country from “food grain importer” to a “net-exporter” one (Chand 2001). Yet, with the increasing production challenges in the recent past and aggravated by climate change adverse effects, there is a dire need for the adoption of smart technologies as the country needs to feed millions despite marching progressively by crossing the magical figure of 300 million tonnes. Smart agriculture is a concept focused on managing agricultural resources using advanced technology inclusive of data analytics, cloud computing, and the Internet of Things (IoT) for various activities such as tracking, monitoring, automating, and analyzing data (Fig. 1.2). It is a big deviation from traditional farming as it offers certainty and predictability. Tools such as robotics, automation, and cloud software systems are used to make the farming systems efficient and sustainable. Robots, drones, and sensors placed

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Fig. 1.3 Information-based farming (Saiz-Rubio and Rovira-Más 2020)

throughout the farms collect data which is processed to create valuable insights. It also helps to optimize the human resources and ease the farmers. Smart farming also decreases the ecological footprint of farming. The minimized or limited use of fertilizers and pesticides such as in precision farming reduces leaching as well as the greenhouse gasses. Various technologies have been integrated in agriculture so that the farmer and environment both are benefited. Some of them are used (Fig. 1.3) as follows and discussed in detail in this chapter. 1. 2. 3. 4. 5. 6. 7.

Field mapping Predictive analytics Data generation for analytics Tracking and monitoring Automation using drones and robotics Warehousing Saving energy, for example, using smart irrigation.

1.2

Smart Water Management

Smart water management primarily intends scientific, efficient, reasonable, and sustainable application of irrigation water at appropriate times of need. It also ascertains the better utilization of recycled and treated wastewater. The rapidly increasing demands of food grains, pulses, oil seeds, and other agro based commodities demand a rational increase in the production and productivity along with higher water productivity as the same has become a limiting resource. The burgeoning population increases environmental issues and pressure on the food and agriculture sector makes the water even a more valued asset. Furthermore, the booming populace is posing tremendous environmental threat and pressure on resources sustainability; therefore, the agriculture sector needs to make concerted efforts for most

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Fig. 1.4 Components of smart green farming

efficient use of the world’s limiting and precious commodity, i.e. good quality water for which most modern technologies are a must. Therefore, appropriate technologies and related activities for water management are a must. As the world is struggling with challenges of looming climate change such as the growing intensity and frequency of floods, drought, cloud bursts, landslides, dust storms, cyclones/hurricanes, and erratic weather phenomenon which has brought the agricultural sector on the brink of serious production risks. Therefore, the smart and wise use of limited water resources in agricultural production for producing more crop per drop to enhance water productivity has become the need of the hour not only in water scarce regions but also in the regions having fairly good water availability as of today. Hence, the development of new water smart strategies and technologies with the more prudent and scientific water use techniques are a must to boost and improve crop yields while ensuring the resources sustainability. Nowadays, several water smart agricultural production technologies belonging to all areas of farming encompass many original, novel, and high-quality contributions, scalable from molecular to whole plant studies, and from farm to global levels are being developed to explore sustainable water use in different climate change scenarios (Fig. 1.4). In this context, the whole water supply chain starting from freshwater reservoirs to wastewater collecting and recycling demand the attention and application of smart water technology which in turn ensures clarity and improved control over such resources. The related hardwares, instruments, software tools, and techniques may

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optimize production, distribution, and consumption of water not only locally but also globally. There is a plethora of hardware and software instruments, including sensors, meters, data processing, and visualization tools, actuators, web applications, and mobile apps/controls are available in the market and the list is increasing. However, the high implementation cost of smart water technologies is the great barrier to the implementation of it especially in developing countries like India. Similarly, data security and its reliability along with system ability to give accurate results are some of the key challenges in its field of implementation of smart water management technologies in agriculture and allied sectors (Gupta and Kulat 2018). Leakage detection and assessment is usually the difference between measured and predicted hydraulic parameters (Tucciarelli et al. 1999). It is worth mentioning the Kalman filter-based algorithm for the prediction of hydraulic parameters of water distribution systems developed by Ye and Fenner (2011). With the advancement of technology in the twenty-first century, we have technologies, instruments, and equipment that are vital to address the challenges of water scarcity and make smart use of water to ensure socio-economic well-being without challenging the long-term resources sustainability. The high use of fertilizers and other chemicals on the farms and rapid growth of mining and construction sectors have contributed adversely to the overall water quality globally (Prasad et al. 2015). It is now a well-known reality to abridge the barriers between the physical and digital world to support the management of complex water cycle in irrigated agriculture by integrated applications of all the available techniques. Some of the modern tools and techniques used for prudent management of water are being described as follows.

1.2.1

Smart Water Meters and Monitoring Systems

Real-time water consumption measuring helps in identifying excessive usage and waste points and also ensures the correct usage patterns and making predictions for future consumption. Analog meters are still used in most of the cities of our country and across the world for measuring domestic and corporate water usage. These early age meters are being used for long periods but they are unable to report any anomaly in water use to the water authorities as they lack connectivity and reproducibility. Smart water meters support the correction of water consumption for budgeting and sustainability goal. Therefore, connected smart meters leveraging IoT technology enable owners to view their water usage and also send immediate alerts to users in case of excessive use of water. Smart meters notify the water authorities regarding the wasting of water by the owner and helping the water authorities in intervening a violator. Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are being used in tandem in research and actual field use for monitoring the water use by collecting and analyzing data from remote locations without the manager actually being present on site.

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1.2.2

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Smart Water Sensors

Managing the water smartly involves a broad application of sensors because of their diversity and purposes. In every basic water supply chain, sensors measure the quality of raw catchment water and its chemical composition after treatment and wastewater recycling. It also measures the changing quantity in the storage reservoir as well as the pressure in the distribution pipeline and wear and tear of the machinery that process and distribute water to end-users. The immediate detection of the leak which is unseen by the human eye in almost every pipeline can be ensured by using a smart sensor integrated with a maze of pipes. Actually, the maze of pipes enables the smooth flow of freshwater into the farms/building and takes out the waste materials. The sensor sends an alert to the farm manager in case of unwanted water flow which in turn guides the farm manager in detecting the exact location of the leak and carrying out the necessary repairs. Such sensors will go a long way in detecting the changes and the same could be plugged in. Managers may receive the appropriate insights into the changing conditions of water at different points by using the data collected by IoT water sensors. Simitha and Subodh (2019) also used IoT/WSN-based water quality monitoring systems for smart cities which needs to be extended to the farm sector as well.

1.2.3

Smart Irrigation Systems

A major share of over 80% of total water usage in India goes to irrigation of crops and raising ornamental plants. Smart irrigation systems using IoT modules may facilitate water conservation to a new level by reduction in the excessive water use in farming and allied activities. Smart sensors in combination with smart irrigation application systems like sprinklers and drips shall enable water supply by ascertaining soil moisture as per the need of plants. Hence, instead of the traditional method, using smart devices is an appropriate intervention for water conservation. In the scientific literature this is referred to as smart irrigation scheduling and efficient irrigation water management.

1.2.3.1

Use of Information and Communication Technologies (ICT)

A large number of ICT have found place in the irrigation sector of late. The Wireless Sensor Network (WSN) innovation is promising ICT intervention for checking the data concerning an outside region of the farming environment which is inevitable too (Pandey et al. 2019). This kind of sensor framework gathers ecological and edaphic data. Actually, it uses a moisture and temperature sensor which helps the field to control the water level as well as soil temperature. The use of WSN as GSM (Global System for Mobile communication) has been advocated for use (Suresh et al. 2014).

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The shadow of climate change looming over the world is a reality now where safe and good quality of water is the epicenter of sustainability and is becoming ever scarcer. Albeit, water shall remain a vital resource for techno-economic progress, healthy ecosystems, and human survival. Modern Information and Communication Technologies (ICT), today had more pronounced applications in smart water management such as water resources mapping and establishing the models for precise availability and meteorological predictions.

1.2.3.2

The Automated Hydrological Information Systems (AHIS)

For the assessment of the total available water resources for planning for future distribution to various sectors, the hydrologic measurement is a must. Indeed, it is the need of the hour and ultimate requirement for water resources assessment for the water resource managers to have the correct information about the hydrology of the watershed or the river basin. Modern tools and advanced technologies have been developed globally for collection, conveyance, processing, and presenting the data capturing the hydrological status of a river basin in real-time. In India, however, there is a need for technology adaptation in almost all river basins in real time. According to the World Bank estimate, there is one-fourth filtration and leakage loss globally, impacting water availability and the farm economy. Looking into the future of ever expanding global urban cities and their food requirements, water management is the key word. Water requirement is, however, grossly challenged by the demographic pressures, augmented water demands, and the yawning water deficit. In this context, optimizing and the integrated water management processes has become unavoidable. It is in this reference that IoT enables careful monitoring of water resources and helps in optimizing and efficient use of water resources and their management. Gubbi et al. (2013) evolved an IoT framework along with a decision support interface supporting the cloud-centric storage, processing, and analysis of data received from ubiquitous sensors. Cruz et al. (2018) further consolidated the case by advocating the reference model for IoT middleware platform capable to supplement intelligent IoT applications. The agriculture landscape is also harnessing the dividends of IoT-based solutions (Sharma et al. 2016). It is believed that these intelligent solutions shall be instrumental in smart irrigation for optimum water utilization. The major elements for designing the smart irrigation system include soil moisture, precipitation, and evaporation. The major dimensions of IoT-based water management systems include: smart water metering and management (SWM), use of modern technologies for smart water deliveries to the intended place/crop, economic objectives, etc. These objectives of smart water management are used to reap the rich benefits of using IoT for scientific and efficient water management, and developing IoT enabled on-farm water management technologies. The water sector is under tremendous pressure as the natural resources are very limited and being used close to their maximum potential. It is essential to identify not only current needs but also future needs for ensuring the sustainability of water

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resources. Artificial intelligence helps to automate risk management and simulate the behavior of water networks and detect anomalies and improves efficiency. Meeting the satisfaction of the end user is the main challenge for water service providers. It is the fast and simple communication which may help to mitigate this challenge. Mobile based applications facilitate companies in offering the new services to customers, disseminating real-time information and scope for contacting staff and solving the potential queries on a 24  7 basis. For example, Smart-Aqua, by Aqualia, is an Android and IOS based mobile app that allows customers to manage the services provided by the company at any time and conveniently.

1.2.3.3

Application of Blockchain for the Strong Link Between Supply and Demand Centers

Water sector, by virtue of its larger magnitude demands transparent, fast, flexible, and secure information, and knowledge exchange, among all the actors associated in the integrated water management process. The digital purchase-sale transaction between the actors for water services is enabled by blockchain intervention which at the same time strengthens the transparency and secure non-personal data. The Catalonian Technological Centre (Eurecat) offering the blockchain platform and enabling the water services resulting into strengthened relationships between consumers and water management entities is a good example of this system. The water management sector has witnessed the unprecedented technological advances impacting the functions of the related entities, transforming the business climate, and resulting in changed opinions of the end-users. Blockchain is an open and distributed ledger that records the transactions between two parties in an efficient, verifiable, and permanent way (Iansiti and Lakhani 2017). Blockchain is the disruptive ICT intervention that may revolutionize how data could be used for agriculture. This technology also offers a dependable platform for tracing the anonymous transactions which may detect the fraud or malfunction, if any in this process and reporting the problems on a real-time basis (Haveson et al. 2017; Sylvester 2019; Mohapatra et al. 2021).

1.2.3.4

Automated Distribution Systems and Precision Application Algorithms

Environmental sensors and predefined algorithms have come forward in a big way for dynamically regulating and managing the water supply. As a result, a large number of related companies are shifting to an automatic water management regime. The smart irrigation, for example, sprinkler system may capture the soil moisture, air humidity, and crop condition and using these reads it may provide enough water to the crop.

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Nutrient Smart Technologies

The use of nutrients for crop production is very much essential for sustainable production as well as for maintaining soil health. There are 16 nutrients essential for plants during their life cycle. Nitrogen, phosphorus, and potassium are the primary (macro) nutrients for crops and plants require these in larger quantities mostly in the form of chemical fertilizers such as urea, DAP, MOP, NPK mixture, etc. On the other hand, the secondary nutrients such as calcium, magnesium, and sulfur are required in lesser quantities by the plants. The micronutrients such as boron, chlorine, copper, iron, manganese, molybdenum, and zinc are required in very small amounts by the plants, but they are crucial to plant development and profitable crop production alike major nutrients. These micronutrients work “behind the scene” as the activators of many plant functions. Application of nitrogen, phosphorus, and potash is widely adopted by farmers across the country but the application of secondary and micronutrients is rarely adopted by the farmers. Even many farmers are not applying potassic fertilizers in their fields. It has been also observed that farmers in Punjab, Haryana, and western Uttar Pradesh are using an overdose of nitrogen in wheat, paddy, sugarcane, and other crops. The technology of Integrated Nutrient Management (INM) has not been adopted by most farmers. Time has come to upscale and outscale INM to each and every farm for balanced fertilization.

1.3.1

Application of Fertilizers Based on Soil Health

Application of fertilizers on a soil health card basis is becoming a reality in India with the launch of the Soil Health Card (SHC) scheme in 2015. This mega scheme was launched to provide SHC to all farm holdings in the country at an interval of 2 years so that farmers can apply recommended dosages of nutrients for crop production and improving soil health and fertility. Till today 140 million SHCs have been prepared and distributed among the farmers. It contains the status of soil with respect to 12 parameters, namely NPK (Macronutrients); S (Secondary nutrient); Zn, Fe, Cu, Mn, Bo (Micronutrients); and pH, EC, OC (Physical parameters). Under this scheme, the Government has made a provision for the assistance of Rs. 2500/ha for the distribution of micronutrients and soil ameliorates. For the supply of gypsum/pyrite/lime/dolomite, 50% cost of the material + transportation limited to Rs. 750/ha. For the adoption of Integrated Nutrient Management, a provision of Rs. 1200/ha (restricted to 4 ha area) has been made (MoA&FW 2021).

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Smart Fertilizer Management

The new era crop nutrition technology-smart fertilizers enables not only the dosage reduction, but it also improves the crop yield in an environment-friendly manner. So far, smart fertilizers are available for the phosphates in the form of smart phosphates and for micronutrients - smart micronutrients, i.e. the fertilizers for zinc, boron, manganese, etc. Smart phosphate is basically the replacement for water-soluble phosphates available in popular chemical fertilizers such as DAP, SSP, etc.; and similarly, smart micronutrients can replace water-soluble micronutrients (zinc sulfate, borax, etc.). The use of smart fertilizers facilitates the nutrient release as per the requirements of the plant itself. This becomes beneficial for yields to increase as plant require different nutrients at different crop stages. Moreover, minimizing the nutrient losses reduces the phosphate dosage by 10%, i.e. from ¼th to ½ of current dosages and contributes for yield enhancement by 10%. In case of smart micronutrients application, such reduction may be ensured to the extent of 90% with corresponding yields increase by 15–20 percent. In this way, the investment made by farmers per acre is reduced but the yield is more than the current fertilizers application level. AAPFCO (1995) and Trenkel (1997) has long back academically defined that the slow- or controlled-release fertilizers are those which either: 1. delays its availability for plant uptake and use after application or 2. is available to the plant for a significantly longer period than reference fertilizers. Practically, there is no operational differentiation between slow-release and controlled-release fertilizers. However, the microbial decomposed N products, such as urea-formaldehyde, are termed as slow-release fertilizers and coated or encapsulated products as controlled-release fertilizers (Trenkel 1997). Delayed availability of nutrients or consistent nutrient supply for extended periods can be achieved through various modifications like use of semi-permeable coatings for controlled solubility of the fertilizer in water, protein materials, occlusion, chemicals, slow hydrolysis of water-soluble compounds of lower molecular weights, and some other unknown means (Naz and Sulaiman 2016).

1.4

Smart Monitoring System for Soil and Crop Health

The soil type and nutrition status of soil are the important factors for deciding the crop to be grown and their quality, increased deforestation has degraded the soil quality enormously. Also, it is often difficult to determine the soil quality objectively. In this direction, a German-based tech start-up PEAT has developed an AI-based application called Plantix. This system identifies the soil nutrient deficiencies and also diagnoses the plant pests and diseases which help farmers to get an idea for using fertilizer leading to improved harvest quality. This app uses image recognition-based technology in which farmers capture the images of plants using

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smart phones and this image is used by the app for further analysis. Trace Genomics is another machine learning-based company supporting farmers to do soil analysis themselves. Such app based interventions help farmers for soil and crop health monitoring and thus, producing healthy crops yielding higher levels of productivity. IoT-based crop health monitoring includes monitoring of different parameters like temperature, humidity, precipitation, pest intrusion, seed and soil quality, thus enabling better decision-making in terms of crop quality and health ([email protected]). GSM technology gives a smarter and an efficient way for a better yield of crops (Bogena et al. 2010; Ramson and Moni 2017). The IoT-based monitoring systems ensure low cost, high fidelity, flexibility, and rapid deployment. There are many other wireless networking protocols such as LoRaWAN, SIGFOX, and NB-IoT which achieve a long communication range (in the order of kilometers) while consuming low power and without the need for intermediate nodes and they are attractive and user-friendly options for agricultural operations (Davcev et al. 2018; Gia et al. 2019).

1.4.1

Analyzing Crop Health by Drones

Drones are the emerging technological options which facilitate aerial photography at considerably lower cost than using a helicopter or small plane (West and Bowman 2016). Drones have confirmed their worth for recording the canopy reflectance (Primicerio 2012). For quantifying the crop growth over a season, aerial images can be taken periodically i.e. at the season’s start, at predetermined intervals during the season and just prior to harvesting to illustrate the growth across the field, thus highlighting any rows that show signs of stunted growth may be because of poor irrigation or lower initial nutrient uptake (Honkavaara 2013). Sky Squirrel is one such technology that has smoothen the drone-based Aerial imaging solutions for crop health monitoring. In this technique, the data captured from fields are transferred to a computer and analyzed by experts. A detailed and systematic algorithm is used to analyze the captured images and a comprehensive report containing the current health of the farm is generated. Moreover, it also helps farmers to identify pests and diseases thus supplementing the timely use of pest control and other methods to take the required action.

1.5

Energy Smart Technologies

Energy is one of the components which add to the cost of production of any crop. The use of energy-efficient technologies should be adopted in farming to reduce the cost of cultivation. Energy is required to perform different farm operations such as tillage operation, leveling of fields, seeding, irrigation, intercultural operations, harvesting, threshing, and transportation. There is a need to upscale and outscale

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energy-efficient technologies at farmers’ fields. Under the rice-wheat cropping system, farmers are adopting conventional methods, i.e. harrowing, planking for field preparation which requires lots of energy. Conventional tillage practices for wheat are very intensive in India’s rice-wheat systems. For instance, tillage alone encompasses around 25% of the total cost of conventional wheat production (Karnal, Haryana). Due to the adoption of zero tillage technology, the number of field operations for the establishment of the wheat crop (including tillage) decreased from an average of seven to only one (Sharma et al. 2002). In Haryana, zero tillage saved 59 L/ha of fuel, 8 h/ha of tractor time, and approximately 3000 MJ/ha of energy in tractor operations as compared to conventional tillage (Sharma et al. 2002). Such potential savings are not limited to the IGP but have also been reported in Central India (Madhya Pradesh), where zero tillage had saved 75 L/ha of fuel by reducing tillage operations from seven to one (Yaduraju and Mishra 2002). The awareness for recent energy smart technologies such as Super Seeder, Turbo Happy Seeder, and Zero till seed cum ferti drill for seeding of wheat without tilling the soil are helping in saving huge quantities of energy. Even seeding of wheat crop with rotavator is also an energy-smart technology wherein one go seedbed preparation, as well as drilling of seed, is done. Most of the small farmers are adopting rotavators in their fields to save energy as well as cost. Harvesting of wheat crop with SMS mounted combine harvesters is also an energy-smart technology adopted by the farmers of northern states. Use of reaper binder for quick harvesting of crops followed by threshing with power threshers will be another option to save energy. For in-situ and ex-situ management of paddy and wheat straw up-scaling and out-scaling of technologies such as straw reaper, straw chopper, hay rack, mulcher, and a straw bailer is of utmost importance. Replacement of diesel-operated pump sets with electric operated tube wells and solar pumps need to be promoted on a larger scale to save energy and time. All energy-efficient technologies need to be promoted through custom hiring centers in rural areas under different Government schemes.

1.6

Carbon Smart Technologies

The emission of carbon in agriculture varies from crop to crop, technology to technology, and field to field. But in the wheat crop, the emission of carbon can be reduced with the adoption of resource conservation technologies. In an on-farm study, the zero tillage-based wheat production helped to reduce CO2 emissions by 1.5 Mg/ha (Aryal et al. 2014). Up-scaling and out-scaling of carbon smart technologies such as zero tillage for sowing wheat can be more eco-friendly technology. Use of all such technologies to stop stubble burning such as straw reaper, straw chopper, straw bailer, reversible MB plough, etc. in northern states should be adopted on a larger scale. There is a need to devise extension strategies to promote carbon smart technologies in the farmer’s field. The govt. of India has made provisions through budgetary allocation for the popularization of all these machines. A

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subsidy of 80% for the farmer’s groups and custom hiring centers and 50% to individual farmers is given in a majority of wheat-growing states (MoA&FW 2021). The government is promoting custom hiring hubs in Punjab and Haryana to popularize these machines on a larger scale. Regular awareness campaigns are being organized by different extension agencies for the farmers. Use of mass media such as television, radio, newspaper, social media, farmers’ fairs, and exhibitions for creating awareness among the farmers on a regular basis. In recent years a lot has been talked about the impact of climate change on wheat in particular and agriculture in general. The rise in mean temperature owing to climate change and water logging due to heavy rainfall are major factors affecting the overall production. The use of weather data for forecasting has become a regular feature of today’s agriculture. Day-to-day variations in weather parameters are recorded by different agencies to make predictions through prediction models. There is a need to be a member of such portals for the weather updates in order to avoid any negligence in the application of irrigation, herbicide, and pesticide application. Many farmers have become members of WhatsApp groups of Agricultural institutes, state agricultural universities, state department of agriculture, KrishiVigyan Kendra, m-Kisanportal, etc. for getting regular weather updates. All the farmers/farmers groups must be linked with IMD and other departments responsible for day-to-day weather forecasting. Farmers need to be educated on how to safeguard themselves against natural risks like natural disasters/calamities, insect, pest and diseases, and adverse weather conditions (MoA&FW 2021).

1.7

Knowledge Smart Technologies

Modern agriculture is a synonym of knowledge and skill. In recent years there has been a tremendous change in agriculture and it is shifting from traditional to modern and from modern to high-tech. In hi-tech agriculture, proper knowledge and skill are the prerequisite to make it a profitable venture as a heavy investment is made on infrastructure. Now protected cultivation is done in a poly house or greenhouse or low tunnels require a lot of skills such as selection of crops and their varieties, cultivation skills, intercultural skills, harvesting skills, grading and packaging, and marketing skills. Knowledge of the e-NAM portal for selling farm produce is the need of the hour and by registration farmers are getting a better price. To promote online marketing of agricultural commodities across the country and to provide maximum benefit to the farmer, on July 1, 2015, the government launched the e-National Agriculture Market (e-NAM) through which a web-based platform has been deployed across 250 regulated markets to promote online trading. There are other online platforms too for marketing of produces and price negotiation. Farmers can get the price information of their produce which is available on the AGMARKNET website (www.agmarknet.nic.in) or through Kisan Call Centers or SMS. The buyer-seller portal is available at www.farmer.gov.in/buysell.htm.

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Farmers in a group may form marketing cooperatives and FPOs for better marketing reach and these marketing cooperatives can open retail and wholesale outlets. Farmers may also operate cold storage and warehouse to store the produce in order to avoid distress sales. It has become very important to remain updated in agribusiness for a better price realization and for that, the following steps must be taken. There are other online platforms too for marketing produce and price negotiation. AGMARKNET website (www.agmarknet.nic.in) or Kisan call centers have been brought into action through which Farmers can fetch information about their produce. The buyer-seller portal can be accessible at www.farmer.gov.in/buysell. html. Farmers in a group may form marketing cooperatives and FPOs for the end-toend support and cover services like better marketing reach and opening of retail and wholesale outlets. Cold storage and warehouse can be a better option to store farm produce and operated by farmers to avoid distress sale. It has become very important to remain updated in agribusiness for better price realization.

1.8

Agricultural Robotics

AI companies are manufacturing multitasking robots for farming purposes. This type of robot is trained to control weeds and harvest crops at a faster pace with higher volumes compared to humans. Checking the quality of crops, detection of weeds, and picking of crops such activities can be accomplished by these robots (Fig. 1.5). These robots are also capable of fighting challenges faced by agricultural force labor. Some of the well-known names that are actively involved in the research and development for various types of weed control robots are the Wageningen University and Research Center (The Netherlands), Queensland University of Technology,

Fig. 1.5 Robots used for planting

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the University of Sydney, Blue River Technologies (Sunnyvale, CA, USA), Switzerland’s ecoRobotix (Yverdon-les-Bains, Switzerland), and France’s Naio Technologies (Escalquens, France). For example, a flexible multipurpose farming and weeding robot platform named BoniRob. (a) BoniRob (Ruckelshausen et al. 2009; Sander 2015) an integrated multipurpose farming robotic platform for row crops weed control developed by interdisciplinary teams which is also capable of creating detailed map of the field, (b) AgBot II (Bawden et al. 2014) an innovate field robot prototype developed by the Queensland University of Technology for autonomous fertilizer application, weed detection, and classification, and mechanical or chemical weed control, (c) Autonome Roboter (Ruckelshausen et al. 2006) a research effort robot developed by Osnabrück University of Applied Sciences for weed control, (d) Tertill (MacKean et al. 2017) a fully autonomous solar-powered compact robot developed by Franklin Robotics for weed cutting, (e) Hortibot (Jørgensen et al. 2007) a robot developed by the Faculty of Agricultural Sciences at the University of Aarhus for transporting and attaching a variety of weed detection and control tools such as cameras, herbicide, and spraying booms.

1.9

Precision Farming and Predictive Analytics

AI applications in agriculture have developed applications and tools which help farmers in inaccurate and controlled farming by providing proper guidance to farmers about water management, crop rotation, timely harvesting, type of crop to be grown, optimum planting, pest attacks, and nutrition management. In particular, it is worth pointing out that predictive analytics datasets always have data, which is linked to crop rotations, crop patterns, weather patterns, the conditions of the environment, the types of soil, soil nutrients, Geographic Information System (GIS) data, farmer details, Global Positioning System (GPS) data, agriculture machinery data, like yield monitoring as well as Variable Rate Fertilizers (VRF) (Grisso et al. 2009). While using the machine learning algorithms in connection with images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyze crop sustainability, and evaluate farms for the presence of diseases or pests and poor plant nutrition on farms with data like temperature, precipitation, wind speed, and solar radiation.

1.10

Smart Green Housing

In the conventional strategy for cultivating, human work was necessary to see the greenhouse at a particular point and to observe all the required levels physically. The regular technique is observed to be slow and requires a large amount of effort and energy. Along these lines, this analysis is around building up a framework that can consequently screen and anticipates various changes in light, temperature soil

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moisture, and humidity levels of the greenhouse. The goal of the survey is to build up a programmed monitoring device observing framework utilizing sensors and send email warnings and messages to the mobiles (Sultan et al. 2021). The recommended framework has an estimation which is equipped for identifying the levels of light, temperature, soil moisture, and humidity. The framework additionally had an instrument to caution agriculturists with respect to the limitation change in the conservatory then safeguard measures can be taken in advance. In this examination, a few experiments were directed to a particular final aim to demonstrate the suitability of the framework. Test outcomes showed that the dependability of the framework in spreading data straightforwardly to the agriculturists could be picked up astoundingly in different conditions.

1.11

Vertical Farming

In the physical layout, the plants are vertically stacked in a tower-like structure. This way, the area required to grow plants is minimized. Next, a combination of natural lights and artificial lights is used to maintain a perfect environment for the efficient growth of the plants. The third parameter is the growing medium for the plants. Instead of soil, aeroponic, hydroponic, or aquaponic growing mediums are used as the growing medium. Using advanced greenhouse technology such as hydroponics and aeroponics, the vertical farm could theoretically produce fish, poultry, fruit, and vegetables (Despommier 2010). This way, more than 3 dozen types of vegetables can be chosen to grow inside the building hydroponically (Ankri 2010). The most common products now produced in vertical farms are lettuce, tomato, Chinese cabbage, eggplant, green onion/chives (Fig. 1.6). Vertical farming has several advantages, which makes it promising for the future of agriculture. The land requirement is quite low, water consumption is 80% less, the water is recycled and saved, it is pesticide-free, and in cases of high-tech farms, there is no real dependency on the weather. A vertical farm makes farming within the confines of a city, a reality. In case the farms are nearby, the produce is quickly delivered and always fresh; when compared to the refrigerated produce usually available at supermarkets. Reduction in transportation reduces the fossil fuel cost and resulting emissions and thus also reduces the spoilage in transportation. However, like everything else vertical farming has its drawbacks. Initial capital costs for establishing the vertical farming system are the major problem. In addition, there are costs of erecting the structures along with its automation like computerized and monitoring systems, remote control systems and software’s, automated racking and stacking systems, programmable LED lighting systems, climate control system, etc.

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Fig. 1.6 Vertical farming

1.12

Cloud Software Systems in Agriculture

Cloud computing is an information technology paradigm through which users can access shared pools of configurable system resources over the internet. Such a sharing of resources enables coherence and economies of scale, which functions like a public utility, which can be quickly allotted by service providers to users with very little managerial effort. In sum, cloud computing can help with real-time computation, data access, and storage to users without having to know or worry about the physical location and configuration of the system that delivers the services.

1.12.1 Practical Information Sharing Web-based agriculture management information systems can be useful in the agriculture sector, as it brings the latest bulletins on weather, prices, fertilizer, sowing of crops, etc., to farmers in rural areas. AgJunction (Precision Ag 2012) has developed an open and cloud-based system that captures and shares data from many types of precision agriculture controllers on a farm to lower costs and reduce environmental impact. Additionally, Fujitsu has launched the “Akisai” (Fujitsu Limited 2012) cloud for food and agricultural industries and is utilizing information communications technology to ensure plentiful food supplies in the future (Sourcetrace 2022).

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Plant Factory

Plant factory refers to a plant production facility consisting of six principal components: a thermally insulated and nearly airtight warehouse-like opaque structure, 4–20 tiers equipped with hydroponic culture beds, and lighting devices such as fluorescent and LED (light-emitting diodes) lamps, air conditioners with air fans, a CO2 supply unit, a nutrient solution supply unit with water pumps, and an environmental control unit (Kozai 2007). Workers generally enter the cultivation room of the plant factory only after taking hot water or air showers and wearing clean clothes. Using plant factories, high-quality pesticide-free plants are produced all year round owing to the optimal control of the aerial and root-zone environment. Leaf-grown vegetables after harvest are doubled compared to those produced in a greenhouse because the bacterial load is generally lower than 300 CFU/g, which is 1/100th to 1/1000th that of field-grown vegetables after washing with tap water. Plant factories with artificial light are becoming increasingly important nowadays for commercial production of leaf vegetables and other short-height leaf plants to enhance local production for local consumption in urban areas (Kozai 2013). Residents/users living in urban areas and having little chance to grow plants in the open field may enjoy using a household plant factory. It is suggested that such a plant factory and its network have the potential to contribute to a better life for people in urban areas, and to provide an educational perspective to them about science, technology, virtual community, plant growing, the origin of food, global ecosystems, and global productivity.

1.14

Artificial Intelligence Based Smart Water Management Solutions

The AI can help the farmers to increase the capacity of production and reduce the cost of production and drudgery. No need to say that the diffusion of AI in all application domains will also bring an ideal shift in the way we do research and development in agriculture now. AI moves towards more automation with more accuracy to perform on real-time management, which is helping in standard shifting of traditional agriculture to precision agriculture with low cost. The AI solution must be viable and accessible to the farming community. With the advent of technology and interference of AI, there has been a dramatic transformation in the capacity of production and reduction in the cost of production and drudgery. The diffusion of AI in all application domains facilitated the, paradigm shift in the way we do research and development in agriculture now (Saxena et al. 2020). IT boosts automation with more accuracy to perform on real-time management, which is helping in standard shifting of traditional agriculture to precision agriculture with low cost. The solution of AI must be economically viable and easily accessible to the farming community. AI solutions should offer an open-source platform for faster adaptation and greater

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insight among the farmers by making its solutions more affordable. It will be a powerful tool that can help organizations cope with the increasing amount of complexity in modern agriculture. It is high time that big companies invest in this space (Wipro 2019). AI cannot replace the knowledge of farmers but in near future definitely, it will edify their knowledge. AI will complement and also challenge the way decisions are made and facilitate to improve farming practices. Such technological interventions are likely to lead to better agricultural practices, yields, and qualitatively improve the lives of farmers.

1.15

Conclusion

After reviewing the available tools and techniques it can be safely said that despite many odds the irrigation sector has evolved several good technologies which can revolutionize water management on future farms. It is needless to say that not only India but the whole world is experiencing a rapid population growth and constantly enhancing requirements for water and all such commodities which use huge amounts of water for their production. The need for becoming water smart, energy smart, and climate smart is the order of the day. Without such interventions there is absolutely no future for mankind, especially due to uneven distribution of the water globally, regionally as well as locally. Technological solutions are being evolved ever since man learnt to use wheels or advancement of science and technology set in. Many modern and futuristic solutions have been suggested in the chapter which are reality, viable, and feasible. At the same time the innovators and developers are required to develop and propagate such technologies which are affordable, effective, and path breaking. Overall objective of all efforts combined should be sustainable resource management for averting any future catastrophists and well-being of the human race.

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Precision Ag (2012) Hemisphere to acquire Ag junction. https://www.precisionag.com/industrynews/hemisphere-to-acquire-ag-junction/. Accessed 7 Nov 2021 Primicerio J (2012) A flexible unmanned aerial vehicle for precision agriculture. Precis Agric 13(4): 517–523 Ramson SJ, Moni DJ (2017) Wireless sensor networks based smart bin. Comput Electr Eng 64: 337–353 Ruckelshausen A, Klose R, Linz A, Marquering J, Thiel M, Tölke S (2006) Autonome Roboter zur Unkrautbekämpfung. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz XX:173–180 Ruckelshausen A, Biber P, Dorna M, Gremmes H, Klose R, Linz A (2009) BoniRob–an autonomous field robot platform for individual plant phenotyping. Precis Agric 9(841):1 Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(2):207. https://doi.org/10.3390/agronomy10020207 Sander S (2015) BoniRob: an autonomous mobile platform for agricultural applications, http:// www.fieldrobot.com/ieeeras/Downloads/20150923-Sander-Presentation.pdf Saxena A, Suna T, Saha D (2020) Application of Artificial Intelligence in Indian Agriculture. Souvenir: 19 national convention – artificial intelligence in agriculture: Indian perspective. RCA Alumni Association, Udaipur. xvi + 128 p Sharma RK, Chhokar RS, Chauhan DS, Gathala MK, Rani V, Kumar A (2002) Paradigm tillage shift in rice wheat system for greater profitability. In: Malik RK, Balyan RS, Yadav A, Pahwa SK (eds) Herbicide resistance management and zero tillage in rice-wheat cropping system. CCSHAU, Hisar, pp 131–135 Sharma I, Sendhil R, Gupta OP, Singh R (2014) Status of wheat in India. In: Wheat: recent trends on production strategies of wheat in India. Jawaharlal Nehru Krishi VishwaVidyalaya (JNKVV), ICAR-Indian Institute of Wheat and Barley Research, Jabalpur, Karnal, pp 1–13 Sharma D, Shukla AK, Bhondekar AP, Ghanshyam C, Ojha A (2016) A technical assessment of IOT for Indian agriculture sector. In: IJCA Proc. Natl. Symp. Mod. Inf. Commun. Technol Digit. India, pp 1–5 Simitha KM, Subodh RMS (2019) IoT and WSN based water quality monitoring system. In: Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, Coimbatore, India, pp 205–210 Sourcetrace (2022) The new age: cloud computing in agriculture. https://www.sourcetrace.com/ blog/cloud-computing-agriculture/. Accessed 7 Jan 2022 Sultan M, Mahns B, Samiei S (2021) Greenhouse automation using wireless sensors and IoT instruments integrated with artificial intelligence. InTechOpen, London. https://doi.org/10. 5772/intechopen.97714 Suresh R, Gopinath S, Govindaraju K, Devika T, Suthanthira VN (2014) GSM based automated irrigation control using Raingun irrigation system. Int J Adv Res Comp Commun Eng 3(2) Sylvester G (2019) E-agriculture in action: blockchain for agriculture (opportunities and challenges). International Telecommunication Union (ITU), Bangkok Trenkel ME (1997) Controlled release and stabilized fertilisers in agriculture. IFA, Paris Tucciarelli T, Criminisi A, Termini D (1999) Leak analysis in pipeline systems by means of optimal valve regulation. J Hydraul Eng 125:277–285 West JP, Bowman JS (2016) The domestic use of drones: an ethical analysis of surveillance issues. Public Adm Rev 76:649 Wipro (2019) Towards future farming: how artificial intelligence is transforming the agriculture industry. https://www.wipro.com/holmes/towards-future-farming-how-artificial-intelligence-istransforming-the-agriculture-industry/. Accessed 7 Jan 2022 Yaduraju NT, Mishra JS (2002) Zero-tillage in rice-wheat cropping system on vertisols in Madhya Pradesh - prospects and problems. In: Malik RK, Balyan RS, Yadav A, Pahwa SK (eds) Herbicide resistance management and zero tillage in rice-wheat cropping system. CCSHAU, Hisar Ye G, Fenner RA (2011) Kalman filtering of hydraulic measurements for burst detection in water distribution systems. J Pip Syst Eng Pract 2(1):14–22

Chapter 2

Smart Technologies in Livestock Farming Amandeep Singh, Y. S. Jadoun, Parkash Singh Brar, and Gurpreet Kour

Abstract Smart technologies and its application have shown great promise for the modernization of extension services in both developed and developing countries. Improving rural livelihoods through smart technologies is one of the key areas, which has potential to change the livestock economy. Enormous increase in the mobile and internet users has ushered in a revolution in ICT research and development. We wouldn’t be wrong if we dubbed this period the “ICT Era”. The government of India’s Digital India Mission, as well as telecom providers’ provision of affordable pricing to subscribers, have cleared the road for internet technology to reach everyone’s doorstep. Many public and private organizations involved in research related to the livestock sector have developed many such ICTs for the use of livestock farmers. Improved package of practices are being provided to the farmers by the use of mobile apps, expert systems, and web portals whereas the regular advisories are provided to them through tele-services, SMS and Remote Sensing based tools. The animals are being identified by the use of RFID tags which are helping livestock farmers as well as the resource-based companies for resource disposal. Furthermore, the farmers are connected to peers through social media and mobile telephony like Kisan Call Centre. The new buzzword, i.e. artificial intelligence (AI) through its diverse applications has the potential to revolutionize the livestock industry, like; artificial neural networks, deep learning, machine learning, natural language processing, cloud computing, block chain technology, internet of things, precision farming, sensor based systems, robotics, and so forth. It is also predicted that AI will lead in the world’s “fourth industrial revolution”. which will be a digital revolution. All of these technologies work together to create an “Information Web” for farmers, which is in charge of disseminating timely livestock development information. This chapter details the ICTs which are in use by the livestock farmers and the ones which are yet to come. Keywords Artificial intelligence · Farmers · ICTs · Information · Internet · Livestock

A. Singh (*) · Y. S. Jadoun · P. S. Brar · G. Kour Guru Angad Dev Veterinary & Animal Sciences University, Ludhiana, Punjab, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_2

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A. Singh et al.

Introduction

Livestock resources are very important for a developing country like India. In order to make India a worldwide leader in animal husbandry, it is necessary to amalgamate it with developments in other fields. Information Technology (IT) based smart technologies have been used for the study and development of livestock production systems, teaching, research and field extension activities. In developed countries ICT is being effectively used for the sustainable livestock management, livestock disease control, precision livestock farming, and diagnosis and treatment through IT applications and technologies. IT has a great role in dissemination of livestock information, technologies, and indigenous technical knowledge to the end users. In the present scenario the mobile phones looks like tomorrow’s most liable right to use device for information dissemination to the end users. Information related to livestock, e.g. alerts related to vaccination can be disseminated before monsoon through mobile phones. Despite all constraints under Indian conditions, the smart technologies are spreading at its own pace and in future the process will speed up. It seems that in the near future there will be a sound platform for livestock-based technology dissemination in rural areas in particular and in urban areas in general through the smart technologies-based applications and devices and value-added services. The utilization of ICT application has the potential to develop livestock and agricultural farmers in India (Sasidhar and Sharma 2006). The need-based, location-specific, and local language content presented in the form of computer software is the need of the hour for the livestock sector along with other e-material with regard to disease control, herd management, production and marketing of livestock, and livestock produce (Tiwari et al. 2010). The development of crop/livestock production systems, as well as increased market demand for animal-based products, are driving up the need for ICTs in emerging countries (Morton and Matthewman 1996). With the ability to smoothen the information communication process among farming communities, the mobile phone in comparison to other ICT tools has turned out to be one of the widely accepted instruments covering almost the entire world (Hayrol et al. 2009). In India, next to the radio and television, mobile phone users are fast expanding, particularly in villages, creating a platform for information transmission through services such as short message service (SMS). The mobile phone shows a very promising role for information dissemination in coming years. Artificial intelligence (AI) has increased the usability of electronic media and sparked a technological revolution in practically every field where it is being used. Since 2017, massive growth has been witnessed in the application of AI. The application of artificial intelligence-mediated ICTs in the livestock industry is no exception. Massive increases in mobile and internet users have sparked a revolution in artificial intelligence-based ICT research and development. The government of India’s Digital India Mission, as well as telecom providers’ offering of affordable pricing to users, has prepared the path for internet technology to reach everyone’s doorstep, boosting the internet of things (IoTs). The world population is growing and similarly the farmers are using smarter tools to optimize utilization of land, water,

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and other resources in agriculture to meet the global food demand. The adoption of AI technology has witnessed an upward trend among various industries and agriculture is no exception to it with drones, robots, and intelligent monitoring systems. In India’s Agriculture and Allied Sectors, AI currently accounts for only 5% of the total, but it is expected to double by 2030. AI can be used in a variety of ways for farmers, including the development of learning simulators for farmers who wish to pursue livestock farming, the development of algorithms to determine livestock production, the development of algorithms to understand mortality and disease losses, development of intelligent expert systems, and so on. Although AI has both advantages and disadvantages, it is also true that robots cannot replace people. Humans are endowed with the ability to be creative, which robots will never possess (Singh 2019). Greenhouse gas emissions primarily lead to global climate change that leads to global warming (Pearce et al. 2014). The livestock sector contributes 14.5% of GHG emissions worldwide, and thus may elevate degradation of land, air, and water along with reduction in biodiversity (Gerber et al. 2015). Consequently, the climate change will affect livestock production through affecting the quantity and quality of feeds, disease outbreaks, heat stress, and loss of biodiversity while the expected demand for livestock products will increase by 100% by mid of the twenty-first century (Garnett 2009). Therefore, to prevent environmental degradation and to optimize livestock production, there are certain smart techniques and technologies which shall be followed by the farmers. These technologies are also detailed in this chapter. On the whole, this chapter exhaustively details all the smart technologies in livestock production.

2.2

Novel Terms

There are some novel terms related to information and communication technologies which the readers must understand. Few of them are explained by Singh et al. (2021b) as follows: • Agvocacy: The word itself is an amalgamation of advocacy and agriculture. It is the positive encouragement of the agriculture industry. When taking part in agvocacy, you are telling your agriculture story. This provides genuine answers to those outside our industry about the product safety, humane treatment of the animals, the reasonable sustainability of the farm practices, and many more queries. In the absence of agvocacy, the extremist groups can manipulate the truth of the agriculture industry and the people practicing it. • Felfies: or Farm Selfies, is the newest edition to self-portraits where people post pictures showing smiling faces with cattle, fields, and farm equipment in the background. • Millennials: Also known as Generation Y or Gen Y, the millennials are the demographic cohort being the successors of Generation X and predecessors of

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Generation Z. These digital natives are known for their extensive use and acquaintance with the elaborate digital services like the internet, social media, and the mobile devices. People in the developing world grew more educated between the 1990s and the 2010s, a factor that aided economic growth in many countries. Social media readiness: The degree to which an organization is prepared for elements relating not only to technology but also to organizational and business imperatives, as well as the ability to successfully implement social media activities. Artificial Intelligence (AI) is typically defined as the science of programming computers to perform tasks that would normally be done requiring human intelligence. Machine learning (ML) is the procedure by which an AI performs artificial intelligence activities using algorithms. Deep learning (DL) is a sort of machine learning that uses numerous layers of processing to retrieve progressively increasing level features from data. It is based on artificial neural networks. Internet of Things (IoT) can be defined as a network of physical devices and other systems that are embedded with sensors and electronics, allowing them to communicate and connect. Cloud computing is a system that allows for worldwide access to shared reservoirs of configurable system resources that can be deployed quickly and with minimal administrative effort, often using the internet.

2.3

Thematic Areas of Information Technology (IT) Use in Livestock Farm Management

• Breeding: This contains information on the genetic stock of the animal and all facets related to animal rearing related to pregnancies, vaccinations, and diseases (Hayes et al. 1998). • Individual records: For keeping track of pictures and body measurements over time, with assistance of electronic identification devices (Stubbs and Ross 1985). • Herd: It shows information about various species/categories of animals on the farm like male/female, milking/dry, adult/heifer/calf, etc. • Selection/culling: When an animal is bought, sold, or transported to another farm, an entry is created automatically. • Feeding: Feeding details of individual animals, concentrate feeding, compounding of feed, balancing of feed, rationing using various feed ingredients, etc. can be done (Nath et al. 2002). • Milking: Individual production records, milk disposal. ICTs have become important tools in Africa and other developing countries for monitoring livestock production.

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• Health: Complete individual/group health records, including details on symptoms, drugs used, dose, route of administration, and so forth (Stubbs and Ross 1985). • Task Reminder: Any planned activities, such as the projected delivery date, veterinarian services, stall cleaning, and so on, should be communicated to you ahead of time (Giovannini et al. 2003). • Finance: Income and costs are broken down into several categories, and balance sheets can be generated (Paterson et al. 2000; Murray and Sischo 2007). • Integration with newer technologies: In order to boost farm productivity, herd management software is a must if the farm manager desires to incorporate contemporary technology such as Radio Frequency Identification Devices (RFID), revolutionary machine milking, and so on.

2.4

ICT Applications

Information and communications technology (ICT) is used in most of the fields such as e-commerce, e-governance, banking, agriculture, education, medicine, defence, transport, etc. The use of ICTs in the livestock sector dates back to the relay of livestock related programmes on radio and television (Singh et al. 2021a).

2.4.1

Radio Frequency Identification Device (RFID) Technology

RFID is the abbreviation for “radio frequency identification”. As the name indicates, digital data is used by this technology which is read through radio waves after encoding. RFID systems consist of three parts: an RFID tag, an RFID reader, and an antenna. An RFID tag in turn consists of two parts: an integrated circuit and an antenna, which transmits the data to the RFID reader. The RFID reader then works for the conversion of the radio waves into a practical form of data which is transferred to a main computer system through a communications interface, for storage and analysis of the data (Singh et al. 2021b). The RFID tags on the packages of agricultural products allows the farmers to ascertain the quality of the product, which further makes it convenient for processing companies to simultaneously add valuable information on the tag, like processing date, batch processing, enterprise codes, and package weight. RFID has also been used in the livestock sector and the examples are detailed in Table 2.1.

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Table 2.1 RFID based technologies adopted by farms in India Organization IIT Delhi and NDRI, Karnal

Start date July 2008

Operational area NDRI, Karnal

Institute of Financial Management and Research (IFMR), Dairy Network Enterprise (DNE) and Ergo-HDFC GIC Ltd.

April 2009

Thanjavur, Tamil Nadu

ITGI (IFFCO-Tokio General Insurance Co. Ltd.) Pasudhan Bima

August 2009

Gujarat, Maharashtra, Punjab, Rajasthan, and Odisha

Benefits • The temperature and humidity sensor based mist controller and water trough to control water flow based animal proximity sensor have been put in a cattle yard for testing by IIT Delhi. Models are being developed to analyse the animal behaviour. • The online policy disbursal on the same day for covering the farmer without delay once the insurance policy has been purchased. • Low premium due to reduction in mortality rate by integrating the insurance schemes vaccination and deworming of animals. • Better animal health management by timely execution of protocol based veterinary and animal husbandry services. • Initial economic study revealed that the investment cost would be recouped even if only 0.5% of insured animals’ false claims could be avoided. Cheating by farmers is difficult due to inbuilt mechanism in the system. • Using RFID technology enabled IFFCO-Tokio to change the current tagging process and brought a check on insuring non-existent or sick cattle. • RFID has helped in correct identification of the animal. • Current claim settlement time is around 10 days which is much lesser than earlier time of 30 days to issue insurance policy. • Reduction in claims. (continued)

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Table 2.1 (continued) Organization Lakshya Dairy, Jind, Haryana

Start date November 2010

Operational area Jind, Haryana

Sangamner Milk Union, Maharashtra

2011

Maharashtra

Gauseva and Gauchar Vikas Board (GGVB), Gujarat

August 2017

Gujarat

2.4.2

Benefits • Record keeping on pedigree, production, reproduction, feed, health, and costs. • To produce a stronger future herd, sound decision-making in the selection of animals with increased genetic producing ability is required. • Performance, in terms of enhancement of productivity, can be calculated • It’s easier to keep track of information about the services they provide and their insights about an animal’s health, than it was before, even with manually handwritten reports. • For improved cattle breeding, disease management, trade, and food safety, every detail of a cow’s history may be recorded and retrieved, and the animal and its products can be traced back to their exact origin. • An RFID-enabled mobile computer may write information such as the animal’s date of birth, breed, milk yield, and owner’s name on it.

Mobile Phone Technology

Cellular communication is carried out using mobile technologies. Over the last few years, mobile technology has advanced at a breakneck pace. A conventional mobile device has evolved from a simple two-way pager to a mobile phone, GPS navigation device, embedded web browser and instant messaging application, and a portable gaming console since the turn of the millennium. There were 4.66 billion active internet users globally in January 2021 (59.5% of the global population). 92.6% (4.32 billion) of this total, used mobile devices to access the internet. Mobile phones are used for sending SMS, for calls, for using different applications, and for using social media for the input and output of information. The few applications of mobile phone technology are detailed below (Singh 2019).

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Mobile Apps

Mobile apps are proving to be very handy tools for need-based information dissemination among livestock farmers. Various national and regional institutions are developing mobile apps on various aspects of livestock farming in regional languages. The package of practices can be easily disseminated using mobile apps with high interactivity. Mobile apps provide a provision for content developers to disseminate information in the form of images, text, graphics, videos, etc. (Singh et al. 2021a). The commonly used mobile apps have been listed in Table 2.2.

2.4.4

Extension Advisory and Social Media

The effectiveness of Extension and Advisory Service (EAS) agencies can be ascertain by the evidence of its contribution towards strengthening and adapting the innovation networks to the extreme events that impact agricultural production and productivity. EAS agencies which provide farmers with information, knowledge, training, and other resources which are necessary for sustaining rural livelihoods. These agencies belong to multiple sectors and facilitate farmers with latest production and marketing skills. Extension advisory and social media are used for knowledge dissemination (creation of different groups and pages on social media for dissemination of information to farmers), direct marketing, for peer-to-peer networking as well as using farmer friendly platforms for online demonstrations (Singh et al. 2021a).

2.4.5

Pashu Palak Tele-Advisory Kendra (PP-TAK)

PPTAK is NABARD sponsored project earned by GADVASU, Ludhiana. It is the first of its kind initiative for livestock farmers. Advisory and information through tele-communications will establish a strong linkage between the farmers and the University all around Punjab. The call centre facility of GADVASU will reach the information deficit areas, thus providing an impulse to livestock farming. The centre will be linked to a Mobile App which will further increase its overall utility. Farmers can call on phone numbers 62832-58834 and 62832-97919 for query redressal and advisories on livestock farming.

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Table 2.2 List of mobile apps developed by various institutions for farmers Name of the App m-Kisan

Launched on July 2013

Developed by NIC, GoI

IVRI-Pashu Prajanan App

2017

ICARIVRI

IVRI-Shookar Palan App

2018

ICARIVRI

IVRI-Artificial Insemination App

2018

ICARIVRI

IVRI-Waste Management Guide App

2019

ICARIVRI

Information Network for Animal Productivity & Health (INAPH)

–

NDDB

Pashu Poshan App

2015

NDDB

Use m-Kisan is a mobile-based extension service that aims to give resource-poor farmers with information on crops, livestock, market pricing, and weatherbased alerts. The major reproductive diseases/disorders covered in the App are Anoestrus, Repeat Breeding, Silent Estrus, Uterine Torsion, Dystocia, Abortion, Uterine Prolapse, Retention of Foetal Membranes, Metritis, Brucellosis, Campylobacteriosis and IBR - IPV. The App additionally provides basic information on Artificial Insemination in cattle and buffaloes. The App is presently available in Hindi, English, Punjabi, Assamese, Bengali, Gujarati, Tamil, and Malayalam languages. This app provides information about commercial pig farming. The app consists of model bankable projects for ease of the farmers to start the enterprise. The app is currently launched in Hindi. The Punjabi and English version of the app is being developed. This app provides information on artificial insemination in case of cattle and buffaloes. The app is developed in English. The app contains a linked software for record keeping and links to watch instructional videos. The app provides comprehensive information about management of waste originating from agricultural, livestock and household activities. NDDB has created the Information Network for Animal Productivity and Health (INAPH), a Desktop/Netbook/ Android Tablet based field IT programme that allows for the collection of real-time, accurate data on breeding, nutrition, and health services delivered to farmers’ doorsteps. This system allows the implementing and monitoring bodies to monitor the project on a real-time basis. This app can be used on phones and tablets and is based on the Android (continued)

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Table 2.2 (continued) Name of the App

NDDB AGR

2.4.6

Launched on

2016

Developed by

NDDB

Use operating system. With the help of this software, a balanced ration is formulated while optimizing the cost, taking into account the animal’s profile, such as cattle or buffalo, age, milk production, milk fat, and feeding regime, among other factors. The livestock farmers are also provided with advisories to use locally available resources for feeding their animals. An easy and interactive way to understand Good Animal Management Practices and Clean Milk Production. Learn how to handle milk hygienically. The app showcases Good Animal Husbandry practices that are easy to imbibe and follow. It showcases practices on clean milk production, Ration Balancing Programme, Green Fodder and also captures health-related aspects.

Other Initiatives for Connecting with Farmers

Other than the above-mentioned initiatives, various platforms like teleconferencing, videoconferencing, satellite phones, and emails are used for connecting to the farmers (Singh 2019).

2.4.7

Information Systems

Information systems are the integrated software and hardware systems connected so as to provide information regarding a particular activity or set of activities. Information systems provide need-based information to the farmers and other stakeholders.

2.4.7.1

E-Choupal

Started in the year 2000, e-Choupal covered a target of 38,000 villages, 6500 kiosks in nine states. Indian Tobacco Company (ITC) Limited started e-Choupal to link the farmers directly through the internet for purchasing agricultural and aquaculture products. The computers were installed in rural areas of the country to provide farmers with up-to-date marketing and agricultural information under this project.

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WMSDP (Web Module for Scientific Dairy Practices)

Sher-E-Kashmir University of Agricultural Sciences and Technology in Jammu established a Web Module for Scientific Dairy Practices to disseminate need-based information about scientific dairy practices to dairy producers. This information system was developed in the English language and contains information on scientific housing, feeding, breeding, healthcare management, etc. Microsoft dot (.) net technology was used to develop this information system. WMSDP and other ICT technologies can be a great medium for disseminating needed information to farmers (Singh et al. 2020a, b).

2.4.7.3

BroiLearn

Web module on Broiler Farming (BroiLearn) was developed by Sher-E-Kashmir University of Agricultural Sciences and Technology of Jammu, and consists of comprehensive information on scientific broiler farming covering important aspects like breeds of chicken, poultry housing, nutrition, brooding, diseases, farm equipment, and recent trends in broiler farming like organic broiler farming, contract farming, institutional finances for poultry entrepreneurship development, poultry waste management, etc.

2.4.8

Expert Systems

The expert systems are almost similar to information systems but are developed for a specified purpose like milk fat analysis, disease prediction, etc. The programming in expert systems is more rigorous as compared to information systems as these are more result oriented. Few examples of expert systems are shown in Table 2.3.

2.4.9

Web Portals and Websites

Web portals provide two-way communication whereas one-way communication is the feature of a website. Few examples of portals are Agritech Portal by TNAU, Coimbatore, Vet Extension, Information Network for Animal Productivity and Health (an application that allows for the collection of real time, accurate data on breeding, feeding, and health services at the farmer’s doorstep), etc. Table 2.4 lists the web portals developed for the farmers along with their benefits and Table 2.5 highlights the Educational software developed by ICAR-IVRI.

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Table 2.3 List of expert systems developed for the benefit of livestock farmers Name of the system Automatic Milk Collection Unit Systems (AMCUS)

National Animal Disease Referral Expert System (NADRES)

Start date 1996

2011

Developed by National Dairy Development Board

NIVEDI

Operational area Gujarat

For the whole country

Benefits • Facilitate easy and fast payment for the milk delivered. • Information on fat content, quality of milk and payment to farmers is also provided. • The testing of milk takes place within 2–3 h after milk collection. • The card reader unit of this system enables the users fast speed of operation and error-free entry of the data. • About 13 priority diseases has been identified by ICAR— NIVEDI based on the past incidence patterns and built a strong database of these diseases. This database forms the basis of NADRES which further provides monthly livestock disease forewarning. Based on this forewarning, alerts are sent to animal husbandry departments, both at the national/state level, to take appropriate control measures for livestock diseases.

2.4.10 Educational CDs There are many educational CDs developed by ICAR-Indian Veterinary Research Institute (IVRI). “Health Information System” is a CD in Marathi language which includes detailed information related to important diseases of the dairy animals. “Digital Pashuswasthya-avum-Pashupalan-Prashnottri” is a well-understood answer to the farmer dilemma, since it contains commonly asked questions (500) on animal husbandry and veterinary science. Other CDs developed by ICAR-IVRI include video CD on Scientific swine management (SSMV) in Hindi and English, video CD on Integrated farming system (IFS) in English and Tamil, audio CDs on Livestock diseases Part-I, Livestock Diseases Part-II and Neonatal Calf Management. Educational CDs on bovine reproduction are developed by GADVASU, Ludhiana.

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Table 2.4 Web portals developed for farming community along with their benefits Name of the portal Agropedia

Start date January 2009

mKisan

2013

C-DAC, NIC

For the whole country

Epashuhaat

2016

Department of Agriculture, GoI

For the whole country

Developed by Government of India with assistance from World Bank

Operational area All the states of country

Benefits Agropedia is an online knowledge resource for agricultural information in India. It offers universal meta models and localized content for a wide range of users, as well as collaboratively produced interfaces in different languages. It provides information on improved animal husbandry and fish farming practices. This portal was started along with mKisan SMS service in order to register farmers for the same. For subscribing to the SMS service provided by mKisan farmers have to register them in this portal. Furthermore, it also registers farmers for USSD, IVRS, KSewa, and KCC. This portal also contains the link to download apps related to agriculture and allied sectors in India. The portal is simple to use because it does not require a login to view information. However, prior registration is required for every transaction. Sellers can create an account, post animal details, including photographs, change those details, and offer other important information, such as their complete address, so that buyers can contact them easily.

2.4.11 ICT-Based Models 2.4.11.1

NDDB: Next Generation AI Service Delivery Model

The National Project on Cattle and Buffalo Breeding (NPCBB) of the Government of India has mandated the support to all initiatives connected to the extension of Artificial Insemination (AI) delivery services. However, NDP I approved a pilot concept for sustainable doorstep AI delivery services that followed Standard Operating Procedures and were provided by a professional service provider. It is anticipated that the Pilot Project will become self-sustaining in 5 years and will continue to provide AI services without the need for outside funding. By the end of NDP I, the Pilot Project expects to have inducted around 3000 trained mobile AI Technicians

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Table 2.5 Educational software developed by ICAR-IVRI Name “Pashudhan-avum-Kukkut Rog Suchna Pranali” (PAKRSP)—An information system for farmers in Hindi language.

“Livestock and Poultry Disease Information System” (LPDIS)—An information system for students and other stakeholders in the livestock industry in English language.

Goat Health Management Information System (GHMIS)

Use This software is basically an information package and provides information regarding 78 most important diseases of livestock and poultry in India. This system is made interactive for farmers by incorporating animations, photographs, and voice back up. The package is provided to the farmers in the form of a CD and provides information on animal diseases, primary aid for most of the ailments, helps in disease identification, its prevention, control, etc. The information system is in English and is housed on a CD that includes voice over, animations, and photo and line illustrations. This approach is useful for students and veterinary professionals dealing with illness treatment in cattle and poultry, including disease identification, prevention, control, and timely treatment. This software can be purchased from ICAR-IVRI. Goat owners can learn about numerous diseases that affect goats, as well as how to differentiate between healthy and sick goats along with goat vaccination and deworming schedules. The technology is supported with language-specific voice, text, and high-quality images that help to convey goat health information in an engaging manner.

and completed approximately 4 million AIs in a financially self-sustaining manner. All the activities were destined to operate through this model digitally (Singh 2019).

2.4.11.2

E-velanmai Model by Tamil Nadu Agricultural University

According to studies published in May, less than 20% of the innovations developed by State Agricultural Universities and ICAR labs in India were transmitted to farmers’ fields. “e-Velanmai” is an ICT-based model developed by Tamil Nadu Agricultural University (TNAU) for timely dissemination of agricultural technologies. This experiment began in July 2007 and was carried out with the help of the Tamil Nadu government (Singh 2019).

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Table 2.6 Various projects undertaken by ISRO for farming community in India Name of the project National Agricultural Land Use Mapping

Start date 2004–2005

Forecasting Agricultural output using Space, Agro-meteorology and Land based observations (FASAL) Automatic Weather Stations (AWS) and Doppler Weather Radars (DWR)

2007

Internet based Dairy Geographical Information System (i-DGIS) by NDDB

2010

2014

Utility The information on net sown area is critical for national planning and identifying possible food security zones. Since 2004–2005, multi-temporal AWiFS datasets have been used to give near real-time net sown area of the country on an annual basis at 1:250,000 scale. Predicts crop acreage using Space, Agro-meteorology and Land based observations. Weather forecasting. Advisories to fishermen in coastal areas Cyclones are forecasted According to the Census of India, this system provides locational and attribute related information for around 5 lakh villages out of approximately 6 lakh inhabited villages in the country (which includes all villages in the country’s key milk producing States), as well as all towns and cities. As people census, livestock census, and land use/land cover of the village are all integrated and displayed in one place on the digital map, i-DGIS can be utilized as a powerful visualization tool for planning operations in the operational region.

2.4.12 Satellite Broadcasting by Indian Space Research Organization (ISRO) ISRO is the premier organization which works for satellite communication in India. ISRO has implemented various projects as shown in Table 2.6 for the farming community of the country.

2.4.13 Remote Sensing and GIS Based Mapping GIS stands for geographic information system, and it is a computer-based tool for mapping and analysing objects and occurrences on the planet. GIS technology combines typical database functions with maps, such as querying and statistical analysis. Remote sensing, on the other hand, is the science of gathering data about an object or an event without making direct touch with it. Both remote sensing and GIS

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based mapping can be used for livestock production and management in the country. Fodder production, movement of vector, wildlife inhabitation, livestock waste management, area under fodder crops, etc. can be analysed using features of remote sensing and GIS, which will help in livestock related policy and planning (Singh and Brar 2021).

2.5

Artificial Intelligence and Its Application in Livestock Sector

In India’s agriculture and allied sectors, Artificial Intelligence (AI) now accounts for only 5% of the total, but it is expected to quadruple by 2030. AI can be used in a variety of ways for livestock farmers, including the development of handy tools for disease identification, estimation of milk production, development of AI-based information and expert systems, estimation of disease losses, development of learning simulators, etc. Although AI offers both advantages and disadvantages, it is also true that robots cannot replace people. Humans are endowed with the ability to be creative, which machines will never possess. This chapter examines the artificial intelligence-based ICTs now in use in the livestock industry, with a focus on technologies for animal products (Singh et al. 2021b).

2.5.1

Applications for Livestock Health

2.5.1.1

Livestock Disease Control

The “National Disease Control Information System” (NDCIS) of New Zealand, according to Ryan and Wilson (1991), provides a database on important animal diseases like tuberculosis and brucellosis. Contagious animal disease outbreaks, according to Jalvingh et al. (1995) and Sanson et al. (1999), necessitate prompt identification and elimination of all virus sources due to their monetary importance. The use of computerized decision support systems (DSS) appears to have potential for managing large amounts of data and assisting in the proper prioritization of tasks.

2.5.1.2

Programme for Monitoring Emerging Diseases (ProMED)

This is an International Society for Infectious Diseases (ISID) programme. Open to all sources, the global electronic reporting system for outbreaks of promising infectious illnesses and poisons.

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Disease Monitoring and Surveillance

Animal disease monitoring refers to continuing efforts aimed at determining a population’s health and illness status. The disease might be a specific infectious disease or general health, and the monitoring activities include the routine recording, analysis, and dissemination of disease or health-related information. Sickness surveillance is a more active approach that suggests that if data indicates a disease level above a given threshold, some type of guided action will be performed. International Agencies involved in Disease Monitoring and Surveillance are WHO Statistical Information System (WHOSIS), World Organization for Animal Health (OIE) (Kivaria and Kapaga 2006).

2.5.1.4

Robotic Imaging

Penn State University’s veterinary college is the world’s first veterinary teaching hospital to use the EQUIMAGINE robotics-controlled imaging system. This system is eyed as better clinical and research advancement in animal as well as human health (https://www.vet.upenn.edu/veterinary-hospitals/NBC-hospital/services/imaging/ robotic-imaging). Furthermore, robotics-controlled computed tomography (CT) scans of various body parts are offered by New Bolton Centre. There are several advantages of obtaining CT scan with New Bolton Center’s EQUIMAGINE system viz. • • • • • •

The patient is standing and awake without much sedation. Scans are obtained using sedation which decreases risk to the patient. Less time to obtain a scan, i.e. 30 s. High-quality images are obtained. Diseases can be diagnosed easily. Board-certified radiologists at the New Bolton Center interpret the scans and can help with image acquisition.

2.5.1.5

Canine Patient Simulator

In the year 2010, the world’s first robotic dog simulator for training purposes was developed which led to the establishment of a new simulation centre at Cornell’s College of Veterinary Medicine. These pet simulators are cutting edge learning tools which are used to teach students. These are in line with animal ethics and welfare. Students can learn using these tools effectively without causing harm to the real animal (Fletcher et al. 2012).

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Thermal Imaging Cameras

A thermal imaging camera is a handy tool for examining an animal body. These cameras can be used quickly and are reliable non-contact methods. Animals need not be sedated, need not to be touched and also there is almost zero exposure of the animals to harmful radiations. A potential benefit of these cameras over the conventionally used diagnostic aids is that these cameras provide real-time results right away to the owners (Singh et al. 2021b).

2.5.1.7

Anti-Stress Ear Tag for Cattle

With robust, real-time animal state monitoring, the anti-stress ear tag enhances herdwide production providing an analysis of about 200 physiological parameters. It helps in heat sensing and advice for the time of insemination. It helps in easy and early detection of diseases. It provides insights on the body condition of the animals and thereby advises the farmers for balancing the right nutrition for optimum body condition of the animals. It also provides integrated herd management solutions along with timely reporting.

2.5.1.8

Pig Respiratory Disease Package

This package consists of a microphone and a sound analyser connected with a computer to analyse pig sounds. The microphone picks up any changes in the pigs’ voices, coughing, or respiratory distress and sends it to the analyser. Any sound that deviates from the norm is detected. It is useful for diagnosing diseases 7–10 days before they appear since it is quite effective at detecting even tiny changes in pigs’ respiratory sounds.

2.5.2

Applications for Livestock Production

2.5.2.1

3D Cameras to Assess Beef Cattle

3D cameras have the potential to enhance livestock productivity. Generally, these cameras are used to assess beef cattle. These cameras take multiple pictures which are tested to form a convolutional neural network based algorithm. Based on the algorithm results, the cameras assess the cattle based on body condition score (BCS). Whenever the cattle has a high or low body score, the alert is made to the farmer (Singh et al. 2021b).

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Automatic Feed Manager

Automatic Feed Manager is a complex system based on sensors and predictive data analytics. Sensors identify any changes in the batch of the feed manufactured and alerts the manufacturer (Karn et al. 2019).

2.5.2.3

Robo-Cams for Poultry

Ground robots were first utilized in the University of Georgia’s (UGA) experiment farmers to determine the practicality of using robots in poultry houses. The results of this experiment revealed that robotic systems in flocks have no harmful impact on the birds. The utilization of robot cams is feasible in poultry houses, however, studies are underway for their automation in poultry houses (Poultry Tech 2016).

2.5.2.4

Virtual Fences for Controlling Cattle

Experiments have shown that cattle may be kept away from a place by using audio and electrical stimulation applied remotely. Cattle learn this stimulation and move to the other part of the pasture. Marsh (1999) proposed that the GPS can be used along with electrical stimulation. The use of GPS technology to track the whereabouts of wildlife is common. Marsh’s work to include bilateral stimulation, using separate sound stimuli for each ear, has led to the better control of animal. The actual stimulus used is a combination of audio tones and electric shocks.

2.5.2.5

The Dutch Cattle Expert System (veePRO)

The Dutch Cattle Expert System was developed by a Dutch organization named Veepro and this expert system may prescribe feed diets, treatments, and livestock health and welfare conditions. It also aids in animal reproduction by suggesting the mating partners whose progeny can lead to better production results. This system also keeps a proper record of individual and group of animals regarding their production, health status, etc. and provides advisories for optimal production. The expert system provides detailed recommendations on health measures to be adopted in the farm to prevent diseases and maintain herd health. The system is efficient in the development of tailor-made breeding programmes particular to the herd.

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2.5.3

Applications for Animal Reproduction

2.5.3.1

Smart Neck Collar

Smart collars have shown to be beneficial not only to health management but also to fertility. The smart neck collars are sensor based equipment for recording various physiological parameters. Based on the collected data, the analysis is done by a computer to yield results. These neck collars are increasingly being used to detect the animals in heat so that the timely insemination can be done.

2.5.3.2

Face Recognition Systems

Face recognition works on the principle of image analysis. The images pertaining to patterns of spots on the animal body along with their actual face are analysed for generating results. It takes a few seconds for the system to distinguish a certain animal. These systems are helpful in keeping record of animal’s nutrition, health, and breeding status. The information obtained using this system can be used by the dairy farmers to increase or decrease the plane of nutrition, inseminate the animal, and keep record of it. The software stores information related to animals and provides farmers with necessary alerts.

2.5.3.3

Cow Gait Analyser or Pedometry

The present status of female fertility is an aspect based on multiple variables. These variables may be based on herd health, nutrition, management, effect of climates, reproduction status, etc. and aid in the cyclicity of the animal. The cyclic animals show prompt estrus signs, whereas some show weak signs. However, pedometry is based on the number of steps an animal walks a day. During estrus, the animals show restlessness and walk more footsteps. Analysis of the footsteps to ascertain the estrus behaviour of animals is pedometry. The cow gait analyser detects the animals in estrus as they walk more footsteps during heat compared to other days.

2.5.3.4

Intelligent Dairy Assistant

Intelligent Dairy Assistant serves as an aide to the livestock farmers for management of their dairy animals. It was developed by a Dutch company to track the movements of the dairy animals. The system is AI-based and consists of motion sensors which are wrapped around the neck of the animal to check its activity. It was launched in the USA in 2017 after many trials in Europe. The data received from the sensors is processed by a computer using AI to understand the behaviour of the animals in real

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time. The processed data provides information regarding the productivity of the animal and also the predictions can be made for the same.

2.5.3.5

MSUES Cattle Calculator

The MSUES Cattle Calculator app was developed by Mississippi State University’s Extension and is beneficial for the users rearing beef cattle. A reproductive calculator is included in the software for the calculation of breeding and calving periods. Another calculator for evaluating animal performance is available, with modified weight amounts for birth, weaning, and yearling primarily highlighted. The final calculator helps managers make informed decisions on medicine dosages and other health-related issues. The app is available for IOS operating systems and is free to download.

2.5.4

Applications for Livestock Products

2.5.4.1

Robotic Milking Systems or Automatic Milking Systems (AMS)

The AMS have been developed for reducing the time management constraint in dairy operations. This is based on the voluntary milking principle whereby a dairy animal decides the time of milking and interval between milking on its own. An automated milking unit comprises a milking machine, a sensor for teat position, a robo-arm for placing and removing teat cups and a gate for controlling dairy animal traffic. These systems are generally used in an open or extensive system of farm management whereby the animals spend most of their time in grazing and resting. When the cow feels that it should be milked, a cow tag sensor on the cow reads the code and sends the same to the control system. If there is less interval between the cow milking, then automatically the cow is sent out of the milking unit. The cow entering the milking unit gets the teat cleaned by a robotic arm. Robotic arm fixes the cups of milking machine on teats, milking takes place, post-milking spraying is also done and the cow is let out of the milking unit by automated operations. Cows are provided with concentrate feed after milking as a perk to get milked in the milking unit. Robotic manipulation in the milking unit is core innovation of this system. The activities of teat cleaning and milking attachment are automated by a robotic arm, which eliminates the final aspects of physical labour from the milking process. This system reduces human intervention and human touch during the complete milking process.

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Robotic Hide Puller

Robotic hide puller signifies an era of automation in the meat industry. It is based on increasing automation and reducing human touch. This is basically an instrument used to remove animal hides after slaughter. The automatic hide puller has a stainless steel stand with built-in apron washes, knives/whizzers, sterilizers, drip trays, and drainage. The principle of reducing human contact enhances the quality due to hygienic production which leads to clean meat production. This machine is made of rust resistant GI steel. Furthermore, the motion and intelligence cameras analyse the quality of the meat and assure that it is safe to consume.

2.5.4.3

Smart Packaging

AI-based packaging is replacing the conventional laser-based packaging. Cortex system is an AI-based system for livestock products packaging which consists of a camera with computer vision. The camera scans the products passing through the conveyor belt and removes the faulty ones from the production line. Cortex can also distinguish between different types of carton packing, such as gable-top and aseptic cartons, distinguishing between almond milk and broth cartons. Cortex has learned to recognize over 150 different carton types and is constantly learning new ones (Ahmed et al. 2018).

2.5.4.4

E-Nose or E-Tongue

Electronic nose or electronic tongue comes under the ambit of electronic sensing or e-sensing. It is a set of gas or chemical sensors that are embedded in an instrument and work together to form a complete sense of taste, smell, and flavour. “Electronic nose (e-nose)” consists of gas sensor arrays, whereas “electronic tongue (e-tongue)” consists of chemical sensor arrays. Sensor arrays are typically used for quick sensing, and their cost is less than that of traditional analytical equipment such as a laser scattering analyser, gas chromatography–mass spectrometry (GC–MS), and high-performance liquid chromatography (HPLC). Sensor arrays can be used to determine a variety of food qualities with respect to microbial, sensory, and processing (Matindoust et al. 2016). Sensor arrays are used in conjunction with classification algorithms and data pattern recognition technologies to achieve these applications. Artificial neural networks can be used to analyse the data collected by these sensors in order to determine meat quality.

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Meat Quality Evaluation using Computer Vision

Computer vision (CV) or imaging technology has gotten a lot of attention as a non-destructive and quick way to measure the quality aspects of agricultural products, including meat and meat products, all over the world. The idea behind artificial intelligence systems that retrieve information from images is known as computer vision. The image data may be retrieved from images, videos, and any other source of pictures or cameras. The pictures are thus analysed to come up with defects of manufacturing, processing, or packaging. Computer vision, like ultrasonography, provides details about the meat structure by detecting the reflected signature of the medium’s interior structure. Traditional meat quality assessment methods, on the other hand, have several drawbacks, such as being costly and time-consuming, whereas computer vision is non-destructive and rapid which makes it a better alternative for assessment of meat quality.

2.5.4.6

Bio-Sensing Technology

There are many health hazards which are related to the meat industry, viz. pathogens, chemical residues, toxins, drugs, heavy metals, etc. To identify these hazards, there is a requirement of precise and handy tools which can ensure food safety. Biosensors are novel aids to ensure food safety which works on the principle of conversion of chemical signals into electronic signals and thereby detecting a hazard in food products. A bioreceptor recognizes the target hazard and emits an electronic signal confirming its presence. These outcomes are presented in such a manner that they can be easily understood by a user. Bio-sensing technology is easy, quick, and userfriendly technology for meat quality assessment (Velusamy et al. 2010).

2.5.4.7

AI Based Meat Sorter

Meat was sorted by human touch until the end of the twentieth century in wealthy countries, but this has recently changed to an AI-based approach. AI-based meat sorter works on the principle of near-infrared spectroscopy, X-rays, LASER, and a specific algorithm to analyse meat samples in contrast to the traditional meat sorting machines. It has a special place in quality control of the product. The product which does not meet the quality requirements are sorted in the initial stages which imparts consumer preference to the product.

2.5.4.8

CNN Based Meat Identification

The convolutional neural network (CNN) is a deep learning technique which is used frequently in the classification type of inputs. CNN learns from the input images and

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trains itself from a large dataset (Krizhevsky et al. 2017). In the meat industry adulteration of superior meats with inferior quality meats and mixing of different meats is an issue which can be resolved using CNN. It can be used for identification of meat belonging to various animal species, fresh or spoiled meat, fat content of the meat, and many more. The GoogleTM Brain team launched TensorFlow, an opensource deep learning neural network software programme, in 2016 (Abadi et al. 2016) which can be used to create an effective CNN based application for assessment of meat quality.

2.5.4.9

Ascertaining Carcass Quality or Classification

The meat industry relies on the production of lean meat, the carcass having more lean meat is graded highly and fetches good returns. The quality of meat is ascertained at the end of the slaughter process and largely relies on human touch and veterinary inspection. This human involvement to ensure carcass quality can be reduced by using convolutional neural networks which will ensure quality along with hygiene in the slaughterhouses. Furthermore, computer vision can also be used for this purpose.

2.5.4.10

AI Based Cameras for Food Safety Compliance

In the meat industry, safe meat production is a major concern. A smallest of contamination can lead to far reaching consequences. Although traditional HACCP procedures have reduced meat contamination on a broader scale, accountability is called into question once a product leaves the factory and enters the retail or food chain. AI-based cameras can be used at eateries, processing plants, restaurants to ensure meat safety and hygiene. These cameras can detect whether the employees are wearing proper safety suits along with tracking their movement and physiological status. While detecting any indiscipline or anomaly, an alert can be issued to the owner of the food joint or processing plant.

2.5.4.11

Intelligent Cleaning Systems

Keeping meat handling and manufacturing plants, slaughter houses, abattoirs, and butcheries clean is a major concern. Most of the companies use automated cleaning systems which are untouched by human hands. But the question arises, what if the pieces of equipment or the machines as a whole are contaminated? Customers these days have been enlightened, and they know that every automated process may not guarantee a product to be safe for consumption. If we compare traditional cleaning methods with the intelligent cleaning systems, it can be found that the former cannot remove minute food particles which leads to pathogen build-up thus reducing the quality of the product.

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Development of Meat Products

Many meat products are available now, each with its own set of components, production processes, and possibility of being purchased by people. Several hundred meat products are produced at a time by one firm. When humans are involved, it is always a gamble to keep the original flavour. Machine learning algorithms used by AI play a crucial part in precisely adding ingredients to a meat product, as well as managing temperature and processing conditions.

2.5.4.13

Meat Supply Chain Optimization

Neural network based algorithms can calculate the present supply and future demand of the meat products. The marketing of products can be optimized by monitoring the demand and supply of products. The perfect balance can be made between the demand and supply which will lead to better customer experience and stable market prices of the livestock products.

2.5.4.14

Marketing of Livestock Products

Information Technology (IT) is used by the National Dairy Development Board to deliver profits to a large number of farmers which are involved in dairy sector. This method has reduced the alteration of milk and prompted payment to the farmers. It is because of transparency in milk marketing, the dairy sector in India has seen an unparalleled growth (Sharma 2000). In India nearly 2500 computerized milk centres are functional today (Kenneth 2001). “Warana Wired Village” project of Maharashtra is an existing computer network used for milk marketing of dairy cooperatives.

2.5.5

Applications for Animal Welfare

2.5.5.1

Robot Fish

A robot fish is an endeavour to replicate the original fish using modern day robotics. It is a bionic robot which mimics the working of a living fish. The first research on robot fish was published by Massachusetts Institute of Technology in 1989. Most of the robot fish are designed so as to emulate living fish which use Body-caudal fin (BCF) propulsion. The BCF robot fish is classified into three categories: Single Joint (SJ), Multi-Joint (MJ), and smart material-based design. The improvement of robot fish control and navigation is the most significant area of their research and development, as it allows them to “communicate” with their environment, allowing them to go along a certain course and respond to commands to make their “fins” flap. The robot fish also serves as a companion robot for the fish lovers. Moreover, more

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precision models are being developed to orient the fisheries research on robot fishes (Yu and Tan 2015).

2.5.5.2

Protection Assistant for Wildlife Security (PAWS)

PAWS (Protection Assistant for Wildlife Security) is an integrated module to prevent poaching which fetches data from the previously poached areas and routes and predict regions and routes where future poaching can take place. This module is based on machine learning (Lemieux 2014).

2.5.5.3

Man’s Best Friend 2.0

A Beijing based start-up named Roobo has developed an artificially intelligent dog named “Domgy”. The pet dog can walk around the house, remember the names and faces of the family member, and greet them by their names using facial recognition systems. It can sense and alert its own battery and maintenance systems. It can integrate IoTs in the household and turn them on and off if asked so. This pet dog provides companionship along with utility for the family members.

2.5.5.4

Minimizing Drug Testing on Animals

All the drugs rolled on for human use that need to be tested on animals has proved to be a harsh reality. One big data analytics firm is working on a technique to use artificially intelligent substitutes in place of real animal subjects. In silico Medicine creates novel medications and investigates strategies to prevent ageing and disease. Located in Baltimore, they employ computers to test clinical studies instead of real animals or humans, using analytical and deep learning approaches. The conditions are predicted using deep learning models and computer-based programmes provide data as recorded after a human trial. The systems can produce good predictions without the use of animals if given enough data, although traditional testing is still required in some circumstances.

2.5.6

Applications for Livestock Statistics

There are many AI based software which are used for analyses of data and interpretation of results. From disease diagnosis to computational genomics, the AI based software have entered animal sciences and proved their utility. Few of the software are enlisted below for reference.

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Vettel’s Diagnostic Software IBM’s Vet Computing Tool Sofie Cognitive Computing Tool Deep Mind for Record Keeping Deep Genomics

2.6

Smart Technologies for Climate Smart Livestock Farming

Climate Smart Farming (CSF) emphasizes on sustaining the farming by creating resilience in the practices through reorientation or transformation in the ambit of climate change scenario. Climate change is a recent phenomenon which is affecting all farming types globally. The ill-effects of climate change are evident through surging temperatures, forest fires, flash floods, droughts, etc. Livestock being an interwoven part of the ecological balance is also witnessing measurable effects of climate change. Reduction in feed resource efficiency, outbreak of diseases, heat stress, breeding problems, reduction in production attributes, etc. are the direct measurable effects of climate change on livestock. Unavailability of feed and fodder resources, shrinking grazing lands, production of greenhouse gases (GHGs), competition for space with agriculture, etc. can be considered as indirect effects. Therefore, for mitigating the effects of climate change on livestock production and building resilience among the livestock, there is an imminent need to draft strategies and develop technologies for Climate Smart Livestock Farming (CSLF). Although all the technologies which are discussed in this chapter can be used for optimizing livestock production and reducing GHG emissions, few being particular to the concept are detailed below.

2.6.1

Nutritional Interventions

The production of the animal depends on the type of feed it consumes. Over and under feeding should be avoided. Over feeding leads to heat loss and under feeding results in decreased production. Economic feed processing techniques like wetting of grasses, cropping and chopping of greens, grinding, and pelleting, use of ureamolasses will reduce the energy loss in the digestion and decrease the heat loss for maintenance of body temperature. Use of available green fodder during summer or efficient use of non-conventional feed resources or newer feed resources will help to negotiate the fodder scarcity produced due to adverse climatic conditions (Behera et al. 2019). Diet modification, direct inhibitors, feed additives, propionate enhancers, methane oxidizers, probiotics, defaunation, and hormones are some of the nutritional approaches that can assist reduce methane production (Moss 1994).

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Dietary manipulation through increased green fodder decreases methane production by 5.7%. Increasing concentration in the diet of animals helps in reducing methane by 15–32% depending on the ratio of concentration in diet (Singhal and Mohini 2002). The CH4 produced from molasses-urea supplementation was found to be 8.7% (Srivastava and Garg 2002) and 21% from use of feed additive monensin (De and Singh 2001). Improvements in feed efficiency and milk output can help the dairy herd emit fewer greenhouse gases and require less land (Bell et al. 2011). Highly digestible high-energy diets have been discovered to be an excellent form of summer diet for helping animals’ maintain body temperature by reducing excess heat. The animals are more comfortable when they are fed a low-fibre diet and have access to cool drinking water. In a study, it was reported that heat stress was reduced by a level of 18–20% when by-pass fat was fed to dairy animals. Feeding of increased quantities of minerals and vitamins in diet have also been useful for the livestock (Bell et al. 2011). Supplementing cows with 1.5–1.6% DM potassium and 0.5–0.6% DM sodium may help heat-stressed cows produce more milk. Antioxidants including vitamin E, vitamin A, and selenium aid to reduce the effects of heat stress by restoring oxidant equilibrium, resulting in better reproductive efficiency and animal health (Behera et al. 2019).

2.6.2

Reproductive Interventions

Progesterone supplementation during early pregnancy has shown better results. Exogenous progesterone administration during the summer season has resulted in low heat stress which boosted fertility. The use of GnRH and PGF2 to synchronize heat in dairy animals promotes fertility. Embryo transfer technology (ETT) is being investigated as a possible technique for reducing the deleterious effects of heat stress on cow reproduction.

2.6.3

Manure Management

In India, most of the animal manure is extensively used as fuel in the form of dry dung cakes or spread in the field (Singh et al. 2020a, b). Animal waste including manure accounts for more than 25 million tonnes of methane emission globally per year. Better management of animal excreta through various interventions can reduce the methane emission. The CH4 emission from subsurface applied manure can be mitigated by using manure solids separation and anaerobic degradation pre-treatment, which otherwise may be greater than that from surface applied manure. Temperature, time of application, and storage duration all influence GHG emissions from manure. Furthermore, manure contains remnants of some compounds that are harmful to both humans and the environment. Furthermore, any ill animal excretions may carry zoonotic diseases that are extremely hazardous to

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humans and can survive in the soil for several days to weeks. Animal excretions and effluents emitted by the livestock products and processing sectors contain active substances that offer a larger threat to all environmental components. Manure management can be improved by combining traditional management practices with climate wise manure management (Singh and Rashid 2017). Recycling manure is a key step in ensuring long-term animal waste management and reducing the negative environmental impact of improper management. Biogas production from animal dung has been an age-old tradition followed throughout India which is used for cooking and lighting purposes (Henuk 2001). The decomposed slurry that is left over is a wonderful supply of manure for agricultural fields since it includes 80% carbon, 1.8% nitrogen, 1% phosphorus, and 0.9% potash, making it a great source of humus and micronutrients for crops. Livestock dung has been utilized as an excellent organic fertilizer for centuries. Animal droppings are a great fertilizer since they contain all of the needed plant nutrients (Bell 2002). Because of its high nitrogen concentration, poultry dung has been identified as the most desired of these natural fertilizers (Sloan et al. 2008). Furthermore, a low-cost vermiculture system can theoretically turn animal waste into vermin-cast and vermin-meal (protein meal) (Singh and Rashid 2017).

2.6.4

Housing and Management Interventions

Good house ensures proper design, height, and orientation with good open and covered space. Adequate ventilation and comfortable floor space per animal will provide a cooler microenvironment inside the house. Proper housing ensures a stress-free environment which leads to better productivity among livestock, thus building resilience among them. While constructing animal houses, heat ameliorative measures such as foggers, sprayers, drinkers, and shady areas should be properly built. There are few recommendations which shall be taken into consideration before constructing an animal house, viz. the long axis of the house shall have north-south orientation with a height of 10–12 ft for dairy animals. The top of the house shall be painted white to reflect as much radiation as it can. The trees shall be planted surrounding the house. There shall be proper feeding and watering space for the animals to avoid competition. The comfortable bedding and optimal floor space also reduces heat stress among animals.

2.6.5

Precision Livestock Farming

The optimum utilization of resources in a farm can be obtained through precision livestock farming (Tripathi and Bisen 2019) along with cutting direct and indirect greenhouse gas emissions. Smart tags, drones, cameras, sensors, and computers are

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available for specific interventions in livestock management. The software based digital tools cut the supply of inputs after sensing the threshold.

2.6.6

Using Digital Technologies

Digital technologies have multifarious role in building resilience and mitigation of climate change in livestock production. The network of farmers can be created using social media whereby timely information can be passed onto them which will also strengthen extension and advisory services. Early weather forecasting systems can help farmers to be ready with preventive measures after the passage of calamity. Disease surveillance and monitoring systems can alert the farmers well before disease outbreak so that the preventive and control measures can be strengthened. Furthermore, livestock monitoring can be done using digital technologies like tags and collars which helps in better management (Singh 2019).

2.6.7

Better Extension Advisory Services

Extension advisory services are the backbone of any livestock enterprise. Farmers requiring any sort of information turn to an extension agent. Therefore, it becomes mandatory for extension services throughout the world to focus on climate smart aspects of farming. The new strategies should be discussed with the farmers and the literature regarding the same shall be distributed. Focus group discussions, mass media talks, popular articles in mass media, blogs on social media can be effective in spreading knowledge about climate smart activities.

2.7

The Way Forward

Until now, extension workers have been manually conveying technology messages to farmers. Due to a paucity of competent human resources, particularly in terms of labour, the modern techniques have been limited to the academic and research institutions. Even today, this gap in information dissemination is a roadblock for extension workers. According to the findings, academics and extension professionals need to be educated on how to use better technology to communicate knowledge and increase productivity in the livestock industry. Furthermore, scientists must develop and disseminate field-relevant, profitable, and long-term tools and approaches with the participation of farmers as research and extension partners in order to effectively produce and transfer technical advances. The country’s rapidly growing number of internet users demonstrates that ICTs can be a new paradigm for dissemination of livestock related information. Digital illiteracy remains a barrier,

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but this can be solved by providing livestock owners with need-based digital literacy programmes. Improper telecom coverage is also a challenge which can either be overcome by installation of cellular towers in the rural areas or by using satellite phone technology. Furthermore, the technology developers require to assess the information needs of the rural community and develop the technologies accordingly. Capacity building of stakeholders and livestock producers is required for the development and usage of ICTs, respectively. Modern protocols, such as AI-based tools, remote sensing, and Geographical Information System (GIS)-based ICT tools, have better prospects, but they must be evaluated for their cost. Utilization of ICTs for the better good of the farming community can also lead to sustainable production and environmental management. Further research should be focused on making the ICT tools more interactive, user-friendly, and cost effective.

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Chapter 3

Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in the Management of Aquaculture Production Potential B. K. Das, D. K. Meena, Akankshya Das, and A. K. Sahoo

Abstract As the scale and density of aquaculture operations have expanded, overproduction in contemporary aquaculture has resulted in an unbalanced water environment, increased fish disease outbreaks, and decreased aquatic product quality. The intelligent fish farm attempts to deal with the precise work of increasing oxygen, optimising feeding, reducing disease incidences, and accurately harvesting through the concept of “replacing man with machine” in order to completely free human labour and complacency, as a result of a labour shortage and an urgent need for innovation in aquaculture technologies. Thus, IoT adoption is increasing at an alarming rate. IoT is currently widely employed in a wide range of industries and applications. Take aquaculture, for instance, as an example of one of a number of options. Traditional farmers struggle to keep up with changes in their cultural system and the quality of their water. Cloud-based aquaculture monitoring and control systems are built on model integration. Client data visualisations were part of a system that included an open-ended smart sensor module for the management of the system’s aeration as well as components for a local network and the cloud computing infrastructure. The smart sensor module gives us information about the water that we use to monitor it. This high-tech sensor module has sensors for hydrogen potential, dissolved oxygen, temperature, and level. For every type of aquaculture, web and Android apps can assist you determine the ideal water temperature for your pond. Additionally, in India, feed dispensing and sensors have been adopted recently. Artificial neural networks and machine learning must be integrated in the logarithm in order for the system to run smoothly AI and machine learning are summarised below, along with their present state and challenges and prospects in the field of smart aquaculture. Keywords Artificial intelligence · Smart aquaculture · Machine learning · Robotics · Precision farming · Sustainable aquaculture production

B. K. Das (*) · D. K. Meena · A. Das · A. K. Sahoo ICAR-Central Inland Fisheries Research Institute, Kolkata, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_3

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Introduction

Aquaculture production in Asia accounts for 88.5% of global output. A record 82 million tonnes of fish were harvested from aquaculture throughout the world in 2018. With the increasing expansion of aquatic product output, traditional manufacturing models have played a significant role (Li and Li 2020). As a result, the quality and quantity of the world’s food supply are now regarded to be at risk due to climate change (Hamdan et al. 2015; Myers et al. 2017). Climate change is posing an increasing danger to food security, notably in terms of dietary protein (Kandu 2017). Aquaculture has evolved from a labour-intensive agricultural method to one that is fully automated (Fore et al. 2018). Intelligent aquaculture is now conceivable thanks to the rise of IoT, big data, AI, 5G networks, cloud computing, and robot technologies. Modern aquaculture development has a new commercial model for this. There are several elements to consider while assessing the water’s overall quality, including its physical, chemical, and biological characteristics. As more and more devices connect to the internet, IoT is becoming increasingly common. Aquaculture is one of several businesses that have embraced the Internet of Things (IoT). When it comes to keeping track of their cultural system and water quality, traditional farmers have a difficult time keeping up. Based on model and cloud integration, a real-time monitoring and control system for aquaculture has been developed. A modular smart sensor module, a smart aeration system for system control, a local network system, a cloud computing system, and client data visualisation were all part of this system’s functionality. Data from the smart sensor module is used to keep tabs on the water’s health. Sensors for dissolved oxygen, hydrogen potential, water temperature, and water level make up the smart sensor module. Any sort of aquaculture pond may be set to the ideal water conditions using a web and Android application. Feed dispensing and sensors, on the other hand, are relatively new additions to the Indian market. Logarithms, machine learning, and artificial neural networks must all be integrated if the system is to run smoothly and accurately. Using robots and high-tech equipment, intelligent aquaculture is able to complete the breeding and growing stages of farmed species, as well as the treatment of circulating water and the accurate feeding of animals. Intelligent aerator systems can operate the aerator, circulatory water treatment, and cleaning equipment based on water quality, fish behaviours, and weather data. For healthy and speedy growth, the intelligent feeder and deep learning consider biomass, water quality, the surrounding environment, and the fish’s behaviour. The automated fish divider may be used to gather and pool fish fry of various sizes and ages. This system’s purpose is to ensure that the circulating water system is always operating at its peak efficiency. With today’s advancements in aquaculture, a central command station, communication with each other as well as offering a comprehensive picture of an entire facility are now very essential. An IoT platform device is used to create a smart pond management system. Sensors for pond water temperature, pH, dissolved oxygen, and water level are used in an IoT intelligent pond water system. Sensors might be solar or non-solar. Fish and shrimp aquaculture are made simple with this system’s

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24-h smart monitoring. As a result, farmers can rest and save money by keeping tabs on the state of their pond culture from home using their smartphones or laptops. A human–machine interface (HMI) display, remote control, cloud storage, and a big data repository are all included in the system. Farmers may better grasp daily changes by turning daily recording and management into a graphical presentation. It is possible to employ an intelligent control system for a variety of different types of equipment such as an aerator or feed pump, as well as water temperature and salinity metres as well as pH and dissolved oxygen metres as well as water level metres. The aquaculture pond’s sophisticated control system sends out alerts when it identifies abnormalities. A microprocessor in the aerator regulates the flow of dissolved oxygen, allowing it to switch on or off the power as needed.

3.2 3.2.1

Significant Components Smart Aquaculture Collection of the Data

Collection of data utilising a variety of sensors, including temperature and humidity gauges, CO2 monitors, light sensors, dissolved oxygen monitors, and other water quality sensors, as well as cameras and other digital picture data gathering devices.

3.2.2

Data Communication

Using communication nodes to send the collected data to the command and control centre. This data may include information about fish growth, environmental parameters, operation, and resource allocation.

3.2.3

Processing of Data

Data is processed and decisions are made using the cloud platform.

3.2.4

Execution

To accomplish long-term “high efficiency, high quality, ecological, health, and intelligence” aquaculture, intelligent and automated operation execution, as well as decision feedback to each execution device, is required.

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Development of the Sensors

It is common practice for aquaculture farmers to use conventional methods and practices. The aquaculture farm’s water quality, water level, oxygen level, and stress level are measured and monitored manually by the farmer. An Internet of Things (IoT) based smart aquaculture model was presented in this work that would monitor water quality (pH, water level and temperature; turbidity; and fish motion detection) for aquaculture. Wireless sensor network modules are used to monitor and manage aquaculture in real time in this study. A water recycling system is also being considered as a way to limit the amount of aquatic trash. This system continually monitors water parameters through a serial port, reducing internet use, transmitting data on a regular basis with minimal latency and error-free, and ensuring aquatic life’s survival in the process. Increased aquaculture profitability is also a result of this practice. An intelligent aquaculture system cannot function without sensors (Su et al. 2020). In recent years, the sensor sector has grown significantly. Sensors will be used more frequently in breeding, adult fish growth, aquatic product storage and transportation, aquatic product processing, operation, and maintenance as a result of advancements in core sensor technology, modern information technology development, rapid cloud technology development, big data platform construction, and application and promotion enhancements. New sensors are being developed that are more accurate, more versatile, more cost-effective, and more network-capable at the same time that they are being developed. As new technologies in contemporary physics such as nanotechnology, laser infrared ultrasound microwave optical fibre strong magnets, radioactive isotopes, and integration technology continue to develop, this has opened up new options for sensor integration (Sharma et al. 2019). On the other hand, micrometre-level sensing components, signal detection circuits, as well as the CPU on a single silicon chip are merging to build multifunctional compact portable sensors with a wide variety of applications as well as high reliability and extended service life. Biosensing is another potential future option for sensing technologies. Unmanned intelligent aquaculture production is achievable with the development and implementation of novel sensors in all aspects of aquaculture, including field monitoring, remote diagnostics, remote data collecting and real-time operation (Jennifer 2017).

3.3.1

Sensor for Automatic Feed Dispensing

3.3.1.1

Automatic Feeding

It is particularly prevalent in big and intensive farms that require an almost constant supply of feed to use automatic feeding systems. Because this is so time-consuming, it is usually done by an automated feeder. According to Fig. 3.1, there are four key parts of an automated feeder, namely the feeder hopper, feed distribution device,

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Fig. 3.1 Automatic fish feeders on pond. (By USFWS Mountain Prairie is marked with CC PDM 1.0)

feed spreader, and power supply. Feeders can be placed on a rack or in a cage that is buoyantly attached. The control unit may be programmed to distribute feed at predetermined intervals based on the frequency and duration of feeding instructions placed into it. A feeding system can range from a basic dispenser that does not require power to a sophisticated computerised feeding system that regulates the food based on the appetite of the fish computerised feeding system.

3.3.1.2

Central Feeding System

In cage farming in India, a central feeding system is a viable option. Feed is stored in a feeder at the centre of the system. The feeder is responsible for delivering feed to fish farms by pumping it via a series of pipelines (tanks, ponds, or cages). Silos, sluice valve, water or air transport pipelines, selection valve, and distribution unit are all part of this system.

3.3.2

Fish Catch Estimation

For the purpose of digitising India’s peninsular reservoir fish catch statistics on a daily basis, the ICAR-Central Inland Fisheries Research Institute has developed an

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android app-based Electronic Data Acquisition System (eDAS) that uses real-time data from mobile phones to collect fish catch data in real time. India has 3.5 million hectares of reservoirs, which are a major source of fish production and employment in the country. Data on the state of fish production, species composition, fish variety, fishing effort, etc. are necessary for the development of management plans for fisheries enhancement and the sustainable utilisation of reservoir resources. There are a lot of water bodies with inaccessible fish landing centres that make it difficult to collect data on fish harvest from reservoirs, making this type of research expensive in terms of both time and money. Electronic Data Acquisition System (eDAS) has been developed by the Institute to overcome data acquisition issues in reservoirs and has been successfully trial-implemented in selected reservoirs in India’s three states of India, i.e. Karnataka, Tamil Nadu, and Jharkhand.

3.4

Process Control and Machine Learning in Aquaculture

AI in the strictest sense is the future created from past pieces. We learn through trial and error. Beginning with agriculture, AI has been implemented in a variety of industries. The fishing sector can rapidly advance because the aquaculture sector is less labour-intensive. For example, feeders, water quality, harvesting, processing, etc. the implementation of artificial intelligence in conserving aquatic species global fish tracking is aided by AI. AI helps considerably in IUD fishing. In aquaculture, 30% of inputs can be conserved via AI. Thus, AI can control fish production with less upkeep and decreased input costs. In a scenario where robots can think and act on their own, AI and the Internet of Things have made this possible (IOT). It is a human simulator that is pre-programmed with your cognitive ability. Nearly 50 billion electronic devices are connected to the Internet today’s IoT. Artificial intelligence is now being applied to agriculture and fishing. No part of the animal enclosure needs to be overlooked in managing the facility. Experience enables it to learn more quickly and easily. As environmental conditions change, this will aid in industry growth of the fisheries. GIS aid not only to commercial fisheries, but also non-fishing open sea management. Fish consumption has risen fourfold in the past decade. Aquaculture has increased in demand while output has fallen. Using AI increases productivity and reduces labour costs (Chrispin et al. 2020).

3.4.1

Artificial Intelligence and Management of Feeding

Feed accounts for nearly 60% of the total cost of an aquaculture system. In the containment, too little or too much feeding can cause a variety of problems. On the one hand, feeding less can lower muscle conversion and, in extreme cases (such as in shrimps), can lead to cannibalism and mutual attack. Excessive feeding, on the other

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hand, leads to waste and degrades water quality. Appetite measurement can assist in feeding the correct amount of feed at the correct time. Through vibration-based sensors and acoustic signals, AI plays a significant role in reading the fish. This will help you distinguish between a hungry and a full fish. An AI feed dispenser developed by eFishery, an Indonesian aquaculture intelligence company, releases the right amount of feed at the right time. It detects the animal’s appetite using a variety of sensors. The device can save you about 21% on feed costs. Observe Technologies is a company that develops artificial intelligence (AI) and data processing systems for measuring and tracking stock feeding patterns. It provides objective and empirical guidance on the amount of feed that farmers should feed. A smart fish feeder controlled by a remote is produced by an aquaculture technology company known as “umitron cell” in Singapore and Japan. It is a data-driven decision-making tool for farmers who want to optimise their feeding schedules. This artificial intelligence (AI) feeding devices help to reduce feeding costs while also maintaining water quality.

3.4.2

Artificial Intelligence and Drones Applications in Aquaculture

Drones allow us to collect and analyse data like the cloudiness and temperature as well as things like blood oxygen and heart rates and blood oxygen levels of fish and vertebrate life cycles, and beat-depth information. Simply connecting a smartphone to this drone makes it simple to retrieve this information and the development of the technique of using robots that are stationed in the shallow water adjacent to a farm to determine pollution levels prior to development as the robot shoaling approach was initially conceived. These autonomous water vehicles are able to navigate across the water to collect water quality information, Such long-wavelength microwaves, even those with extremely low frequencies, can be employed for communication purposes.

3.4.3

Artificial Intelligence and Disease Prevention

The biggest danger facing the fishing industry is the introduction of diseases. The most successful systems are those that compare historical data with pre-programmed information at the site to newly collected data to detect disease outbreaks. Also, they are able to administer measures to avoid incidents before they occur. A cloud-based programme called Aquacold launched in April of 2017, helping to protect both cage and wild salmon farmers from developing sea lice, as well as open-ocean farmed salmon from ocean lice, was in use. We were able to stop or even minimise the fish mortality, without having to resort to the costlier treatments.

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Artificial Intelligence and Fish Seed Screening Form Culture Sites

Identifying and selecting a suitable variety of fish as good or bad food is critical in fish farming. Because of the manual labour involved, it is difficult to employ a large number of workers for seed screenings. The Underwater Agriculture Research, Fisheries, and Forestry Research Institute (JAFRI) at Kindai University employs Microsoft Azure ML Studio to sort out and destroy “non-shaped” seeds from the rearing tank. In the Indian scenario it is imperative to develop and adopt such types of devices for betterment of the aquaculture sector.

3.4.5

Smart Phone-Based Application in Aquaculture

Researchers are working on smartphone apps that use artificial intelligence to help farmers in monitoring and track water quality and predict disease outbreaks. Farm MOJO was a completely new mobile app created by “Aquaconnect” an Indian startup focused on aquaculture, to help shrimp farmers keep an eye on water quality and predict diseases. Farmers may be able to stave off disease outbreaks long before the start of the use of these applications. In order to remain as relevant as possible, farmers and developers continuously upload photos of parasites and other maladies of shrimp to the app on a regular basis. These images make it possible for the programme to record information on the diseases to be retrieved and stored in future.

3.4.6

Artificial Intelligence and Real-Time Monitoring of Stocks

The swimming pattern, size, injuries, and other characteristics of the cultured animal can be analysed using vision-based sensors on AI devices. These records will be kept in order to compare them in the future. “Xpertsea” is an aquaculture innovation company that offers the “Xpercount” AI device, which uses machine learning and a camera to weigh, count, image, and size shrimp in seconds. These data are analysed in order to determine the stock’s periodic health.

3.4.7

Artificial Intelligence and Shrimp Culture

In the areas of real-time water quality monitoring and voice call alerts, appetitebased intelligent feeders, and automatic aerator control, Eruvaka, an Indian company, provides AI-based solutions to shrimp farmers. Farmers in Surat, Goa, Andhra

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Pradesh, and Pondicherry are benefiting from Eruvaka’s AI-based shrimp culture solutions, which are now installed on 1000 hectares of shrimp farms.

3.4.8

Artificial Intelligence and Software Development

The general assumption about aquaculture is that it is trial and error base system. The formulation of feed for fish species is initially a trial and error base method using Pearson and least square methods then updated excel version came into existence, however, some of the information such as cost, essential elements profiling, complete biochemical composition of the formulated feed was not included. Similarly, the estimation of carcass composition of fish and proximate composition of fish feed, ingredients, etc., is time intensive by traditional methods. Keeping these limitations in view, based on the facts from the Research and Developmental agencies, the companies started working on it and could develop some important instruments and software. For instance, for feed formulation, NACA, Thailand developed, and other software are also available such as Win Feed, LOTUS, etc. Furthermore, instruments such as DK-NMR are providing the sample analysis for extended biochemical composition in both liquid and powder forms. The instrument is based on memory of a particular simple that has been analysed and put into the memory so later it can give analyses in fraction of time when receiving samples for the same nature. However, these software and instruments are lacking system integration with advanced AI and machine learning for better delivery of the inputs.

3.5

Precision Fish Farming

The aquaculture sector is highly scattered and the country is always facing natural calamities. In addition, erratic rainfall and other factors sometimes create uncertainty in viable aquaculture enterprises. Therefore, it is imperative to develop the prediction and forecasting model for sustainable fish production and precision fish farming with following objectives. • Enhance the precision, repeatability, and accuracy of farming operations. • Make it feasible to monitor biomass and animals in a more self-sufficient and continuous manner. • Improve the reliability of the decision-making process. • Improve worker safety by reducing the reliance on physical labour and subjective assessments. The concept of potential fishing zone is catering the need for assessment of fish stock and biomass availability. However, it has certain limitations in terms of bad weather conditions, etc. some of the ICAR institutions like ICAR- Indian Statistical Research Institute, New Delhi (ICAR-IASRI) are working on precision farming

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models for agriculture and now they have started working on precision models for precision fish farming using GIS coupled with AI applications. This will provide the precision about the expected harvest and accordingly a management plan can be fixed for managing the culture practices so that money and expenses could be reduced up to a great extent. Thus, this technique would in turn enhance aquaculture production thereby maximising the profit margin.

3.5.1

Climate Smart Fish Farming

As a result of the presence of poikilothermic animals, which are extremely sensitive to various types of biotic and abiotic stress, freshwater aquaculture is under more pressure from climate change than terrestrial agriculture. This means that the effects of climate change on freshwater aquaculture are more complex than those on terrestrial agriculture. Fish stocks can be directly affected by climate change or ecosystems can be affected indirectly by changes in primary and secondary productivity, ecosystem structure, and composition. Fish prices and the cost of fish meal and other goods and services can also be affected by changes in the price and availability of these products and services. ICAR-CIFRI, Bararckpore is dedicated to improving fisheries and aquaculture for the benefit of the local community. In Thycattussery village, Alappuzha district, Kerala, a case study was conducted on the black clam Villorita cyprinoides in climate resilient pens. Clam culture’s involvement in decreasing carbon emissions by changing it into blue carbon is a new field of development as a climate resilient technology. In addition, it makes use of the culture system’s multitrophics to boost productivity and provide more money and a means of subsistence. Bamboo and HDPE nets were used to construct a 114 square metre experimental pen that was separated into two halves. The seeds gathered from the wild (Paathiramannal) were divided into two categories: little clams (mean length of 15 mm and weight of 1.46 g) and giant clams (mean length of 22 mm and weight of 3.54 g). Clams of various sizes were kept in separate tanks, with a total of 5000 and 2000 shells per square metre, respectively. Over the course of a year, the enclosures held 650 kg of young clams. An average clam’s weight and length increased by 14.23 mm and 47.98 g/year, respectively (Fig. 3.2). Female clam collectors assisted in the deshelling process as part of the initiative. The sale of clam flesh and clam shell brought in Rs. 26,300 for the community. Separation, washing, and sale of the meat at Rs. 100/kg and the shell for Rs. 3500/tonne resulted in a profit of Rs. 100/kg.

3.5.2

Climate Smart Aquaculture (CSA)

To ensure food security, Climate Smart Aquaculture considers both adaptation and mitigation. For climate change mitigation, adaptation, productivity, and economic growth, CSA focuses on minimising potential negative trade-offs while maximising

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Fig. 3.2 showing harvested black clam, Villorita cyprinoides in climate resilient pens. (Source: ICAR 2022)

potential positive trade-offs. The following are the prime requirements of smart aquaculture. • Improving efficiency in the use of natural resources to produce fish and aquatic foods. • Maintaining the resilience of aquatic systems and the communities that rely on them to allow the sector to continue contributing to sustainable development. • Gaining an understanding of the ways to reduce effectively the vulnerability of those most likely to be negatively impacted by climate change. • Examples of tactics for attaining CSA objectives in respect to fisheries include: the reduction of excess capacity and the implementation of fishing activities that are linked with improved fisheries management and healthy stocks; increased production efficiency through better integrated systems. • Improved feeding and reduced losses from disease in aquaculture; the reduction of postharvest and production losses; and the further development of regional trade.

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3.5.3

Nutrismart Fish Farming

3.5.3.1

Small Indigenous Fishes (SIFs) as an Alternate Livelihood Option and for Better Health Status

A diet rich in micronutrients from small indigenous fish (SIFs) can help avoid malnutrition in rural populations and provide a source of income (Fig. 3.3). Since they favour a diet rich in SIFs, the SIFs command a premium price for marginal fishermen who use traditional gear like traps, cast nets, and the like to supplement their income. Health and nutrition programmes for women and children in the NorthEastern states of India have previously been designated as a priority topic by national policy makers and policy planners because of the difficulties in delivering health and nutrition programmes due to insufficient infrastructure. Indian and international research shows that tiny indigenous fish contribute greatly to consumers’ nutrition, diet fortification, and the reduction of malnutrition. SIFs, such as Mola (Amblypharyngodon mola), river shads (Gudusia chapra, G. variegata, and Gonialosa manmina) and others (Fig. 3.4) might help ameliorate the status of underweight prevalence, stunting and wasting in children, and low BMI and anaemia in women in the North-Eastern area. It is possible to fulfil the nutritional needs of women and children by promoting small-scale fisheries and SIF consumption in the North-Eastern area, where fish is consumed by the majority of the population (95%). Many researches have been done by ICAR-CIFRI on SIFs, and they have advised the following dietary recommendations based on their findings. Global average surface air temperature increases of 1.1–6.4  C by 2100 are expected to reduce precipitation in most sub-tropical land areas and weaken tropical cyclones, according to the Intergovernmental Panel on Climate Change (IPCC). Natural catastrophes such as floods, cyclones, and even draughts are becoming more frequent, and this is having an effect on agricultural productivity. In order to accomplish the SDGs, effective disaster preparedness must focus on health, nutrition, and child safety. Investment in children’s, girls’, and women’s nutrition is a major

Nandus nandus

Gudusia chapra

Amblypharyngodon mola

Chandanama

Puntius sophore

Pethia conchonius

Fig. 3.3 Showing important Small Indigenous Fish (SIFs) species

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Fig. 3.4 Schematic diagram of a bio floc technology system (Mugwanya et al. 2021)

development objective, according to the Government of India’s NITI Aayog’s National Nutrition Strategy.

3.6

Internet of Things Technology

There are still numerous obstacles to overcome before aquaculture can completely benefit from autonomous, intelligent, and high-precision farming. Due to the highrisk nature of aquaculture practices, it is impossible to envision a world without any human management in the near future. Smart technology such as micro- and nanosensors to monitor fisheries data, bionic robots to operate production and automatically check, intelligent sorting equipment, and energy-saving processing equipment for aquatic goods would considerably automate and save labour at various phases of aquaculture. The Internet of Things, or “cloud-network-edgeend”, will link all of the equipment. The “collaborative collaboration” among the equipment determines whether the autonomous operating ground can function optimally under production settings in real-time, security, dependability, and precision (Martin 2019). “Network” technology for aquaculture IoT must be capable of transmitting data according to this list. In the first place, the network is completely unrestricted in terms of territory or time, meaning that all network equipment in the unmanned farm can connect to it. Second, with centimetre-level positioning precision and microsecond-level network latency, the maximum transmission rate may approach 100 Mbps/1 Gbps (5G technology). A network outage is less than one millionth of a percent likely with ultrahigh reliability and a density of 100 equipment connections per cubic metre. It is also possible to connect to many networks at the

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same time. As a fifth benefit, the enhanced sensing, placement, and resource allocation capabilities of fishing gear will be enhanced by integration with cuttingedge technology like artificial intelligence (Jenssen 2019). If future aquaculture information transmission technology is to survive network assaults and trace the source of attacks, it should be able to do so.

3.7

Digitisation of Equipment, Precision Control, and Cutting-Edge Computing Techniques

Achieving fully automated operation of an aquaculture system requires precise control of the equipment. Aeration is a good illustration of this. In the classic arrangement, the farmer must manually turn on and off the switch in order to adjust the water’s oxygen concentration. Using computers or mobile phones, farmers may now transmit orders to control equipment remotely, allowing actuators like the pump and aerator to be activated and deactivated by themselves. The sensor in the intelligent aquaculture system may provide the oxygen reading directly to the system, allowing the system to comprehend the dissolved oxygen in the water in real time. A water pump, electronic valve, or water treatment equipment can be turned on or off by the central controller based on data gathered and parameter thresholds. Both a professional expert database and reliable sensors are clearly necessary for the equipment to continue to function properly. Using an actuator in an aquaculture system might shorten its useful life because of the many variables that can influence it. If a machine breaks down and is not fixed right away, the automation process will be thrown off. The vast majority of aquaculture equipment troubleshooting solutions are still experimental despite the fact that certain professionals have done substantial study into them. As a consequence, additional study is required to boost equipment monitoring in order to improve the precision and integrity of the intelligent aquaculture system.

3.8

Management of Big Data and Cloud Computing

To monitor, detect, and regulate aquaculture requires an extraordinarily sophisticated system owing to the wide variety of influencing elements and the variety of aquatic organisms (Rao et al. 2018). Large amounts of data created by aquaculture practices may be easily analysed and presented to producers and decision makers in an easy-to-understand style using big data technologies combined with a cloud platform (Roy 2020). Big data and cloud platform technologies have a subcategory called aquaculture that falls under this umbrella (Balakrishnan et al. 2019; Figueroa et al. 2018). The collection, categorisation, processing, administration, mining, and analysis of aquaculture data can supply producers and decision makers with useful

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information. Aquaculture makes extensive use of big data and the cloud platform for data collecting, storage, data mining, and application development (Cruver 2015; Chen et al. 2019). Data from aquaculture production, processing, and sales can be gathered using a variety of data collecting technologies, including the Internet, Internet of Things sensors, industrial management systems, professional databases, and classic format data in traditional formats. Solutions to data storage and processing issues in aquaculture are typically found in the realms of information storage and computation. As a result of the diverse nature of aquaculture, big data must be integrated before it can be saved in a target database or processed further. Targeted techniques are required for a wide range of aquaculture datasets, data storage, and processing methodologies. Aquaculture’s complex production environment generates a wide range of data that are heterogeneous and uncertain, making it difficult to build mechanism models using traditional data analysis methods. Human cognitive ability is used to learn natural laws from data, and mechanism models are then built (Huang and Wu 2016). An accurate model of the real world may differ from the one prepared in advance because of human observation. It is possible to automatically discover hidden patterns in data using data mining and analysis technology (Ma and Ding 2018), to build aquaculture data models and analysis tools, to integrate these into the aquaculture big data cloud platform, and to provide users with analysis results and data services for decision-making. Prior to, during, and following production, the aquaculture business has employed big data analysis and cloud platform technologies in tandem (Roy 2020). Aquaculture environment forecasting and early warning (Diamanti et al. 2019; Qu et al. 2017), disease diagnosis and early warning (Govindaraju et al. 2019), abnormal behaviour detection and analysis, market analysis and mining have all been used to create solutions (Purcell et al. 2018; Freitas et al. 2019).

3.9

Integration of Systems

System integration technology is required for smart aquaculture. Aquaculture equipment and subsystems are linked together to produce a coherent, intelligent system. Intelligent aquaculture systems are designed to supply farmers with a full, integrated solution that also ensures that the system’s overall performance is optimal, technically sophisticated and implementable and adaptable. Equipment and application system integration are part of intelligent aquaculture system integration. There are a variety of aquaculture equipment systems, including oxygen enrichment equipment, sensor-based feeding systems and water treatment systems. All of them require the same communication interfaces, transmission modes as well as voltages. It is thus necessary to establish an all-encompassing set of parameters that can be applied to all aquatic system components, as well as an IoT platform that can monitor and regulate these components. In order to get the most out of the equipment, the layout should be optimised as well. For example, the intelligent aquaculture ground includes a water quality monitoring system, a data intelligence processing system

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and a fish pest knowledge base that are all integrated. In order to resolve inter-system communication challenges, each subsystem must be integrated with each other. Application system integration can be aided by cloud computing, edge computing, and other approaches. Finally, intelligent aquaculture system integration is based on user needs, the design of intelligent aquatic equipment and technologies, and the employment of auxiliary technologies to handle different system building issues. Increased system stability, data processing speed, and production intelligence are the primary research goals in intelligent aquaculture system integration. The present focus of intelligent aquaculture systems is on 5G and cloud computing, although long-term robust equipment and trustworthy intelligent algorithms are still being investigated.

3.10

Mode of Smart Data Processing

Aquaculture relies heavily on individual fish data monitoring and forecasting. Smart data processing models can be used to do this. Fishery identification has been addressed fully in recent advancements with satisfactory general solutions, including fish mass estimation (counting, measurement of size and quality evaluation), as well as behavioural monitoring. It is more difficult to use IT in aquaculture since the inspected subjects are sensitive, agitated, and able to move freely, and the environment is not always regulated in terms of illumination, visibility, or stability. To be effective, the gear must be both inexpensive and water-resistant. However, commercial applications are not yet extensively utilised or achieving the expected outcomes since these characteristics complicate model creation and make the process significantly more complex than other elements of animal husbandry. Intelligent information processing models have evolved, but they are not quite ready to be put to use in the aquaculture industry just yet. There are various limitations with intelligent models, including the inability to explain biological reasons underlying observed patterns and substantial mistakes in projected findings for data beyond the model’s range. It is possible that the technique might be used in aquaculture to improve product quality and production efficiency. For the most part, investigations using intelligent models in aquaculture have been undertaken in a relatively simple context with limited interfering factors and are still in the experimental stage. Nonlinear calibration models, data mining and information technology integration, support vector machines and memory-based learning, artificial neural networks, and deep learning, need to be studied further to improve the aforementioned technologies for commercialisation and adoption by industry sectors.

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Rearing System Innovation Need Coupling with AI

When it comes to aquaculture in India, it is all about keeping fish in ponds and tanks full of water. Percolation, evapo-transpiration, and waste thrown away during and after culture all contribute to a large portion of the water and nutrients needed in these systems being wasted. However, given the declining supply of water from a variety of sources, this cannot continue for the ongoing expansion of aquaculture. Since water conservation is an issue, farming must be done in a way that either uses little water or recycles it for the sake of water quality in culture tanks and before it is finally discharged. In order to provide these provisions, the following systems have been developed: However, each have their advantages and disadvantages. Using an aquaculture recirculating system (RAS), tank water may be recycled indefinitely and continuously via a pipeline. Using mechanical, bio, and UV filters, the system eliminates suspended particles and excessive nutrient levels from the fish tanks and maintains water quality that is safe for the fish to eat. When compared to a high-throughput system, this one only needs to change 10% of the water every day, allowing for greater water savings. Fisheries may be kept at an extremely high density thanks to regular monitoring of the water. Despite the fact that both outdoor and indoor recirculation systems are available, the indoor system is preferred because of its better and more convenient water treatment capabilities. This system’s isolation from the external environment means that all of the water’s properties may be managed, including temperature, pH, salinity, free carbon dioxide, dissolved oxygen, ammonia, nitrite, and nitrate, as well as disinfection from pathogens. This is a huge benefit. The treatment of organic waste before it is released into the environment is also made possible by this technique. One disadvantage is that it uses a lot of energy and is dependent on complicated technologies. There are a number of high-value fish and shellfish species that can be grown using this approach, Since RAS has become widely used for intensive salmon and trout production in several nations in recent years, it has been procured for 2–4 times more productivity and more efficient fish pond systems than previously used. RAS has lately attracted Indian entrepreneurs and a few such units have been energised in various areas of the nation where pilot-scale production of striped catfish, GIFT tilapia, singi (Heteropneustes fossilis), and magur (Clarias magur) has been commenced with excellent results. In India, pilot-scale experiments have shown that the technique is appropriate for seed production and grow-outs of high-value flora. For high-value fish, however, the main issues are its high price and a lack of seed supply. Cobia, pompano, grouper, and snappers are very susceptible to diseases when grown in open tanks and cages, and the technique has been proven to be highly favourable for the development of brood stock of marine species such as these. Similar to biofloc and aquaponics, new fields of aquaculture production are biofloc and aquaponics, respectively. It is a farming technology that combines aquaculture and hydroponics, making use of no soil at all. The water from an aquaculture unit containing nutrients is fed to a hydroponics unit where the by-products from the aquaculture are broken down by nitrification bacteria into nitrites and nitrates, which

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are utilised by the plants as nutrients, and the water is then recirculated back to the aquaculture system. When hazardous chemicals like ammonia released by fish and shellfish are transformed into proteinaceous feed, known as biofloc, it is an innovative and cost-effective method (BFT). Artificial intelligence and smart aquaculture concepts may be used in conjunction with these rearing techniques to dramatically boost yield.

3.11.1 Aquaponic System The system allows for a 90% reduction in water consumption while consuming very little energy. On the other hand, getting recycled water and nutrients for the crop results in significant cost savings. People are stepping forward to adopt this technology as the demand for organic food grows. Several countries, including the USA, China, and Australia, have shown a strong interest in the technology in recent years, and hundreds of such units have been built around the world. China has also advanced this technology by developing floating wetland units with a surface area of about 4 acres in one of the most polluted and third largest lakes in the world, “Taihu”, which had a severe problem with algal blooms due to eutrophication of the water. It is now possible to culture fish and grow a commercial crop of rice on floating platforms in a large body of water using this system. Aquaponics is a relatively new concept in India, but it is gaining traction in some areas. With the financial support of Pallipuram Service Co-operative Bank, MPEDA has taken the lead in developing aquaponics in Kerala’s village Pallipuram, where over 200 farmers have successfully adopted the technology in a cluster area. The system enables integrated farming of a large number of vegetables and flowers, as well as lower-cost fish production and water quality improvement. Thus, if we were able to couple this technique with AI may suffice the purpose of sustainable aquaculture.

3.11.2 Biofloc Technology (BFT) The BFT is a culture system when exposed to sunlight, the wasted feed and excreta are converted into natural food by bacteria, algae, fungus, invertebrates, and detritus, among other microorganisms. This protein-rich live feed is known as BFT as shown in Fig. 3.4. Fluctuations in the floc size range from 50 to 200 μm In order to maintain a desirable ammonia concentration level, just a little quantity of water needs to be changed, which is why the BFT system has been referred to as a “zero water exchange system”. Biofloc contains a dry weight protein content of 25–50% and a fat content of 0.5–15%. High in vitamins and minerals, particularly phosphorus. It has a probiotic impact as well. To substitute fishmeal or soybeans, dry biofloc is being considered. An important part of this method is encouraging heterotrophic microbial growth, which metabolises nitrogenous waste and provides nutrients for

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the cultured organisms to utilise as food. While treating wastewater, BFT also supplies food for aquatic life. Molasses is used to keep the greater C:N ratio and to produce high-quality single-cell microbial protein, which improves water quality. These circumstances encourage the growth of dense bacteria, which serve as a bioreactor and as a source of protein food. Due to their increased growth rate and synthesis of microbial products per unit substrate, heterotrophic nitrogen-fixing bacteria have a significant advantage over autotrophic nitrogen-fixing bacteria. Since its introduction to India’s aquaculture industry in the 1990s, BFT has proven effective in the production of several species of fish and shrimp, including the popular GIFT tilapia, the striped catfish, the singi and the striped murrel. Shrimp grown in the biofloc technology showed a higher breeding success rate than shrimp reared in traditional methods. The growth of the larvae was also improved.

3.11.3 Recirculatory Aquaculture System In order to increase fish output in India, diversification of horizontal and vertical aquaculture, species diversification, scientific farming, high-quality feed and seed, and effective disease management are among the techniques. To raise carp, a variety of techniques can be used. These include the use of wastewater recycling, IAA, and various short-term procedures. In India, however, pond-based culture techniques remain dominant in freshwater aquaculture. It is possible to find floodplain wetlands and lakes as well as rivers and streams inside India’s inland waterways. Increased output can be achieved without the need for additional land-based fish farms when fish are cultured in open water bodies such as tanks and pens. It is also a big problem for cage farming to receive enough high-quality seed in the proper amount and on schedule. A low cost recirculatory system developed by ICAR-Central Inland Fisheries Research Institute. Only Pangasius hypophthalmus (Pangas) is currently being grown in freshwater cages commercially in India due to its rapid growth, well-developed culture systems that allow for omnivorous feeding habits, high acceptability of artificial diets, high stocking densities, improved disease resistance capacity, and tolerance of a wide range of environmental parameters. Pabda survival was previously studied by several authors using various feeds and stages of culture, and the findings of our study exceeded those of other studies by ICAR-CIFRI in a trial of Ompok bimculatus rearing. Previously, survival rates ranged from 5% to 90%, but in this research, the highest ever reported, it was 96%.

3.11.4 Algal Aquaculture It is called “algaquaculture” when algae are produced in conjunction with fish husbandry. For the most part, it is an aquaculture and algae farming system in one. Using an algal culture, the fish farming system’s effluent is bioremediated. This

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method substitutes algae for green crops. When it comes to aquaponics setups, it is similar to this one. For aquaponics, the most crucial advantage is that algae cultivation can utilise all nitrogen forms (including total ammonia nitrogen, or TAN) while still maintaining appropriate levels of ammonia, pH, and dissolved oxygen (Addy et al. 2017). It is possible to replace fishmeal in fish diets with micro and macroalgae (seaweeds and phytoplankton), which are generally referred to as “plants”. It is important to keep in mind that algae make up the foundation of aquatic food chains that give fish with the nutrients they require. Biochemical diversity among algae can be far larger than that of terrestrial plants, even if “blue-green algae”, or cyanobacteria, are eliminated from the equation. This demonstrates the early divergence of algae species in the history of Earth’s existence. The green algae family of algae created a lineage from which all terrestrial plants descended. Therefore, it is impossible to generalise about the nutritional value of alga because of the particular properties of every algae. Debbarma et al. (2022) also fed microbial waste to Ompok bimaculatus and reported a better growth performance of species in treated groups as compared with control group. This includes aquaculture, algal cultivation (which includes algal harvesting), and aeration. This effluent is pumped over the algae production unit, where fish waste (ammonia) is released into it. Algae eat ammonia and turn it into high-protein, polyunsaturated-fatty acid (PUFA), and other mineralrich compounds. For this reason, the bio-aerator algae consume carbon dioxide from aquaculture effluent as well, which will also release oxygen into the water, making the water more oxygenated. Other suggestions for improving water quality included the addition of algae-bacteriaceous consortia. In the end, it was recirculated in the aquaculture unit.

3.12

Challenges in Smart Aquaculture

3.12.1 Lack of Information Exchange There is a lack of information on aquaculture being disseminated. Progress in data gathering technologies and industrial scale is limiting the amount of aquaculture big data that can be made available. Because of the wide variety of aquatic species and the ever-increasing complexity of their habitat, it is challenging to collect data. The laboratory is still the primary location for the majority of current scientific investigation. Big data in aquaculture is hindered by the difficulty of capturing video images in natural (often murky) conditions, such as during sickness and aberrant fish behaviour.

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3.12.2 An Analytical Model and Technologies That Is Not Up To Date There aren’t enough sophisticated models and tools for aquaculture analysis. The sources of aquaculture big data have been considerably enhanced by the growth of Internet and IoT technologies, creating the foundation for aquaculture big data. Research into aquaculture big data, on the other hand, requires further advancements in intelligence.

3.12.3 Correlation Analysis Have Not Been Performed Data correlation analysis is inadequate throughout the aquaculture value chain. Although big data technology in aquaculture may be applied in a wide range of ways, it is difficult to integrate the whole data chain in correlation analysis, because of the discrepancies in volume and quality resulting from varying application depths. A lack of interoperability between aquaculture data and data from other industries makes it difficult to examine the implicit linkages, such as quality traceability, that cannot be integrated throughout an entire industrial chain in aquaculture.

3.12.4 High Investment Although AI is more beneficial, it has drawbacks. Because AI is significantly more expensive, many people are unable to afford it. The upkeep of an AI system is also costly. Another significant disadvantage of AI is that it causes workers to lose their jobs. Farmers may gain, but fishermen will lose.

3.12.5 Lack of System Integration Still a long way to go before aquaculture can catch up to more conventional food production methods. The use of big data, robots, the Internet of Things, and simulation software is becoming increasingly common in production. The cornerstone of intelligent aquaculture is an artificial intelligence technology platform that combines data from diverse sources. Advances in technology might be characterised by terms like “digital,” “industrial,” “mechanised,” or “big data.” As a result, many “Smart Aquaculture” choices have been taken purely on the basis of past experience. Because of this long-term cognitive growth. All parts of aquaculture, including breeding technology and intelligent technology, must be integrated into traditional aquaculture. Additionally, aquaculture production and management may be totally

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automated because of advances in computer technology. In addition, the long-term viability of a country is strongly influenced by policy and organisation. Scientific research systems and innovation mechanisms are critical to the growth of smart aquaculture, but without change, policy and organisation will be a major impediment.

3.12.6 Complexity of the Culture System In aquaculture big data, the rising complexity of intelligent fish farms and the expanding diversity of aquatic creatures provide challenges to data collection. Many studies are conducted in a laboratory environment. It has always been difficult in aquaculture to collect accurate data using traditional video image capture technologies to watch for the appearance of fish diseases and abnormal fish behaviour in natural environments. Big data analysis for aquaculture is lagging behind the market’s expectations due to a misunderstanding of aquaculture’s specific characteristics while using big data technologies. Increasing the breadth and depth of aquaculture intelligence applications such as deep learning, knowledge computing, swarm intelligence, hybrid-augmented intelligence, and other emerging intelligence technologies are also necessary for improving aquaculture intelligence. Aside from these shortcomings, the current study on aquaculture big data is limited in scope and does not address the whole aquaculture industry.

3.12.7 Lack of Adoption of Advance Technology/Techniques Deep learning is the defining feature of AI in intelligent fish farming. By combining deep learning with agricultural technology, it is possible to extract more useful information from photos and structured data than classical machine learning can provide. The following issues have been discovered in the use of deep learning to aquaculture: Cameras or sensor equipment must be built to capture data in a variety of contexts for deep learning model training, verification, and testing. Complexity in the design of deep learning algorithms is exacerbated by underwater imaging systems’ ambiguity and instability. Most aquaculture issues based on deep learning require labelled sample data, therefore this is an important consideration. Handmarking the target category by more specialists is usually necessary. It should be noted, however, that while deep learning is excellent at learning the properties of training datasets, it cannot be extended beyond that dataset’s capacity to express itself.

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3.12.8 Lack of Solid Decision Support System Intelligent fish farms may be categorised into three levels based on how far they have progressed in information technology. Most of the labour in fish farms is done by aquaculture professionals who run and control them from a distance using their knowledge and expertise. It is possible for the Internet of Things (IoT) to run autonomously at the intermediate stage, which is referred to as the “unattended fish farm”, because the equipment in the monitoring room does not need to be operated remotely 24 h a day. At this point, the fish farm may be produced entirely without the input of any human beings at all. Using a cloud-based management platform, all operations and management activities are planned and chosen freely, and robots and intelligent equipment run independently as well. This is a fish farm with no workers at all. Intelligent digital technology may be used by the intelligent fish farm to address the issues of aquaculture labour shortages and water pollution as well as high risk and low efficiency.

3.13

Way Forward

1. The benefits of AI in the future are undeniable, but full automation of this process has yet to be achieved. In the development of an untethered automation device for automated devices, scientists are working on a technology that can function without human assistance. 95% of the time, the accuracy is close to perfect. If AI is used correctly, it can result in an increase in aquaculture production. Fisheries and aquaculture appear to be the most reliant of all industries on future technological, scientific, and social advancements, and there appears to be no way around it. 2. Reduce, recycle, and optimise resource utilisation are all possible outcomes of smart aquaculture. In other words, it can cut feed consumption while also enhancing waste and water quality control through big data analysis and realtime modifications. Renewable energy facilities and enhanced innovation and interaction with other systems like aquaponics, BFT, or RAS are needed to produce ecological aquaculture that is green and sustainable. 3. Aquatic goods may be considerably improved in quality, safety, and productivity with the use of smart aquaculture techniques. As an example, smart monitoring and management can keep an eye on the health of the environment and fish. Faster growth and better quality are possible outcomes. Although labour costs can be reduced, there are trade-offs such as greater capital and energy expenses, therefore more study and economic analysis will be needed throughout to find ways for intelligent aquaculture to be financially feasible. As a result, more robust models are required to establish a completely autonomous operation system, especially in aquaculture, where a sensor or other component failure might have disastrous results, including crop loss.

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4. Climate and aquaculture environmental information management can help boost aquaculture production while reducing losses. This is a win-win situation for both the preservation of natural resources and the increasing demand for seafood. 5. Worker productivity can be improved while labour expenditures are reduced thanks to automation and intelligent equipment. In addition, intelligent aquaculture may promote economic growth by supporting smart industry and workforce transformation in response to the demand for technological skills.

3.14

Conclusion

In many nations, such as Malaysia, internet and digital technology has made great developments. This progress can aid the aquaculture business. It provides as an example of how aquaculture may be used to improve seafood production systems that are becoming increasingly significant. Water quality can be continually monitored with a payload of numerous sensors thanks to advances in instrumentation technology over the last few decades, such as those supplied by YSI. Accurate digital and real-time monitoring of aquaculture water quality (temperature, salinity, pH, dissolved oxygen) in a local or remote manner may be achieved by aligning sensors to a wireless communication system and building an integrated sensor network-wireless platform A different hardware design and operating programme are required for the corrective action, which is handled by a neural arc coupled to a robotic facility. Additionally, this sensor-digital combo will make it easier for farms to share information via common devices and applications, allowing for more data to be analysed via cloud computing from more sources. The integration of artificial intelligence (AI) with robots is seen as the logical next step. Artificial Intelligence (AI) is the most intriguing and significant topic of robotics. Data and facts are gathered by sensors or human input while using an AI system placed in a computer. When a computer programme is written, it runs through the potential actions and determines which one is best for the situation, using the obtained data. There is no doubt in my mind that a computer will solve the issues that it has been programmed to address. It is not possible for a computer to perform analytic functions on its own. A mechanical device (such as a robot) can be programmed to execute certain functions and control its activities through the use of software commands. An aquaculture-ready robot’s information flow must be efficient. When one variable (such as dissolved oxygen) changes, other variables (such as water dissolved gases or fish survival) should also change, and the robot should respond mechanically to ANN to find a solution to the problem. This is the foundation of programming. Android-based smartphones, tablet PCs, and desktop computers may all be used to control the system. Aquatic farming as a primary or secondary food production system will benefit from the ubiquitous availability of mobile phones in all segments of society, including rural regions, thanks to this handy instrument. In the aquaculture process, numerous elements come into play. Take, for instance, dissolved gas concentrations, pH levels, stocking densities, and food intake as just a few. The

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functions that robots software programmes will play would be complex. In other positions, the robot may not be able to address the problem except to tell the hatchery operators to act despite the information it gets. Consider the issue of hatchery tank fish stocking density. Using just a camera and a computer’s image detector, a single species and how many of them survived a treatment may be determined using artificial intelligence. It is possible to remove dead fish specimens and introduce fresh specimens to the hatchery by human workers at this point of understanding. In the current century, as we move away from coastal aquaculture to the deep sea, where sea conditions are rough and extended human presence is neither economical nor practical for operations such as feeding the fish or regular daily monitoring, AI and robotics will increasingly find application in aquaculture. Another key activity that is ideally suited to fish farming is biofouling control. A lack of oxygen-rich water enters a marine cage due to biofouling, which is well-known in the industry. In addition to decreasing growth, this also raises the risk of sickness and increases the likelihood of fish death. In addition to being heavier, the cage’s lifetime is also shortened. Keeping the cage free of biofouling necessitates a significant amount of manual labour. Robots are capable of cleaning the nets. For sustainable fishing in the face of climate change, there is a pressing need to implement a climate wise approach that includes adaptation and mitigation strategies. Furthermore, the climate smart method has a low level of public awareness. The ability of farmers to provide for themselves and their families in the long term may be improved if more people in the fishing industry become aware of climate savvy practices. Solid infrastructure and generous government policy support will be necessary in the future for intelligent fish farm development to move forward quickly. Potential aquaculture enterprises must first be pushed toward upgrading their operations with the introduction of advanced agricultural technology and high-tech talent, as well as the regulation of aquaculture sustainability indicators, to ensure the healthy and green operation of intelligent fish farms. Acknowledgement Authors duly acknowledge Dr. P. Barik, Assistant Professor, College of Fisheries, Kawardha, Chhattisgarh for his inputs for the article.

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Cruver P (2015) Offshore aquaculture and marine big data™. Environ Coast Offshore 3(6):38–45 Debbarma R, Meena DK, Biswas P, Meitei MM, Singh SK (2022) Portioning of microbial waste into fish nutrition via frugal biofloc production: a sustainable paradigm for greening of environment. J Clean Prod 334:130246 Diamanti S, Domenikiotis C, Neofitou N, Panagiota P (2019) Preliminary study for quantitative determination of ammonium in aquaculture environment by the use of sentinel 2 satellite data. In: 14th Conference of the Hellenic Hydrotechnical Association (H.H.A), Volos, Greece, pp 1–9 Figueroa R, Araneda M, Correa K, Lhorente JP, Manuel Yáñez J (2018) GenDataSave: information management platform for aquaculture genetic improvement programs optimized by means of parallel computing. In: Proceedings of 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France Fore M, Frank K, Norton T, Svendsen E, Alfredsen JA, Dempster T, Eguiraun H, Watson W, Stahl A, Sunde LM, Schellewald C, Skoien KR, Alver M, Berckmans D (2018) Precision fish farming: a new framework to improve production in aquaculture. Biosyst Eng 173:176–193 Freitas J, Vaz-Pires P, Cmara JS (2019) From aquaculture production to consumption: freshness, safety, traceability and authentication, the four pillars of quality. Aquaculture 518:734857 Govindaraju K, Itroutwar PD, Veeramani V, Kumar TA (2019) Application of nanotechnology in diagnosis and disease management of white spot syndrome virus (WSSV) in aquaculture. J Clust Sci 30:1–9 Hamdan R, Othman A, Kari F (2015) Climate change effects on aquaculture production performance in Malaysia: an environmental performance analysis. Int J Bus Soc 16:364–385 Huang XF, Wu W (2016) Dynamics system analysis and intelligent identification of aquaculture water quality data. Int J Database Theory Appl 9:157–168 ICAR (2022). https://icar.org.in/content/successful-demonstration-pen-culture-climate-resilienttechnology-clam-fishers-kerala Jennifer KP (2017) How sensors, robotics and artificial intelligence will transform agriculture. Forbes Mag. https://www.forbes.com/sites/jenniferhicks/2017/03/19/how-sensors-roboticsand-artificial-intelligence-will-transform-agriculture/#2877b2df384b Jenssen PI (2019) Artificial intelligence is shaping the future of aquaculture. Aquacult Blog. https:// blog.akvagroup.com/artificial-intelligence-is-shaping-the-future-of-aquaculture Kandu P (2017) Papua New Guinea. Impacts of climate variations on local fisheries and aquaculture resources in PNG. In: Ramos EJ (ed) Ecological risk assessment of impacts of climate change on fisheries and aquaculture resources. APEC Ocean and Fisheries Working Group, Peru, pp 45–49 Li D, Li C (2020) Intelligent aquaculture. J World Aquacult Soc 51(4):808–814. https://doi.org/10. 1111/jwas.12736 Ma Y, Ding W (2018) Design of intelligent monitoring system for aquaculture water dissolved oxygen. In: IEEE Advanced Information Technology, Electronic & Automation Control Conference. IEEE, Washington, DC Martin H (2019) Aquaculture 4.0: applying industry strategy to fisheries management. Marit Fish News. https://www.governmenteuropa.eu/aquaculture-4-0/93038/ Mugwanya M, Dawood MAO, Kimera F, Sewilam H (2021) Biofloc systems for sustainable production of economically important aquatic species: a review. Sustainability 13(13):7255. https://doi.org/10.3390/su13137255 Myers SS, Smith MR, Guth S, Golden CD, Vaitla B, Mueller ND (2017) Climate change and global food systems: potential impacts on food security and undernutrition. Annu Rev Public Health 38:259–277 Purcell SW, Williamson DH, Ngaluafe P (2018) Chinese market prices of beche-de-mer: implications for fisheries and aquaculture. Mar Policy 91:58–65 Qu JH, Sun DW, Pu H (2017) Vis/NIR chemical imaging technique for predicting sodium humate contents in aquaculture environment. Water Air Soil Pollut 228(5):177.1–177.10

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Chapter 4

Smart and Automatic Milking Systems: Benefits and Prospects Suvarna Bhoj, Ayon Tarafdar, Mukesh Singh, and G. K. Gaur

Abstract Manual milk harvesting in a farm is labor-intensive and is one of the major reasons for the long working hours which takes about 25–35% of the annual labor demand. Milking activities add substantially to the costs of the farm enterprise. Over time, dairy industries have incorporated new technologies with a motto of enhancing efficacy resulting in the implementation of automatic milking systems (AMSs) to reduce associated labor. AMS offers a future alternative to allow higher milking frequencies and monitoring of each individual cow based on its productivity level to bring flexibility in the daily farm routine. Biosensor-enabled AMS technology combined with a data management system formulates a better farm management program. Pasture-based mobile AMS creates a new spectrum of challenges, different from those of indoor-based feeding systems. Integrated AMS is the most extensively acknowledged configuration and involves a defined protocol from cow entry to exit. The industrial robotic arm is a proven technology that can aid two cows at a time when set up side by side while the automatic rotary milking system generally possesses five robots, which impressively progresses the efficacy of the procedure. The automatic teat-cleaning and milking cup attachment process has the potential to affect milk variables and udder health along with a positive effect on animal welfare. These set examples display AMS as a promising technology if used effectively, in the times to come. Keywords Robotic milking · Automated milking system · Biosensors · Mobile AMS · Milking frequency · Milk quality

S. Bhoj · A. Tarafdar · M. Singh · G. K. Gaur (*) Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_4

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Introduction

Dairy farming includes cow milking, barn cleaning, and sanitation, maintaining herd health, continuous checks on hygienic milk production, and improved milk quality while focusing on the main motto i.e. to gain a respectable consequent turnover. The activities on a dairy farm are relatively complex and time-consuming and generally handled by the farm family. When labor is hired, it often adds to the production cost and on certain occasions, it becomes difficult to even hire labor for a longer duration due to their ever-increasing demand in other lucrative sectors with greater pay and benefits. Automatic milking systems (AMS) offer a solution to this long-existing problem to relieve the farmer from this labor-intensive farm routine. Historically, all the cows in the ancient farming system were milked by hand. A survey of this practice showed that 40% of the work-wages workers suffered from health issues with reported back pain and 30% reported neck and shoulder injuries (Rossing and Hogewerf 1997). However, it was strongly noticed in the early 70s that the prerequisites of the dairy industry were not being completely achieved by the concurrent conventional practices, and much investigation had to be carried out for advanced technical innovations to ease the hardships associated with dairy farming and assist in decision making. Robotic milking systems became the center point of the recent advances providing a greater push to strengthen the dairy farming business. Lately, AMS is believed to be crucially important for refining working environments, increasing time flexibility, and saving on expenditures on wages. AMS can thus be associated with lowered labor costs, more quality time, and better herd management practices, among other advantages (Carolan 2020). The incorporation of modern techniques in dairy farming was recognized during the industrial revolution. Early illustrations of milking machines were seen in the mid-nineteenth century. Over time, AMS has become a modern-day digital technology in the contemporary dairy industry and is gradually turning into conventional practice. It exemplifies a unique and most generous scientific innovation in the dairy enterprise due to its competence to lower human involvement in several phases of milk production. Besides mechanization of milking procedures, AMS also regulates the entry and exit of cows in the milking box with none of the manual labor required. This not only cuts the cost of labor but also results in the improvement in milk frequency and milk yield, enhanced quality of milk, udder health, and marked animal welfare. The first commercial AMS in dairy farms was introduced in the Netherlands in early 1992, and by 2020, the installation of 50,000 units across the globe mainly in Europe (90%), Canada (9%), and other countries (1%) has been reported by AMS manufacturers’ estimate. It is expected that by 2025, 50% of dairy cows in North-Western Europe will be equipped with AMS. In the USA, DeLaval, Lely, and GEA are the largest firms, but AMS-Galaxy and Boumatic Robotics are also operating in the market. In Europe, AMS is manufactured by SAC (Sacmilking) and Fullwood Packo.

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Automatic Milking System (AMS)

AMS refers to the mechanization of all phases of the milking operations and farm management which are commenced manually in a non-automated traditional milking system (de Koning et al. 2004). In general, the AMS includes a milking machine, a teat position sensor (commonly a laser sensor), a robotic arm for automated teat cup application and removal, and a gate system for limiting cow traffic. A cow ID sensor scans the identification tag (transponder) of the cow and passes it to the control system to regulate the entry of cows (Maculan and Lopes 2016). After sensory identification of milch animals, AMS tracks the last milking time and if the cow is supposed to be milked, it is given entry into the AMS box, there the cow is fed with good quality concentrate as an incentive, and the robotic arm initiates its working with the following activities: (a) udder and teats detection; (b) teat washing with water, air jets and rollers; (c) dipping of teats to disinfect; (d) teat cup attachment; (e) detachment of teat cup when lesser milk flow decreases to avoid chances of over-milking; and (f) disinfectant spraying as post-dip. It is not necessary that the same protocol is followed by all AMS models and not all models are set with the pre-dipping step. On the other hand, it is possible that they may be installed with only the teat-cleaning step. It should also be noted that individual teat cup removal after the reduction in milk flow is not a universal step in all AMS models and full milk flow as a criterion to remove all four teat cups, is preferred by only a few AMS models similar to CMP. The basic device of the AMS is the robotic manipulator in the milking unit (Fig. 4.1). The science behind the working of the milking system is the pulsation mechanism which releases milk from the teats at regular intervals or pulses while massaging the teats to avoid teat injury (Fig. 4.2). A robotic arm for attachment and detachment of the teat cups to the udder, automatically, devoid of human involvement is provided which results in a 20–30% reduction of long working hours in a

Fig. 4.1 Representation of the operation of an AMS. (Adapted from Hogenboom et al. 2019)

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Expansion phase Teat

Stainless steel teat cup Rubber inflation

Vacuum off (air in)

Vacuum on (air out)

To milk flow

To milk flow

Fig. 4.2 Science behind the automated milking system

dairy farm (Kragten 2014). It includes a single stall system with built-in robotic functions and milking programs and a multi-stall system with a transportable robot, pooled with milking and detachment tools at every stall. Single stall structures are capable of milking 55–65 cows numerous times a day, while multi-stall structures are facilitated with 2–4 stalls that can milk 80–150 cows thrice a day.

4.2.1

AMS Operations

To have a better understanding of AMS, it must be analyzed in a different way than conventional milking methods. The underlying principle behind AMS is to let cows be milked voluntarily with no human intervention, at any stage of the milking process. Therefore, the animals must enter the milking box without being forced. Animal herding patterns were also introduced to improve the efficiency of the system, increase the number of visits to the milking area, and reduce the number of cows to be gathered into the AMS, as they do not go themselves voluntarily. A guided pattern was thus developed which consisted of two systems. The first one is the “feed first” system, which is based on independent access of cows from the resting area to the feeding area. But to return to the resting area, cows must pass through a selection gate, from where animals that are allowed to be milked are sent to the waiting area. Cows that are not capable of being milked return to the resting area, while only those cows that have to be milked reach the AMS. Another relevant

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system is “milk first,” in which the cows must pass through a selection gate to access the feeding passage. From this area, cows allowed to be milked are directed to the AMS; thus, they access back to the resting area only after being milked.

4.2.2

Types of Animal-Flow

Milking permission is different for each individual cow as per the lactation stage and milk yield. Different animal-flow models are installed accordingly in the voluntary milking system. The important flow models have been discussed here.

4.2.2.1

Free-Flow Model

The simplest of all models is the free flow, in which the milch cows have open access to the milking area, resting space, and feeding alley except a gated entry at the AMS. Bach et al. (2009) in their study found that the AMS visits were less when cows were kept under a free-flow system than in a guided-flow system. The research team also observed that cows in this system were directed to milking 0.5 times a day, whereas cows kept in a guided-flow system were directed to milking only 0.1 times a day. Animal welfare and feed intake are estimated to be higher in the free-flow system, with the rise in dry matter intake (DMI) (Melin et al. 2007). A free-flow system provides more accurate feed formulation as per the animal requirement. Free-flow systems function with fewer cow lines, lesser waiting time, and a lesser interface between dominant and inferior cows (Thune 2000).

4.2.2.2

Guided-Flow Systems (Feed First and Milk First)

As discussed earlier, there are two types of guided-flow systems: (a) feed-first system (b) milk-first system. In the feed-first system, the cows have access to the feed then they are forcefully made to enter the AMS box via selection gates and drivers so they could return to the resting area. While, in the milk-first system, the cows first pass through the milking area following which they access the feeding alley, and later return to the resting area. In the guided-flow systems, there is a decreased DMI and lower feed intake time (Bach et al. 2009; Melin et al. 2007). Furthermore, in this system, the variations in feeding behavior cause ruminal acidosis because of a significant reduction in feed intake time due to which the cows tend to eat more in the feeding alley, where feed is available as an incentive to milking. Rumen acidosis leads to reductions in milk fat while decreased DMI causes lower protein levels (Bach et al. 2009). Long waiting time taken to enter the AMS in the guided-flow area is associated with serious hoof and welfare issues (Thune 2000) while a major benefit of this system is the reduction

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in fetch cows, i.e. more and more cows readily adapt to AMS and are self-motivated to enter the milking area voluntarily (Bach et al. 2009).

4.3

Types of AMS

Automatic milking is preferably known as robotic milking. Primarily, there are three principal robotic milking machines that are developed for refined working conditions, enhanced time flexibility, and savings on labor expenses: integrated AMS; industrial robotic AMS; and the automatic milking rotary system. The details of these systems have been described in this section.

4.3.1

Integrated AMS

Integrated AMS is a highly accepted computerized setup and several manufacturers supply machinery under this type (Fig. 4.3). In this type of configuration, there is a milking area that receives the milking cows while the robotic arm is attuned to a spot closer to the milking space. When initiated, the arm moves outward to locate and attach the teats (Kragten 2014). This AMS operation involves various steps from cattle entry to exit: • Cow entry: The AMS contains a gate for cow’s entry which checks its entry to the milking box. When the animal moves voluntarily to the AMS for getting milked, the entrance gate will trace the identification details of the cow and allow it to come inside. • Feed and teat preparation: After access, the cow is fed with a good quality concentrate cow to compose the animal during the milking process. Animal’s ID tag and tracking information of particular teat locations for attachment of teat cup,

Fig. 4.3 Integrated AMS available at DeLaval (left) and Lely (right). (Courtesy: High Contrast and Anoek 2012)

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gets automatically registered at the entrance gate. The robotic arm traces each teat using laser, ultrasonography, or image analysis (Artmann 1997) to recognize the precise point of attachment. Later, the teats are cleaned and dried with the help of special teat cups. Other cleaning procedures like cleaning using brushes are also practiced by integrated AMS. • Attachment of milking cup and auto-cluster removal: The robotic arm clutches one or more than one teat cup in a single turn and fixes them to the teats taking about 12 s to complete (Hogeveen and Ouweltjes 2003). Milking is initiated almost immediately after the milking cups are on the teats, and it is regularly checked for the rate of milk flow, yield, and conductivity, etc. per teat with the help of biosensors tagged. In case the flow of milk is disrupted, the cup is removed and again fixed back to the same position. • Teat spray and cow exit: After milking, teat disinfection is executed by the robotic arm via the spray method. Then the exit gate is opened to allow the animal to go out. Amidst the last exit and the successive entrance, thorough rinsing of teat cups is carried out with water for sanitization.

4.3.2

Industrial Robot AMS

Industrial robot AMS is installed with a specially designed industrial robotic arm for attaching milking cups, located in line with the milking box (Fig. 4.4). Its worldwide manufacturers are ProFlex, USA, Futureline MARK II from Denmark, and Galaxy Starline in the Netherlands with ProFlex as the leading manufacturer. The typical characteristic of this AMS is that once set up alongside as a double-box configuration, one robotic arm can attend to two cows which moderates the milk production cost considerably. After the milking arm is detached from the teats, the rest of the schedule is alike as in integrated AMS.

Fig. 4.4 Robotic arm with teat cup attachment in an AMS. (Courtesy: USDAgov)

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Automatic Milking Rotary System (AMR)

The workflow of AMR is based on the concept of rotary milking parlors. AMR (DeLaval, Sweden) was first conceptualized in 2010, which seems analogous to the DairyProQ (GAE, Germany) with respect to configuration and functioning (Fig. 4.5). A double bar gate checks the entry of not more than one cow at a single point of time on the platform, and then the platform moves to the next spot so that the next cow enters the platform. This system is generally well equipped with five robots, two engaged in teat preparation and two allotted for teat cup attachment, while one focused on spraying disinfectant after milking. Thus, four robots assist four cows at the same time, thus making the system appreciably proficient.

4.3.4

Mobile Automatic Milking Systems (MAMS)

Some farmers want their dairy herd to stay long on pastures (mainly from spring to autumn). They believe that this will not only give them access to fresh air and natural grasses, but it will also eliminate problems with manure disposal, with natural fertilization of land. The need of a movable milking robot was felt for cows grazing in large pastures in bigger herds on ranch systems. This allows the cows to spend

Fig. 4.5 Automatic milking rotary system. (Courtesy: Csand12 and Thomas Fries)

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Fig. 4.6 Representation of a pasture-based mobile AMS system

more time on pastures thus enhancing feed intake with the least manual handling (Gaworski and Kic 2017). Moreover, lease or share-farming like enterprises can come into existence without facing labor issues for the milking process (Neal 2014). All across the globe, AMS is working in free-stall or compost barns. However, AMS was first incorporated in pasture-based dairy systems in 2001 in Australia and New Zealand’s dairy farms (Lyons et al. 2013). Later, this advancement propagated to pasture-based farming in Australia (Wildridge et al. 2018; John et al. 2019), Ireland (Shortall et al. 2018a, b), and the USA (Nieman et al. 2015). Recent research in Belgium, Denmark, France, and the Netherlands has proved MAMS as a highly encouraging development for pasture-based dairy farming. AMS is always in a fixed position inside the milking parlor while MAMS follows the cows to the pasture (Fig. 4.6). The Danish system (Oudshoorn 2008) works with a transportable mechanism that enables it to be shifted from one location to another with minimal harm to the pastures (Lenssinck and Zevenbergen 2007). It is well fitted with the entire necessary gears like a bulk tank, vacuum pump, and cleaning apparatus, etc. so it can operate efficiently for a maximum of 2 days.

4.4

Sensors in AMS

AMSs are also facilitated with sensory tools assembled with data analyzing tools to supervise and regulate the entire event. It utilizes wearable sensors on the cow to record milking and feeding behavior allowing distant monitoring of cow health (Neethirajan 2017). The use of biometric sensors helps AMSs compile large amounts of data, which gets automatically stored in a database and later processed with suitable algorithms (Hogeveen and Ouweltjes 2003; Ouweltjes and de Koning 2004). The dairy owners, with the help of a management program, are capable of regulating the settings and situations for the milch cows. Detailed reports are received by the farmer on a screen or as printer messages which can be used further to take suitable action (Fukatsu and Nanseki 2011). Carbon electrodes serving as electrochemical sensors are generally used in the robotic milking machine. Radio frequency identification (RFID) tags embedded in animal bodies trace the milking management behavior.

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In addition to controlling the milking process, modern automated systems have in-line biosensors that investigate milk quality in numerous ways by estimating the composition of milk, Somatic cell counts (SCC), blood detection, progesterone levels, electric conductivity, etc. It manages to evaluate the energy and protein intakes of cows along with metabolic imbalances which are major health constraints in dairy cows and suggest better utilization of grasslands. These milk quality factors can be assessed precisely (milk fat, R2 ¼ 0.95; milk lactose, R2 ¼ 0.83; milk protein, R2 ¼ 0.72; Somatic cell count (SCC), R2 ¼ 0.68) by a near-infrared (NIR) spectroscopic sensing method or with biosensors or sensor displays or optical methods (Kawasaki et al. 2008). Automated cow drafting systems which employ the information assimilated from different sensors to individually isolate cows that demand particular attention can also be installed on farms (Wagner-Storch and Palmer 2002). In addition, sensors incorporated with in-parlor feeding set up offer individual cow exact quantities and types of concentrate as per the productivity parameters (Bach and Cabrera 2017). This system is generally coined as a feed-to-yield system. In pasture-based dairies, the milking parlor is generally equipped with personalized feeding systems. Such sensors assist the farmer in making correct decisions through smart data handling solutions. Special guidelines for automatic milking and the use of sensor technology within the framework of the International Standards Organization (ISO) have been developed and are being continuously updated.

4.5 4.5.1

Benefits of AMS Labor Savings

Most of the manual labor associated with dairy farming is involved in the milking process. As per the reports of Castro et al. (2015), milking thrice a day and its management by the farm workers takes more than 4 h a day. Installing AMS in a dairy farm can significantly reduce the need for manual labor which can be uninvolved or can be used elsewhere in the farm for a better economy. The world’s first AMS was established in the Netherlands in 1992 because of the significant rise in labor costs in the country (de Koning 2010). AMS enhances the competency of labor by auto-cluster removal, and self-managed entry and exit gates to regulate cow traffic. Rodenburg (2017) observed that automated milking decreases labor requirements on all dairy farms irrespective of size and offers a more flexible lifestyle for farm owners with up to 250 cows. Farm operations like teat cleaning, milking, and distinction of normal milk from abnormal milk are integrated into the robotic unit of AMS. The implementation of AMS technology brings about 20–50% labor savings which is the major reason for the increased rate of AMS adoption across the globe. In Europe and the USA, 18–46% labor saving has been recorded with the use of AMS (Rotz et al. 2003; Mathijs 2004; Bijl et al. 2007). Moreover, the lesser the time spent in the milking

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parlor, the more is the improvement in the quality of life of the wages-workers after the introduction of AMS. Households involved in the dairy business often face problems taking part in social activities outside the farms. AMS can alter this situation and help them to participate in other productive activities.

4.5.2

Increased Milk Yield and Frequency

AMS is said to have a positive attitude on milk yield with increased milking frequency as there is a higher comfort level in cows and lower stress on the udder. However, the attributes may vary, because of external influences like farming environment, animal health, and climate. The installation of milking robots is trailed by a considerable rise in the volume of production per farm. Bijl et al. (2007) recorded that Dutch dairies with AMS had an average of 74 cows per full-time employee while those with conventional milking parlors (CMPs) had an average of 59 cows per employee. Minnesota’s farm business management association observed that dairies in the upper Midwest averaged over 1 million kg of milk per full-time employee in a year due to automation while non-automated farms had only 700,000 kg of milk per worker (FINBIN 2016). The positive influence on milk yield may be because of more frequent milkings per day when using AMS (Melin et al. 2005). A new idea to fetch all cows into the AMS with the help of an automatic herding system (AHS) consisting of slow-mobile fences was introduced by Drach et al. (2017). It was seen in Israel when an AHS was used in a large commercial dairy enterprise, the functioning time of bringing the cows to the AMS was lowered by 80% in relation to the experimental set of cows. They also used this tool to raise milk frequency and milk yield. Hence, economic benefits are anticipated from this technique. Furthermore, Canadian research reflected that after AMS installation, dairy farms increased their herd size from 77 to 85 lactating cows, although this change seemed irrelevant for small dairy enterprises (Tse et al. 2017). In Europe also a hike of 5–10% in herds being automatically milked is registered (Bach et al. 2007; Bijl et al. 2007; de Koning and Rodenburg 2004). Lopes et al. (2014) reported an average increase of 14.75% in the milk yield after adequate management of AMS by allowing a higher frequency of visits to high-yielding animals. Salfer et al. (2017) also suggested that the AMS should be managed in a way that the right animal occupies the AMS at the right time so that the early lactating cows get greater access to AMS to be milked more frequently. Such an approach is important to bring an upsurge in milk yield in AMS. This intensified milking frequency appears to be more effective in the case of multiparous cows rather than heifers. The average number of milking in AMS is generally in the range of 2.5–3.0 per cow in a day, but considerable differences in milking intervals are recorded in large dairies (de Koning 2011). Few commercial farms find it hard to reach the desired milking frequency with AMS. In such scenarios, a forced-traffic system is implemented to compel cows to visit the milking box which has a negative influence on the resting time, feeding

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Table 4.1 Alterations in milking frequency, milk yield, and labor reduction post-AMS adoption in comparison to non-AMS Country/ region France

Drop-in labor services –

Milking frequency –

19.8–21.3%

–

Increase in milk yield 3% (2 years) –

Methodology 44 large farms

European countries The Netherland Denmark

29%

–

Nearly same

50%

>19%

Finland

30%

2.7 in summers, 2.4 in winters –

Iowa (US)

75%

2.9 times/day

12% higher

9 AMS and Non-AMS Varied from Con to AMS 8 dairy owners

Poland

–

2.5–3.0 times

>12%

50 cow farm

Poland

–

–

Higher

Canada

20%

3.0 times/cow

Higher

Poland

–

–

Higher

2 farms, 60 cows Phone survey, 530 AMS farms 16 herds, 3398 cows

Not higher

Interview schedule 62 farms

References Veysset et al. (2001) Mathijs (2004) Bijl et al. (2007) Oudshoorn et al. (2012) Heikkila et al. (2010) Bentley et al. (2013) Bogucki et al. (2014) Sitkowska et al. (2015) Tse et al. (2018) Kolenda et al. (2021)

time, or after-feeding behavior (welfare parameters). This can also cause a negative energy balance and increased risk of subclinical ketosis (Bogucki et al. 2014; de Marchi et al. 2017; Sitkowska et al. 2015). Endres and Salfer (2015) emphasized the usage of minimum employees and lesser fetch cows as much as possible to achieve maximum milk output in the system. The herd must consist of high yielders which stay in the AMS box for as little as possible, for larger volumes of milk flow in kg/min. Well-balanced economic feeding, improved genetic pool with high reproductive traits, decent udder conformation, proper AMS maintenance can extract maximum benefits with AMS in terms of productivity, profitable returns, and better decision making. While studying milk yield of buffalo in comparison to the AMS and conventional tandem milking parlor, Sannino et al. (2018) observed that that the AMS milked buffaloes had suggestively higher milk yield per day with increased persistency of lactation. This establishes the idea that mere installation of AMS does not guarantee higher milk yield, rather a well-managed and properly implemented AMS can lead to better gains. A country-wise summary of milk yield as affected by AMS has been shown in Table 4.1.

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Less Construction Work Requirement

The civil construction work in a robotic milking facility is very less as compared to a traditional milking parlor. AMS facilities are less complex; the cost involved in facilities can be significantly reduced both in terms of smaller built-up areas and lesser building complexity. AMS installation is therefore much more economical with respect to civil work involved (Carregosa and Almeida 2015). However, the design of AMS is quite important because it can affect a cow’s accessibility to AMS. Location site of gates and corridors in an AMS can affect cow traffic and behavior, thus time available for milking is also impacted. The significance of a good design and layout has been emphasized in earlier works (Lyons et al. 2013; Rodenburg 2017) and therefore must be taken seriously.

4.5.4

Greater Economic Viability

It is well-known that the introduction of AMS in a farm requires high initial investments but there are some factors that must be taken into account to evaluate the economic feasibility of AMS. Salfer et al. (2017) suggest that labor costs, rise in milk yield, milk flow management, investment cost, and lifespan of instruments are a few factors that should be studied to evaluate the profitability of AMS. It has also been emphasized that installation of AMS is more profitable and has a lower breakeven point than CMP (Hyde and Engel 2002), which is basically due to saving on wages and increased milk yield. Salfer et al. (2017) replicated a few cases to compare economic performances between farms practicing AMS and CMP with labor cost increases ranging from 1% to 3%. They found that with up to 240 lactating cows, AMS-equipped dairy farms were more capable to support variations in workforce wages than CMP.

4.5.5

Increased Feed Utilization

Another important gain in farms using AMS was reported by Salfer et al. (2017) where they found that providing pelleted feed to cows in the AMS box strongly influences animal’s body condition which in turn raises the milk productivity. This is attributed to individual herd management where every dairy animal is identified individually by the system. In this system, the quantity of concentrate supplied is based on individual nutritional demands calculated by the milk yield of the cow. The free-flow AMS manufacturing company, Lely, suggests a protocol to offer PMR (partial mixed ration) on the feed pad by which about 80% of the nutritional requirement is met. Rest 20% of the feed should be given in the AMS preferably as pelleted concentrates as an incentive to enter the AMS box (Data from the company

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Lely Industries N. V.). AMS manufacturing companies, DeLaval and Lely Industries collected actual data from client farms and reported a feed conversion ratio (FCR) of 1 kg concentrate feed for each 3.3 kg of milk produced in AMS farms while in conventional farms, where total mixed ration (TMR) diets are fed to cows (divided into standard production batches), the best FCR rates of 1 kg concentrate consumed per 2.5 kg of milk yield were achieved.

4.5.6

Quality Working Environment and Staff Health

Many dairy farmers or dairy workers faced health issues such as pain in knees, back, hips, and shoulders concern, etc. Such conditions pushed dairy owners to invest in AMS to improve the life quality of their workers. Salfer et al. (2017) stated that many dairy owners chose to invest in AMS keeping health in mind the health concern of their milkers and as a step to improve the working environment in the farm enterprise.

4.5.7

Information Management and Decision Making

The main features of AMS that make it a digital age machine are computer-based monitoring, individualized analysis, complete transparency, absolute online approach, and data processing of individual cows with exceptional recorded details. According to King et al. (2017a, b), AMS generates enormous data and vital information from routine farm operations. This data when processed with digital monitoring of behavior, rumination, and activity level systems create detailed reports of the farm status which assists the managers in taking decisions and defining strategies for smooth management of farm operations and profitable returns. Measurements of rumination, daily activity, and milk production can assist in the early detection of farm issues and give a strategic solution of the existing problems in a planned manner thus lowering economic losses. Such measurements are possible to be carried out only by AMS in collaboration with special digital monitoring systems. Auto-sensors can store useful information regarding daily operations, udder health, milking schedule, rumination time, and feeding pattern in the AMS (Jacobs and Siegford 2012a) while cartesian teat coordinated with AMS records udder conformations, facilitates research on genetic and phenotypic variation between parities (Poppe et al. 2019). The recorded data helps the farmers to take prompt action and make quick management-related decisions to minimize the impact of any farm crisis. The efficient use of such a digital age technique is highly associated with the learning skills of the dairy farmer and willingness to process and manage such a large amount of data (King et al. 2017a, b).

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Effect of AMS on Milk Quality

Milk quality is a subject of primary concern when it comes to milking via automated systems. Clean and hygienic milk production with a balanced nutritional composition including all the important factors such as somatic cell count (SCC), total bacterial count (TBC), fat content, free fatty acids (FFA), freezing point (FP), protein, lactose, casein, and urea levels should be in desired limits. Milk quality has been observed to be significantly lower in AMS introduced farms in the first 6 months after installation (van der Vorst et al. 2002). However, this concept has been highly debated. Pomies and Bony (2001) reported no substantial influence on the hygienic quality of milk, while Berglund et al. (2002) suggested that AMS milk quality was as good as manual milking and in a few cases was even higher. This section discusses the effect of AMS installation on different quality characters of milk. A country-wise summary of the effect of AMS on milk quality has been shown in Table 4.2.

4.6.1

Effect on Somatic Cell Count of Milk

SCC is a widely accepted indicator of udder health and milk quality. It is a major sign of the possibility of inflammatory reactions in the cow’s udder, mainly mastitis. Bulk milk having high SCC (usually >300,000 cells/mL) is not suited for cheese manufacturing and has detrimental effects on the quality and sensory characters of a finished product (Barbano et al. 2006). The influence of AMS on milk SCC is very variable; authors have reported an upsurge in SCC once the cows are subjected to robotic milking (Rasmussen et al. 2002), while few others observed no effect on SCC (Berglund et al. 2002; Mollenhorst et al. 2011). In a Dutch study, SCC was found to be low in cows milked in such a system (Ipema 1997). In another study, de Marchi et al. (2017) did not find any difference in somatic cell score for cows in automated and non-automated parlors, though they were found in greater number in milk received by AMS. The investment in AMS In the late nineties’ amplified the TBC and SCC in the bulk tank as compared to the situation prior AMS introduction. A trial in Europe concluded that both SCC and TBC reduced gradually with time and experience. However, an increase in washing frequency of the milking system to twice or thrice a day lowers TBC in the bulk milk samples (van der Vorst et al. 2002). An increase in milking frequency results in higher chances of bacterial invasion as the teat sphincters remain open after every milking, exposing quarters to the outside bacteria. Irregular milking interval is also considered as a contributing factor for higher SCC in the milk from AMS.

– – –

–

– –

Higher

No variation

36% lower

Lower Clearly lower

Clearly lower

–

Lower in high yielders Lower

Denmark

Iowa (US)

Poland Latvia

Chez

Poland

Poland

Poland

–

–

Significantly lower – – –

Higher

–

–

–

–

–

Lower

–

Significantly higher Higher

– –

– Lower

2.7% lower

No variation

Higher

– –

–

No variation

No variation

–

–

Slightly higher Slightly higher higher

A bit higher

The Netherlands European countries Finland

A bit higher

–

–

0.007  C higher Slightly higher Slightly higher significantly higher –

higher

Higher

Denmark

–

–

–

–

Clearly lower

Fat content –

Freezing point Higher

TBC Higher

SCC Higher

Country The Netherlands Israel

Anaerobic spores –

–

–

–

– –

Higher

Slightly higher Slightly Higher Higher

–

Lower

FFA content Higher

Table 4.2 Analysis of milk quality factors post-automatic milking system adoption in comparison to non-AMS

No variation

Clearly higher Slightly higher –

No variation No variation – Lower

Kolenda et al. (2021)

Sitkowska et al. (2017)

Sitkowska et al. (2015)

Bogucki et al. (2014) Petrovska and Jonkus (2014) Tousova et al. (2014)

Bentley et al. (2013)

Oudshoorn et al. (2012)

Salovuo et al. (2005)

–

–

van der Vorst and Ouweltjes (2003) de Koning et al. (2003)

Shoshani and Chaffer (2002) Rasmussen et al. (2002)

References Klungel et al. (2000)

–

No variation –

Protein content –

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Effect on Total Bacterial Counts of Milk

Anaerobic spores often originate from the dung layer over the teat surface. Teat skin is regarded to be the most important source of milk microflora along with secondary sources such as herd feces, bedding material, and milking equipment (Derakhshani et al. 2018). Thus unsatisfactory robotic cleaning of the teats is held responsible for bacterial growth in milk. In 28 farms, Klungel et al. (2000) observed that the average TBC raised from 8000 to 19,000 cfu/mL, while the occurrence of bulk milk samples with TBC >50,000 cfu/mL amplified from 4% to 15% and those with TBC >100,000 cfu/mL from 1.6% to 6.8% after the introduction of AMS. It is not always the case that complete teat sanitation before milking is reached in the AMS and no techniques have been established to evaluate the teat dirtiness in the current AMSs. Moreover, teat-cleaning failures are quite frequent. This explanation is supported by Janstova et al. (2011) and Tousova et al. (2014) who found an improvement in microbiological properties of milk with AMS after emphasizing on hygienic milking practices, including regular brushing and cleaning of teat and milking cup, as well as frequent disinfection of the milk piping and bulk tanks. Besides the milking system, TBC is influenced by certain other parameters too, such as shed cleanliness, equipment hygiene, and largely on milking interval, which influences the time of bacterial growth in the teat cistern. Optimization of all of these parameters will certainly decrease the spore count. In some cases, minor counts of E. coli, S. aureus, and Enterococci were observed in milk sampled from AMS installed farms. AMS which is not in continuous use can have some residual milk in its pipelines that can get mixed with warm fresh uninterrupted milk supply, providing a favorable medium for bacterial growth and ultimately increasing the total bacterial count.

4.6.3

Effect on the Fat Content of Milk

Minor changes in milk composition can cause pertinent economic loss in the long term especially for the milk chosen to manufacture cheese. For this reason, many studies have investigated the effect on milk fat content after the use of AMS. Wirtz et al. (2004) in his study reported the fat content to be 0.23% less in cows undertaken by AMS. In contrast, Salovuo et al. (2005) reported an average increase of fat content from 3.85% to 4.20% after the introduction of robot milking. However, the increase was statistically insignificant and the change was related to shorter milking intervals in the AMS. Janstova et al. (2011), Innocente and Biasutti (2013), and de Marchi et al. (2017) assessed the fat content for milk samples obtained with AMS and a conventional milking system with different herd sizes, different stages of lactation, and at different periods of the year to conclude that no significant effect of milking system on the fat content of milk was noticed.

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Effect on Free Fatty Acid and Composition of Milk

An increase in robotic milking frequency elevates the quantity of free fatty acid (FFA) (Klungel et al. 2000; Wiking et al. 2006), which adversely influences the milk flavor and cheese-making process. However, no significant fat quantity difference in samples taken from AMS and CMP was observed, except for C16:0 and FFAs, which were found to be higher in AMS than CMP. Svennersten et al. (2002) also suggested an increased milk FFA profile with improved milking frequency. Higher content of FFA in robotically obtained milk is also reported by Wiking et al. (2006). The variation in FFA with lactation seems to be related to milk yield. Higher levels of FFA in AMS were observed in the first 3 months of lactation when per day milk yield is higher. Abeni et al. (2005) conducted trials on Holstein Friesian (HF) milk to find out that lipolysis was influenced by practice followed and concluded that FFA was in a higher ratio in milk fat obtained from AMS than CMP. FFA is derived after lipolysis of milk fat-generating glycerol and FFA. Chilling and mechanized conditioning of milk can cause disruption of fat globule; subsequently, an upsurge in FFA is reported. Shorter milking intervals are also related to bigger fat globules which are much liable to lipolysis than small ones (Abeni et al. 2005; Wiking et al. 2003).

4.6.5

Effect on Freezing Point of Milk

The freezing point (FP) of milk is relatively constant because it originates from the osmotic equilibrium between blood and milk. A FP near 0  C signifies admixing of water to milk; hence FP is considered during the milk payment system. Many researchers have indicated that the increase in FP is due to increased water content after the adoption of robotic milking. While producing good quality milk, this aspect is of major concern because dilution of milk, even in small quantities, results in a lower concentration of nutrients and degraded technological performance. Two independent investigations indicated the same entity of rising in average FP (0.520 to 0.517  C) after the introduction of AMS with the level remaining substantially higher afterward (de Koning et al. 2004; Klungel et al. 2000). The rise is attributed to the frequent cleaning and rinsing of the system which adds residual water to the milk. Janstova et al. (2011) also experienced a minor increase in FP in Czech dairy farms. Innocente and Biasutti (2013) received similar FP values in repeated milk samples from different AMS manufacturers. On the contrary, Tousova et al. (2014) recorded a lower FP in milk from 300 robotically milked cows as compared to 200 cows milked by CMP.

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Effect on the Protein Content of Milk

Wirtz et al. (2004) showed that cows milked with AMS had no alterations in protein content to that from CMP. Short milking intervals were related to higher milk production per cow per hour with improved protein yields (Hogeveen et al. 2001). According to Innocente and Biasutti (2013), no effect of the management system was seen on the protein content of AMS milk. Lower protein content, if observed, was attributed to higher milking frequency in robotic milking. In a study, Smith et al. (2002) stated that milk protein ratios were substantially lower in dairy herds milked thrice a day than in those milked twice a day. Johansson et al. (2017) reported milk protein composition varies with an average of 2–7% reduction in total casein and 4–6% lesser β-casein content in milk from the robotically milked herd as compared to CMP. Dietary factors such as the availability of limiting amino acids also significantly affect the milk protein content (Schwab and Broderick 2017).

4.7

Effect of AMS on Udder Health

Besides the obvious benefits of mechanization, animal health and welfare is also an important aspect that is to be studied with respect to AMS. After investment in AMS, cows are milked more frequently, which does not provide adequate time for the harmful bacterial spores to grow. Early studies suggested poor udder health with damaged teat orifices in AMS led farms in comparison to CMP. Conventional AMS models usually have extended machine-attachment times as compared to modern-day AMS, which is suspected to be the major cause of deleterious udder health. Recent studies reveal that AMS is incapable of detecting subclinical and clinical mastitis without prompt tracing of dirty udders and thorough teat cleaning (Hovinen and Pyorala 2011). In an AMS, the cleaning decision is no longer in the hands of the herd’s person. There are four types of devices used by various AMSs for teat cleaning: (1) simultaneous scrubbing of all teats by a horizontal rotating brush, (2) successive washing by brushes or rollers, (3) instantaneous washing of all four teats in the same teat cup used for milking, and (4) successive cleaning each teat with a distinct cleaning apparatus. It is noticed that not even one of the four schedules dried the teats beforehand the initiation of milking, thus providing another chance for bacterial growth in the teat orifice. Jago et al. (2006) studied 130 teat-cleaning procedures in AMS to conclude that only 67% of all the four teats were thoroughly brushed. Similarly, Hvaale et al. (2002) recorded nearly 10–20% of the teat scrubbings per cow to be incapable of removing all dirt and manure from teats before milking. On the contrary, Berglund et al. (2002), Wirtz et al. (2004), and Abeni et al. (2008) indicated no significant effect in udder health in robotically equipped dairy farms with respect to non-automated farms by recording mastitis incidence or evaluating SCC, while Lopez-Benavides et al. (2006) reported a greater prevalence of udder infections

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with milking machine in comparison to robot operated parlor, this suggested that switch to AMS is a positive change for udder health.

4.8

Effect of AMS on Milk Let Down

Bruckmaier et al. (2001) suggested that in AMS, teat-cleaning devices stimulate the release of oxytocin and milk letdown prior to the start of the milking process. In CMP, tactile stimulation of the mammary gland leads to alveolar milk ejection through a neuroendocrine reflex (Dzidic et al. 2004a). Thus, a proper stimulus to the udder is of much more importance in AMS than in hand milking practice because of short or irregular intervals between milkings (Bruckmaier et al. 2001; Dzidic et al. 2004b; Maþuhova et al. 2004). AMS should be programmed to stimulate teats based on the expected degree of udder fill to make milk let down more effective. Alternatively, the threshold for allowing a cow to be milked can be set to accept cows with udders expected to be >60% full (Dzidic et al. 2004b). In AMS, the time gap between cleaning and neither the initiation of milking nor the sequential attachment of teat cup negatively affect milk ejection (Bruckmaier et al. 2001; Maþuhova et al. 2003).

4.9

Effect of AMS on Milk Leakage

A persistent visual and auditory stimulus from AMS leads to the release of oxytocin that could also increase the chances of milk leakage. Milk leakage is related to a greater risk of mastitis (Persson-Waller et al. 2003). It is often observed in cows being milked by AMS. Milk leakage is frequently observed in the resting area before taking entry into the milking parlor. Intra-mammary pressure (IMP) is considered a strong factor responsible for the leakage of milk (Rovai et al. 2007). Although IMP is yet to be measured for AMS, it may vary to a large extent in milking intervals and is expected to be higher with AMS. Much study is required to identify the accurate interpretation of milk leakage with respect to teat end condition and prior milk leakage history.

4.10

Effect of AMS on Animal Welfare

Animal welfare in a dairy farm is influenced by several related factors. Social interfaces along with herd mates, manual intervention, farm managing strategies, nutrition status and feed supply, housing, and further ecological surroundings can impact animal welfare in both deleterious and progressive manner (Wiktorsson and Sorensen 2004). Unlike cows reared in non-automated farms, cows in AMS are

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independent to regulate their day-to-day events and are more interactive with their surroundings. However, seclusion with the herd mates in an unacquainted environment is supposed to cause stress in dairy cattle (Rushen et al. 2001). As a result, different animal welfare factors are to be taken into consideration with AMS.

4.10.1 Lameness Lameness can be regarded as among the utmost disturbing issues in the dairy business causing production losses and economic issues on a farm. Lameness results in production losses in milk and hence fat and protein components of milk are also decreased which is held responsible for economic losses in a dairy farm. It can be tagged among some of the principal welfare and financial concerns in the contemporary dairy industry, which may be the root cause of a drop-in milk yield 4 months before diagnosis and continue to affect the yield 5 months even after its clinical diagnosis (Green et al. 2002). It is therefore known to cause the highest losses in a dairy farm, after mastitis and fertility issues. This problem is highly associated with the type of housing system, condition of the barn, and state of the floor in the sheds. Lameness in cattle is a debilitating disorder in which it experiences foot lesions and pain. In the majority of lameness cases in cattle, foot lesions are a cardinal sign. The affected animal attempts to reduce the weight tolerated by a particular limb by frequently shifting their own weight from one leg to another leg in order to bear the pain during lameness (Neveux et al. 2006). Pain is a serious factor in lameness which is often concealed by the enduring nature of cattle which results in late detection of lameness by the farmers and often leads to life-long losses. In the USA, the incidence of lameness is recorded in 15% of the adult dairy animals (Rajkondawar et al. 2002a) whereas, in 75% of the cases, farmers fail to recognize the illness (Whay et al. 2003). Reducing the walking activity can be taken as a measure to minimize the chance of lameness. Pain experienced from lameness could act as a stressor in dairy cattle. Lameness influences the motivation and frequency of voluntary visits to AMS hence the milking interval and the productivity of the cows are affected (Borderas et al. 2008). All these factors collectively sum up the negative impact on herd profitability, as well as on the health and welfare status of the cows. Therefore, it becomes predominantly significant to address cases of locomotion and lameness concomitant to AMS in time. Rajkondawar et al. (2002b) developed a scoring system to detect lameness in limbs in AMS. However, the discussion on the scoring system is beyond the scope of this chapter. Bach et al. (2007) observed a lower tendency to visit AMS with increased fetch rates for cows with high locomotion scores (scores of 3) on an increasing severity scale of 1–5 in comparison to low score cows. Similarly, in another research, Danish cows identified as lame had a low milking frequency than healthy cows being milked with AMS (Klaas et al. 2003). Cows that entered the AMS less are reported to have higher gait scores (mean  SD: 2.5  0.8 vs. 1.8  0.4, respectively) than the cows

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that visit AMS frequently (Borderas et al. 2008). They were scored for gait using the method described by Flower and Weary (2006) which suggests that cows with lameness (high score in severity scale of 1–5) tend to be reluctant in entering AMS because of painful conditions. While renovating an existing building to accommodate an AMS, necessary alterations may result in increased lameness because of faulty abrasive concrete floor which can add a negative impact on hoof health. On the contrary, no significant observations were made with the severity or quantity of lameness after switching to an AMS, keeping all other structures of the barn and management practices fixed (Hillerton et al. 2004; Vosika et al. 2004). It can be concluded that lameness is more a result of faulty housing design and ill management rather than the form of a milking system. AMS can be facilitated with the distinct examination of the force applied on each load cell which auto-senses the variations in the distribution of weight which is a strong indication of lameness (Pastell et al. 2008). This can be a very helpful and influential tool to detect the ailment in the early stages when medication is quite effective. Some AMS has four load cells on the floor of the milking stall to sense weight shifts in the cow limbs. This feature lets the robotic arm stay directly beneath the udder every time.

4.10.2 Estrus and Its Detection In general, AMS is not found to affect most of the reproductive parameters in a cow. However, variations were seen in conception rates and the number of services per conception 1 month after setting up AMS in a farm, and a non-significant decline in fertility level was recorded up to 12 months after installation (Dearing et al. 2004; Kruip et al. 2002). This condition may resolve with time as AMS is equipped with estrus detection mechanisms that function better once the cows get accustomed to the newly introduced system and the dairy owners dedicate more time to observing the cattle behavior. Most of the studies undertaken in this field are short term and longer trials are required for a complete explanation for any changes in fertility parameters. Transponders identifying the cow on its entry to the parlor are often coupled with activity and rumination monitors. The activity monitors record the number of steps taken by a cow each day. Increased activity is highly correlated with low progesterone levels during estrus which can be used to determine the correct timing of artificial insemination (AI) (Durkin 2010). A study conducted with six trials by an Afikim/DeLaval activity monitor achieved an average of 82% estrus detection specificity with a range of 73–92% (Durkin 2010). The low detection rates were most likely attributed to the presence of lame cows that showed the least activity during estrus and negatively affect the data. Auto-detection of increased activity can facilitate visual monitoring of estrus; however, the data interpretation from the AMS has to be learned by the farmer on his own.

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4.10.3 Stress Responses to Different Milking Systems 4.10.3.1

Physiological Stress Response

In AMS, milch cows are handled in a manner to give adequate motivation for the self-regulatory and effective approach of cows to entry and exit to the milking stalls independently without the help of herd handlers. Gygax et al. (2010) suggested that when there are no movement restrictions, animals have more freedom to choose their partners. Hopster et al. (2002) compared the stress response of primiparous cows while milking in an AMS and a tandem milking parlor. Cows under AMS reflected a low heart rate (HR) as well as lesser plasma epinephrine and norepinephrine levels indicating decreased stress during milking while the HR increased significantly just before entering the AMS as well as the tandem milking parlor. However, HR drops by the end of milking in both the milking systems (Wenzel et al. 2003). The accelerated HR may be attributed to anticipation of feed or feeding behavior offered before or during milking in AMS. Hagen et al. (2005) found no difference in the HR variables between AMS and parlor milking systems. Higher cortisol levels were reported in milk obtained from AMS-milked and parlor-milked cows (Abeni et al. 2005; Hagen et al. 2004; Wenzel et al. 2003) which is indicative of chronic stress due to forced-guided cow traffic permitting access to feeding or resting areas in the AMS. Gygax et al. (2006) and Lexer et al. (2009) found no differences in the milk cortisol levels between the systems, although HR was elevated in both free or guided-/ forced-traffic AMS types.

4.10.3.2

Behavioral Stress Response

Vocalization, defecation, and urination are acute stress indicators in cattle. These factors have been reported particularly on the first day of transition when milking was done by AMS. However, the occurrence of these stress factors quickly dropped to zero on subsequent days (Jacobs et al. 2012). An increased activity like kicking and stepping reflects anxiety in animals. Hopster et al. (2002) stated no variation while analyzing steps and kicks during the milking between AMS and a CMP, whereas Hagen et al. (2004) found less stepping and kicking in AMS. It is said that step-kick rates are expected to be highest at the end of milking during teat cup detachment (Jacobs and Siegford 2012b). In this regard, Wenzel et al. (2003) observed that the step-kicking occurred significantly in all phases of milking by AMS as compared to CMP.

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Disadvantages of AMS

4.11.1 High Initial Cost of Investment The cost of AMS is the primary obstacle in the adoption of such a system. The initial investment borne by the farmer is often two to three times more in comparison to the cost incurred on a traditional milking parlor. Each single stall AMS is estimated to cost $150,000 to $200,000 and can serve approximately 60 cows depending on the number of milkings the dairy owner wants to gain for each cow per day. However, building a new traditional parlor is also not always an inexpensive affair. At times it may involve a heavy investment of $4000 to $15,000 per milking stall. However, the CMP is expected to be a single cost incurred while farmers might require buying several AMS to accommodate their herd (Bijl et al. 2007). The farmer needs to put significant funds to install as well as maintain the machinery in AMS. Moreover, the operating cost of AMS is high due to increased electricity consumption whereas water and chemical consumption is reduced to 50%. Initial returns from AMS are not as expected at least 10–15 years post-installation as compared to the cost-benefit ratio (BCR) for CMPs. The introduction of AMS also affects the cost incurred in milk production, feeding, energy usage, and labor requirements. Therefore, it is important to find a logical scientific outlook to minimize costs and enhance profit in the long run (Jiang et al. 2017). According to Maculan and Lopes (2016), investment in AMS in Brazil can cost R $19,000 (or about US$5250 at the time of the survey) per head, and return may take 8–10 years. A recent quote by DeLaval in 2018 (access granted to authors) presented value in the order of R$740,000.00 (or US$195,000.00) for acquisition and implementation of the newest AMS unit (DeLaval VMS V300). It consists of a milk-first system capable of milking about 70 cows on average in a compost dairy barn.

4.11.2 Alterations in Milk Quality Another potential disadvantage is in milk quality. In AMS, an increase in milk yield is achieved through frequent milking intervals due to which the milk fat is estimated to slightly decrease than the milk obtained with conventional hand milking practice twice a day (Klungel et al. 2000). This accounts for a major economic loss as the fat content is the major factor in the milk payment system. Furthermore, to achieve a higher level of production, the more concentrated feed was to be consumed which in turn may raise the feed costs.

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4.11.3 Alterations in Milk Composition High levels of milk FFAs are undesirable for promoting sensory changes and shortening shelf life and milk yield. Several studies have suggested that an increase in FFA in AMS farms can be associated with increased frequency and lower milking interval, interfering with the fat globule size and making them more vulnerable to lipolysis (Wiking et al. 2003, 2006). Bach et al. (2009) suggested that milk first or feed first like guided-flow systems are likely to reduce milk solid levels (fat and protein). Fat content is intended to fall due to an increased chance of ruminal acidosis and reduction in protein is suggested as a result of a reduction in dry matter consumption of cows in forced flow systems, especially when water access is restricted.

4.11.4 Lack of Flexibility It is a huge challenge for a dairy owner to bring change in the size of herds while using AMS. AMS is already mechanized in such a manner that it can entertain only a fixed number of cows with provided infrastructure. Thus AMS lacks flexibility with respect to modification in herd size. While choosing whether to invest in an AMS or a traditional milking parlor, dairy owners must compare the savings on labor achieved in AMS with respect to the increase in fixed costs and depreciation rate (Bijl et al. 2007).

4.11.5 Increase in Incidence of Subclinical Ketosis According to Tatone et al. (2017) and King et al. (2018), chances of subclinical ketosis are found to be higher in dairy herds with robotic milking. The reason is associated with the increase in the number of milking events and milk production integral to AMS. King et al. (2018) also stated that AMS managed dairy cows are more likely to develop negative energy balance as they produce more milk than those milked by CMP. Since these cows do not tend to surge feed intake to the same extent, the incidence of subclinical ketosis may eventually happen. This study established that AMS herds have higher proportions of β-hydroxybutyrate than those in CMP (Tatone et al. 2017). King et al. (2018) concluded that cows with higher milk yield have larger amounts of β-hydroxybutyrate circulating in the blood with more likelihood of subclinical ketosis.

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4.11.6 Requirement of Specific Body Conformation Few cows in the herd may show specific behavioral or conformational characteristics which are not appropriate for incorporating them into a robotically milked herd. Undesirable teat position and udder size create problems for cluster attachment in AMS. During a study, teat variation and cluster attachment was observed on all 15 North American dairy farms, causing 0–3 extra culls per year from herds with an average of 94 cows (Rodenburg 2002). Distance between rear teats is also considered a major complication for cluster attachment by AMS. According to Rodenburg (2002), a very high rear udder is also held responsible for causing cluster attachment failure as it was tough for the sensors to get to the high rear teats in a horizontal plane. In New Zealand, 8% of productive new cows were culled due to detrimental conformations that were expected to cause difficulty in washing and milking (Woolford et al. 2004). Although technological advancement has brought 85–98% higher success rates in AMS cluster attachments with commercial herds (Gygax et al. 2007), a 7.6% teat cup attachment failures has been reported to at least one quarter during milking, even after successful teat positioning by the sensors (Bach and Busto 2005). Thus, udder and teat conformation of cows need to be checked before including it to the herd. Alternatively, genetic selection of cows with desirable teat positions has to be done to avoid cluster attachment hitches and unsuccessful milkings. Teat cup attachment failures bring about 26% losses in milk production during subsequent milking (Bach and Busto 2005). Milk productivity of unaffected quarters is also said to be reduced because of increased milking interval between quarter failure and subsequent milkings. However, milk production reaches previous levels within seven milkings following a failure.

4.11.7 Long Transition Period from CMP to AMS Dairy managers are required to devote time to train their herds on how to enter the milking system and experience the AMS in the beginning. It takes about 3–4 weeks of intense labor to get a herd acclimatized to enter voluntarily with a success rate of 80–90% (Rodenburg 2002; Jacobs and Siegford 2012b). However, the adaptation period may differ between individuals in the herd depending on the response, age, and experience of the herd mates (Munksgaard et al. 2011; Weiss et al. 2004). Prior exposure to the typical sounds and movements within the milking parlor may help the animal to get accustomed to the new setup with ease; although, primiparous cows seemingly familiarize themselves with the AMS more readily than multiparous cows (Jago and Kerrisk 2011). Net returns or profitability, and management of the setup have been observed to be the main objectives of small and medium farmers in view of the transition period for the AMS (Hansen and Jervell 2015).

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4.11.8 Lack of Motivation for Voluntary Entry and Exit Milking frequency and maximum amount of feed distributed to the cows at each milking is at the dispense of the computerized management system. There may be situations where the milch cow is not willing to enter the AMS box. Moyes et al. (2014) and Tse et al. (2018) surveyed 217 farmers in eight Canadian provinces and observed that on an average, 2% of a herd had to be culled for not being able to adapt to voluntarily milking, although the physical and behavioral features are in the acceptable range. It may be possible to take manual help to manage animals in the milking area and feeding passage, to execute the milking and feeding processes. Therefore, motivating the milch cows to individually access the milking parlor voluntarily is vital for accomplishing the task and overall worth of the system (Hogeveen et al. 2001).

4.12

Future Prospects of AMS

There is an upsurging interest in automation because of the speedy evolution of machine learning and artificial intelligence. Milking robots are not ideally those devices that substitute human resources to create a life of leisure; rather, it seems to yield a new type of work opportunities for farm labor under AMS. The automation of dairy farms cannot be assumed as a one-way process but can also be studied with a “process-relational” perspective which suggests approaching the dairy business as a “dynamic socio-physical process” where humans and animals are intertwined together. After the COVID-19 pandemic, in order to reduce the chances of human intervention, robotic milking is the “new normal” in the dairy business with Norway recording the highest number of AMS installed, out of all the Nordic countries. It has been reported that more than 47% of the milk production in Norway is conducted by milking robots by the end of 2018 (Vik et al. 2019). In the past, many studies have been conducted to assess the aftermath of AMS installation on milk productivity, labor savings, and animal welfare. Although an enormous amount of generous research is being carried out over the above-mentioned factors, exotic studies over many prospects can be done to evolve better solutions. The main objective of AMS installation, i.e. labor reduction is not always feasible due to a significant number of cows that are not willing to enter the milking area and need to be fetched to the AMS manually. Thus, to make the machine 100% efficient, strict strategic innovations are required to modify animal behavior, reduce fetching rates, and reduce the labor usage to nil. The stockmen are essential in the setting of new milking and farm schedule after AMS installation as the main motto of the machinery is to bring modifications in workload, not lesser work. Cows that are meant to be fetched each day to the AMS may show a tendency to develop lameness or other health issues which is another area of concern that

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requires investigation. It may be a possibility that AMS can only detect subclinical lameness not before it becomes prominent in gait. Thus increasing sensitivity for early detection of lameness in cows, automatically, may prove beneficial for cows that have an increased probability of being fetched daily. AMS in the pasture-based farming systems need to be explored more to improve the competence by effectively co-coordinating both the systems. Researchers can explore further to study the cow behavior in pastures with respect to changes in energy balance and milk yield. Higher stocking rates, effects of weather in AMS pasture, and motivating factors for pasture-based cows for voluntary actions are the areas that require more attention. Technological advances in teat cup attachment and precise identification and sanitation of muddy udders need to be pushed further to increase their effectiveness to elevate cow health and milk quality standards. Further studies on milk leakage should be conducted to determine whether this is truly a problem for cows in AMS systems. Clean and hygienic milk practices and ways to motivate voluntary movements of cows to be milked in AMS box should be further sorted to reduce undesirable FFA content in AMS milk. Decision making and management practices should be analyzed to take maximum advantage of the large data pool generated by AMS to predict the highest milking frequency for each cow to maximize the milk yield. Although AMS has been widely adopted in European countries, it has yet to be recognized in other regions of the world that have a considerable cattle population. For instance, it is important to continue looking for opportunities for its extension in Indian conditions for farms of all sizes and locations. A constant matter of attention should be to understand why AMS is approved only in specific areas or countries. Other than quantitative and structural aspects, the focus should be more on the feasibility of the location whereby individual farmers want to establish practices related to AMS. It should be taken into consideration as to what factors encourage or hinder the implementation of such equipment. To this effect, biosensors in AMS should be studied to exploit their ability in the detection of significant changes in herd health and feed changes that might signal ill management. More extensive research regarding metabolic and immune responses of AMS supported cows needs to be done. However, few evidence suggests that AMS is not responsible for negative energy balance in AMS-milked cows despite more frequent milking, but they do not offer any conclusive explanation. AMS designs should be modified according to the stocking rates of cows per AMS, stalls and feed passage, breeds, and structural conformations which are distinct for individual breeds found in specific regions. Animals of different breeds belonging to different geographical areas differ in temperament and may show different behavioral patterns when put in AMS. Efforts should be made to decrease the time spent by milch cows in the selection and waiting areas to encourage the voluntary movement of cows through AMS. Modern AMS stalls open in the front and rear to permit cows to enter and exit the milking area in a straight line, without turning or bending themselves.

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Conclusions

The integrated approach of learning about AMS involves three factors, i.e. humans, machines, and animals. Concerns arising after incorporating AMS to the herds cannot merely be explained by taking farm owners and animals into account. It is also required to consider the human–machine–animal nexus. It takes enormous technological innovations to influence everyday farm operations in a dairy. The rise in milk production, labor savings and flexibility in farm schedule, better life quality, reduced feeding cost, better reproductive performance, improved milk quality, herd health, less equipment maintenance, and profitable turnover on investment are the main factors to be considered by farmers who are likely to invest in AMS. Installation of the milking machine to reach its maximum efficiency requires intricate work by the servicemen who fix up the entire setup and also the efforts of the farm supervisors in smooth management for a better functioning AMS. For efficient AMS operation, factors such as reproductive traits, animal genetic potential, and diet formulation should be improved in order to gain maximum returns. AMS not only extends its help in milking operations with in farm but also on a larger network in running a dairy business. Although AMS is a digital age automated technology, its proficient working requires the physical presence of the dairy owner and strategic management without which the project is likely to fail.

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Part II

Smart Food Manufacturing

Chapter 5

Smart Technologies in Food Manufacturing Rahul Vashishth, Arun Kumar Pandey, Parinder Kaur, and Anil Dutt Semwal

Abstract The concept of “smart food factory” is also known as “food industry 4.0” or “connected food industry” which relies on the up-gradation of existing facilities to modern technologies and their integration with the internet, cyber physical system, artificial intelligence, big data, cloud computing, etc. Smart food industry is a digitally connected, automated food processing environment where the real-time monitoring of physical operations, collection and sharing of data throughout the processing line, storing and processing the data through neural networking and algorithm, communicating and cooperating with humans in real time, along with precise control over the operation through actuators and robots are done simultaneously. As compared to others, food industries are lagging behind in adoption of modern technologies. However, increasing market requirements and strict compliance of food safety regulation in food industries around the world like, grain processing industry, fruits and vegetables processing industry, meat, fish and poultry processing industry, dairy and beverage industry, etc., upgraded their existing facilities at different levels with modern technologies. This chapter illustrates a deep insight on transformation of different food processing industries from a traditional processing environment to a digitally connected modern technologies based environment over the decades. Keywords Automation · Artificial intelligence · Food industry 4.0 · Internet of things · Processing

R. Vashishth (*) · P. Kaur Vignan Foundation for Science Technology and Research, Guntur, Andhra Pradesh, India A. K. Pandey Department of Food Science and Technology, MMICT & BM (HM), Maharishi Markandeshwar (Deemed to be University), Ambala, India A. D. Semwal Grain Science and Technology Division, DRDO-Defence Food Research Laboratory, Mysore, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_5

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Today, automation in the food processing industry is becoming critical due to the rapidly changing global scenario. Several factors such as increasing competition due to globalization and mergers, consumer requirements for high-quality food, government emphasis on cleanliness, hygiene and safety factors, and flexibility in manufacturing operations for more diversified product lines took the attention of food industries towards up-gradation of existing systems. Moreover, precise control over the processing line from a simple step to a highly sophisticated operation is also of utmost importance to achieve the above-said predetermined goals. For example, in today’s competitive market, it is becoming very common for manufacturers to facilitate a variety of their existing products by changing the product formulation or processing conditions. However, this needs flexibility and control over the system through which a food technologist can easily modify the recipe. To remain competitive, food factories look for up-to-date manufacturing technologies that meet the increasing market demand for variability in products with high processing capacity, efficient supply chain, and optimized energy consumption. Apart from building an effective control system and maximizing operational flexibility, automation of processing lines is one of the important ways for food industries to tackle future challenges. The rise of a concept of “smart food factory” also known as “connected food industry” or “food industry 4.0” relies on the up-gradation of existing facilities to modern technologies and their integration with information technologies for digitalization of processing lines. The core concept of a smart food factory includes efficient and sustainable production by building a smart ecosystem where employees, machines, and electronic devices can interact with each other through information technology (Konur et al. 2021). Smart factory enables the transformation of manual and disconnected operations into a digitally interconnected and inter-operable system within the ecosystem that allows making decisions based on real-time data and real-time communication between operators, sensors, and machines to execute necessary actions and accelerate the overall processing activity. In a smart food factory, a high rate of productivity can be achieved by collecting and evaluating the real-time data of machines and sensors, and utilizing the inferences to build a flexible advanced process with reduced error and costs. Such an integrated approach also offers unprecedented real-time management of problems, optimization of unproductive set-up times, and taking rapid actions against the threats and opportunities throughout the processing lines (Konur et al. 2021).

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Components of Smart Food Factory

Usually, modern food processing technologies, sensors, and information technologies are considered as major components of smart food factories as they together play an important role in creating a smart ecosystem throughout the food chain ranging from the purchase of raw materials to the supply of finished products. From the last decade, food factories are continuously shifting from conventional practices to more advanced processing technologies such as vacuum microwave heating, ohmic heating, extrusion, irradiation, high-pressure processing, pulsed electric field processing, plasma processing, pulsed magnetic field processing, ultrasound processing, etc., to achieve their sustainable development goals (Ghoshal 2018; Chauhan 2019). These technologies are capable of producing microbiologically safe minimally processed food with the same or improved quality attributes as compared to conventionally processed ones. Even modern food processing technologies are more efficient than their conventional counterparts in terms of energy consumption, emission of carbon dioxide, impact on the environment, waste generation, and recycling. Modern food processing machines are more flexible and provide control overprocessing due to their programmable logical control (PLC) and sensor-based automatic system. Today, most automated food processing machines come with an inbuilt PLC system in which user-oriented instructions can be programmed and stored in internal memory to perform specific functions such as continuous monitoring and control over processing time, temperature, pressure, etc. A PLC system generally contains a processor, internal software and data memory areas, input/output (I/O) interface, and electronic circuits. In an automated processing machine, the PLC system continuously evaluates the data received from the sensors by scanning the loaded software and then conveys the I/O status through a digital screen to the operator and allows him to take necessary action (Yeole et al. 2017). The application of each technology has its advantages and disadvantages; however, smart sensor-based technologies are also considered more prominent for the present and future development of sustainable food processing and automation (Jambrak et al. 2021). In modern food processing system sensors play an important role as they are designed to detect and alarm towards the unusual change in their physical environment and keep informing the operator through PLC about the situation, and thus facilitates better control over the processing technologies, processing conditions, and the quality of the final product (Yeole et al. 2017). Smart sensors can utilize the available data to analyze or describe the situations and take corrective decisions or control processing conditions. Here, the “smart” concept refers to those devices that are intelligent or can be connected to other devices. Today, several types of chemicals, biological, and electronic sensors are used at different levels of food processing lines depending on their specific functions. However, electronic wireless sensors (radio frequency sensors) are of utmost importance in the smart food factory due to their ability to connect from distant devices through information technology (Miranda et al. 2019).

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The most important part of a smart food factory is information technology (IT) which play a pivotal role in digitalization or creating a smart ecosystem by integrating machines and sensors at a different level of intelligence through the Internet of Things (IoT), cyber physical system (CPS), artificial intelligence (AI), big data, and cloud computing, etc. Together, these IT tools offer real-time monitoring of physical operations, collecting and sharing data throughout the processing line, storing and processing the data through neural networking and algorithm, communicating and cooperating with humans in real time, providing precise control over the operation through actuators and robots, and thus facilitating the smart and automated food processing environment (Otles and Sakalli 2019). Today, both perishable and non-perishable food processing industries took a step ahead and adopted several innovative technologies at different levels of food processing and management to increase their efficiency and productivity through automation. The technological automation in different food processing is as follows:

5.3

Cereals, Pulses, and Oilseed Industry

The demand of the food grains is expected to increase by 1 billion Mg by the year 2100 owing to the rapidly increasing population and thus food demand (Stewart and Lal 2018). Also, there is a rise in the production of pulses, however, there has also been a stagnant per capita availability from 1980 to 2013 (Joshi and Rao 2017). Similarly for food grains, there is an increase of 2% per annum in the demand; however, a declining trend is observed in the per capita consumption (Chand 2007). There is thus a slow growth which may be attributed to a number of reasons such as lack of policy neglect (Singh et al. 2016), global climate change, decrease in plant communities’ biodiversity, risk of epidemics. Advance methods and digital innovations which can help improve forecast, control epidemics, monitor crop cultivation, etc., i.e., Industry 4.0 and technologies such as artificial intelligence, Internet of things, etc. can help improve the agri-food system (Butsenko et al. 2020). Moreover, the food industry is believed to have a possibility to automate most of its tasks (Chmielarz 2020). For modeling pulse production models such as ARIMA (Autoregressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditional Heteroscedastic) can be used (Gudadhe et al. 2018). Similarly, many more models, automation, biosensors, etc. are making its way in the cereal and the pulses industry.

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Automation in Identification and Classification of Seeds Quality

There is a wide range of genetic diversity among the edible legumes crops worldwide and the ascertainment of best quality seed is the major problem for the seed distributors as well as farmers. The knowledge of legume seeds quality and variety before sowing is another important aspect for agribusiness operators as it influences the overall crop production. Today, market value of grains and their products are determined by their physical features including size, color, appearance, physical defects, etc. (Tian et al. 2020). However, manually sorting and grading of seeds including beans is a very difficult and time-consuming process and even inefficient when performed at large production volume. Therefore, automation in legumes seeds classification is essential for both marketing as well as crop production and for sustainable agriculture. Application of Computer Vision System (CVS) or Machine Learning Technology is a big breakthrough in identification of seed health and distinguishing between different varieties of the same beans having similar features. Moreover, machine vision systems are very useful in inspection and quality evaluation of food grains with higher speed, consistency, and accuracy. Mahajan et al. (2015) reported that the machine vision system is a key to the successful inspection of legume seed quality through image acquisition. The quality of legumes can be inspected without destruction using visible, infrared, and other bands of the electromagnetic spectrum. As compared to conventional techniques which are based on manual inspection, machine vision technique is fast and automatic, and seeds characteristics like external surface examination, moisture content, oil content, insect infestation detection, and internal structure visualization can be done without destruction. De Araújo et al. (2015) developed a computer based visual inspection system for distinguishing different varieties of beans present in a same batch using a correlation-based multishape granulometry. They found their system extremely successful in distinguishing grains based on their size, eccentricity, and rotation angle with an accuracy of 99.97% and suggested the application of developed technology for automatic inspection of beans. In another study, Koklu and Ozkan (2020) developed a userfriendly SVM using a MATLAB graphical user interface to identify quality and distinguish the varieties of dry beans to obtain a uniform seed classification. Their study showed that CVS with support vector machine (SVM) classification model resulted in highest accuracy for Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira bean varieties, i.e., 92.36%, 100.00% 95.03%, 94.36%, 94.92%, 94.67%, and 86.67%, respectively. Apart from grain variety and quality, computer vision, machine learning, and image processing are also useful in automation in discovering diseases and supplementing medication on time to agriculture crops including cereals and legume crops to avoid heavy economic losses (Urva 2021).

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Automation in Cereals Processing

Immense advancement is making its way in agriculture smart technology. Technologies including UAV cameras, satellite data, IoT sensor networks, ground sensors, robotic platforms, etc. (Donaldson et al. 2019) will play a great role in boosting the production. Technologies such as biosensors find use in order to detect components like starch in the wheat flour sample (Mello and Kubota 2002). X-ray systems also find use in grain bulk (Mahalik 2009) for information such as detecting the size of grain. Material discrimination X-ray (MDX) technology has also been used to differentiate materials of different densities such as detecting contaminants of different density in case of breakfast cereals. They offer the advantage of being able to detect food in non-linear packaging which might not be possible in case of traditional X-rays (Massaro and Galiano 2020). The traditional milling equipment such as hand stone (chakki), mortar, and pestle have now been replaced by modern milling equipment for pulses and cereals such as pre-cleaners including drum scalpers, aspirator, reciprocating air screen cleaners, etc., milling equipment such as rubber roller huller for rice, carborundum roller mill for pulses, etc. Dehullers used for pulses include barley pearlers, tangential abrasive dehulling devices, etc. (Tiwari et al. 2020). Other sophisticated and advanced equipment such as color sorter to sort rice or the milled product from product that still contains the adhered seed coat, stone separators, plan sifters for wheat, hydrocyclones for maize, etc. are utilized. Large modern commercial plants involve automation of the milling process from grain intake to grain packaging/bagging. There are flow metering devices, various milling machines performing dehulling and splitting simultaneously and help in achieving high throughput.

5.3.3

Automation in Legumes Processing

In the grain processing industry, soaking is a most widely used pre-treatment for cereals and legumes before applying other processing methods. Soaking plays an important role in hydration of grains to provide a required amount of water for further processing, such as the removal of husk, bran, and splitting of grains by breaking the close contact between cotyledon from the outer husk/bran part by increasing grain volume, and at the same time strengthens the grains through pre-gelatinization of starch and denaturation of protein during pre-cooking which later reduce the damage of cotyledon of legumes during splitting/milling. It is also an important step in different processes such as malting and/or fermentation of grains. To mark the proper hydration of grains it is important to measure their volumetric expansion individually, which is a time-consuming process. However, disregarding the variation in volume of individual grain, bulk analysis method is a preferred traditional method. Therefore, Valerio Cubillo et al. (2020) evaluated an automated

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digital imaging processing pipeline for measuring individual grain volume and modeling the hydration kinetics of Phaseolus vulgaris (Matambu bean). Their study suggests that the technology is potentially useful for the automation in grain processing industries and can be applied for future studies on food properties, grain quality, processing and packaging design. However, Holopainen-Mantila et al. (2021) used hyperspectral imaging technology for the automatic monitoring of early-stage moisture uptake and germination of two different varieties of faba beans. Furthermore, extrusion processing is a fully automated technology. Recent innovations made this technology more efficient in terms of energy consumption during cooking and process control. It is a complex process which requires a specifically designed instrument called “extruder” in which mixing, forming, cooking, and shaping of snack foods including texturization of legume proteins can be done through programmable logical control (PLC) based automatic operational control system. Today, extrusion technology is widely used in industries for the removal or reduction of anti-nutritional factors in cereals, legumes, and oilseeds as well as retention of more nutrients in snacks as against conventional processing (Nikmaram et al. 2017; Sánchez-Velázquez et al. 2021).

5.3.4

Automation in Oilseed Processing

Oilseed’s processing industry is in its initial stage of automation. Several studies have been conducted to increase the productivity of oilseed processing industries in terms of processing methods, combination technologies and byproduct utilization. Most of the studies are focused on the use of different pre-treatments such as microwave heating, ultrasound, pulsed electric field, high-pressure treatment, etc., to the oilseeds to increase the oil extraction efficiency of existing systems. However, as such the oilseed processing industry is lagging far behind in application of computer technology, IoTs, cyber physical system, smart sensors, etc. in current scenario.

5.3.5

Automation in Quality Control

Many new technologies have started to make their way to ensure better quality control. Methods utilizing portable sequencing devices, for instance, “MinION” as well as mobile PCR can detect the pathogen such as rust fungi in cereal crops such as wheat (Donaldson et al. 2019). Detection of fungal activity in grains is of importance to prevent the spoilage of grains and production of mycotoxins. Many techniques are used for the same including DNA and immunoassays, immune-fluorescence, electrochemical methods, photo-acoustic FTIR methods, etc. (Magan and Evans 2000). E-nose can also prove to be an immensely useful tool for detecting the mycotoxins and thus help

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in ensuring safety and quality during cereal production and processing (Cheli et al. 2016). Automated fluorometric sensor for the determination of zearalenone mycotoxin is also being used (Llorent-Martínez et al. 2019). Gas chromatographic (GC) analysis using multiple selective detectors, such as an electron capture detector (ECD), a flame photometric detector (FPD), etc. are utilized in order to detect the pesticide residue in grains (Mastovska et al. 2010).

5.3.6

Automation in Preservation

The newer technologies are also used in the preservation of food grains and help achieve higher shelf-life and stability over storage by preserving the grain along with making them chemical free. Ozone also finds use for the preservation of food grain (helpful in reducing the fungal, mold, and bacterial contamination) and also provides the advantage of minimizing/eliminating the chemicals (pesticides, fumigants) used, and thus help in preservation along with producing chemical free grains (Tiwari et al. 2010). Similarly, UV radiation is also used for sterilization of the stored food grains which acts by altering the nucleic acid directly, making it impossible for the microbes to read the genetic code and thus causing them to die (Irradiation et al. 2006).

5.3.7

Challenges

The automation, advancement, and the digitalization provide immense benefit to help increase the production; however, it comes with its own challenges. However, these technologies due to situations such as insufficient or unstable energy supply pose a threat of heavy financial loss due to production shutdowns (Butsenko et al. 2020). Methods to detect fungal growth or mycotoxins such as electrochemical methods, etc. are not sensitive enough to detect fungal activity early and are also expensive and time-consuming (Magan and Evans 2000). The use of biosensors and e-nose possess a number of challenges such as the results being affected by the ambient gas as well as due to sensitivity of these sensors to changes in the relative humidity and temperature (Wojnowski et al. 2017). Other challenges continue to hold food manufacturers back from upgrading completely to Industry 4.0. Problems such as lack of proper infrastructure and internet availability in all the areas allowing to utilize IoT and related techniques truly as they are meant “anywhere, anytime” still is somewhat far and continuous up-gradation for the same is required. To conclude, for crops such as cereals, a new approach requiring methods which can integrate physical engineering and biology while working with breeding, agronomy, etc. are required (Donaldson et al. 2019).

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Fruits and Vegetable Industry

Automation is a process in which fast and precise technologies are used to perform repetitive work in an organization or industry, such as management of material, process control, packing of items, management of supply chain, etc. Automation not only optimizes the overall profitability by reducing production, processing, and handling costs but also assures produce delivered to the appropriate market in its sound condition. Moreover, smart automation significantly reduces manual labor, offers robust control over the process by increasing accessibility and connectivity in processing steps, and facilitates data-driven decisions using computer capabilities (Martinez et al. 2019). The fruits and vegetables processing industry includes several activities ranging from postharvest to retail of fresh/processed produce. Their processing includes different workstations to perform several steps such as grading, washing, peeling, cutting, antimicrobial treatments, packaging, transportation, and waste management, etc. The automation at different levels of the fruits and vegetable industry is as follows:

5.4.1

Automation in Grading

Fruits and vegetables are highly diversified horticulture commodities in terms of their quality parameters. For example, any two fruit or vegetable grown at the same time, in the same field, and picked from the same tree at the same time may differ in their shape, size, skin color, texture, aroma, and other attributes such as the presence of blemishes and diseases. Furthermore, in commercial operations, these quality characteristics significantly influence the appearance, nutritional content, and organoleptic properties of processed products and even their suitability for preservation. Therefore, grading of fruits and vegetables, based on one or two or more of the aforesaid parameters, is usually performed to increase their commercial viability. Traditionally, these parameters are evaluated manually through visual inspection and the sense of touch by the trained operators, which is a tedious and time-consuming process. Moreover, the decision taken by the operators can be influenced by psychological factors such as acquired habits or fatigue, or inconsistency due to human error in the classification process, which sometimes could lead to repeat the entire inspection process (Cubero et al. 2011; Paulus et al. 1997). In manual grading, the error in fruits and vegetable classification increases with the increase in the number of quality parameters considered for the decision-making process. Therefore, such type inconsistencies raised the need for automation in fruits and vegetables processing industries to speed up the grading process with high classification accuracy. Machine vision or computer-vision-based systems have received more attention from researchers to reduce human errors and simplify the tedious fruits and vegetables classification process. Today, machine vision-based systems and new optical

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technologies, such as ultraviolet, near-infrared, hyperspectral, and multispectral imaging, made it possible to develop potential tools for non-destructive quality monitoring of fruits and vegetables with the adequate accomplishment of predefined standards. As compared to manual inspection, non-destructive technologies are much faster allowing automatic inspection of the whole processing lines with defined objectives. The use of ultraviolet and near-infrared based systems also made it possible to inspect those defects of fruit and vegetables which are beyond the sensibility of human eyes. The creation of machine vision-based tools required the inclusion of multiple technologies and knowledge that are ranging from image acquisition techniques to specially designed algorithms for the analysis of spectral images. Machine vision-based fruits and vegetable inspection systems are specially targeted towards desired goals such as in-line sorting for commercial grades, detection of spoilage, or the distribution of chemicals on the fruits and vegetable surface (Blasco et al. 2017). The application of most machine vision systems is similar to conventional techniques, and uses visible information to determine the external quality of fruits and vegetables. However, its efficacy largely depends on the type of camera used and the illumination of the scene as they are closely associated with the quality and resolution of the acquired images. The system requires illumination with a good color rendering index to compare and measure the difference in object color from the reference standard. During image acquisition, proper illumination of the scene avoids specular reflection as it can mask the certain blemishes on the surface by producing bright spots. Apart from color, image acquisition also allows analyzing several other external properties of fruits and vegetables related to their maturity and quality. For instance, Prabha and Kumar (2015) developed an image analysis algorithm to predict the maturity of bananas. By the application of the color intensity algorithm and surface area algorithm, they achieved 99.1% and 85% accuracy in defining the maturity stage of bananas. Moreover, Vélez-Rivera et al. (2014) developed a ripeness index to classify pre-climacteric, climacteric, and senescence stages in the “Manila variety” of mango using a non-destructive computer vision system. The external defects of fruits and vegetables such as skin damage and diseases can be also analyzed through a machine vision system using color information alone. Blasco et al. (2007) identified different types of defects including differentiation between calyx or stem-end from the skin defects in orange. They developed a regiongrowing algorithm to identify defects when the color of a region diverged from the homogenous color of the largest region of orange skin, by assuming it to be the sound skin. They achieved 94% efficiency in identifying surface defects and 100% efficiency in distinguishing stems from the surface defects. Recent advances in hyperspectral imaging technique have also shown the potential of real-time automated in-line inspection and quality control as it offers to analyze the chemical composition, internal quality or detection of invisible damages of produce. However, hyperspectral imaging needs to be studied more for its effective implementation for the automatic grading of fruits and vegetables in industries.

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Automation in Washing, Peeling, Cutting, Disinfection

Today, consumers prefer either fresh or fresh-cut fruits and vegetables over processed ones due to increasing awareness about a healthy lifestyle, nutritional losses during processing, and desired fresh-like sensory attributes. In the fresh-cut industry, one of the major problems is the high perishability of fruits and vegetables. However, the quality of fresh-cut produce can be maintained for a longer duration of the processing, and distribution conditions are optimally maintained. The implementation of innovation and automation in different processing stages, such as washing, peeling, cutting, packaging, and storage, could be one such way to increase the shelf-life of fresh-cut produce (Tapia et al. 2015). Today, most of the fruits and vegetables processing industries rely on electricity-based semi-automatic batch or continuous type mechanical systems for washing, peeling, and cutting operations (Shirmohammadi et al. 2012; Ansah et al. 2018). Although, several modern non-thermal technologies such as high-pressure processing, plasma processing, ultrasound processing, irradiation processing, pulsed electric field processing systems are being adopted in many commercial units for the automation of sanitization and decontamination step of fresh or fresh-cut fruits and vegetables (Chauhan 2019). These novel non-thermal technologies help in extending the shelf-life of fresh-cut produce by arresting enzymatic reactions and inhibiting microbial growth with high precision and with no or marginal effect on nutrition and sensory properties of produce. Drying fruits and vegetables up to optimum moisture content is one of the effective ways of extending the shelf-life of produce by arresting the microbial growth, chemical, and enzymatic reactions. However, conventional dryers have no such system to monitor the changes in physico-chemical properties and moisture content of products, when the product is under drying process. Furthermore, separate facilities and extra manpower are required to estimate the physico-chemical properties and moisture content of produce and to assure the completion of the drying process. In conventional drying, sometimes repeated drying is needed to achieve optimum moisture content, which increases the processing time as well as overall processing cost. The integration of conventional dryers with non-destructive quality evaluation techniques, such as machine vision, low frequency nuclear magnetic resonance (LF-NMR), visible near-infrared (Vis-NIR), and hyperspectral imaging system, could be one such most recent innovation in developing smart dryers to overcome the above-said problems (Su et al. 2015). For instance, Sampson et al. (2014) used a computer vision system for in-line monitoring of changes in color, texture, and moisture content of apple slices during drying. Pu and Sun (2015) successfully investigated moisture distribution in mango slices during vacuum microwave drying by combining it with the Vis/NIR hyperspectral imaging system. Romano et al. (2016) successfully investigated the changes in browning, hardness, and moisture content of mango and litchi during in-line drying operation by integrating Vis/NIR spectroscopy with the existing drying system. Chitrakar et al. (2019) used the low field nuclear magnetic resonance (LF-NMR) technique for

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automation in monitoring the water activity of produce during the in-line drying operation. However, Sun et al. (2019) used LF-NMR integrated microwave vacuum drying system and developed a BP-ANN (Back Propagation—Artificial Neural Network) model for rapid real-time monitoring of changes in moisture content of banana, carrot, and a pleurotus eryngii. These studies show that integration of non-destructive and computer vision techniques with existing dryers can facilitate automation in fruits and vegetables drying industries as well as can reduce the overall operational cost.

5.4.3

Automation in Packaging and Supply Chain

Fresh fruits and vegetables trigger a series of stress-related physiological changes after harvesting and exhibit a greater variance compared to processed products, which complicates their automatic packaging and control over quality during the supply chain. Before transportation or sale, some fruits are usually placed into packaging trays or into cartons, which requires manual inspection of visual defects and correct orientation of fruits, for example, the direction of the stem. The manual inspection of such type of operation is tedious and time-consuming and even increases the overall operational costs due to the low level of automation. Giefer et al. (2020) used a combination of line laser and charge-coupled device (CCD) camera for constructing three-dimensional images and developed a convolutional neural network (CNN) model based on an automated orientation determination system. However, further studies are required for the complete automation of fruits packaging lines in industries. The cold supply chain is usually preferred for storage and long-distance transportation to avoid postharvest losses by maintaining the metabolic activities of fruits and vegetables. For proper functioning, the cold chain needs desired controlled atmospheric conditions in the supply chain. Automatic real-time monitoring of temperature and humidity is important to improve the transparency and assure the quality of fruit and vegetables during storage and cold supply chain. Today, radiofrequency identification (RFID) technology in combination with wireless sensors is extensively used for the automation of traceability, management of supply chain, monitoring of cold chain of moving goods and retails (Biji et al. 2015). RFID is a wireless sensor-based automatic identification technology that identifies items and gathers data without human intervention. In this technology, information can be retrieved from the database using identification numbers stored in RFID tags which can be useful for taking necessary action. RFID tags are in-built with specific hardware and software which offers real-time monitoring, environment sensing, tracing, and tracking automation in the supply chain. According to the requirement of the fruits and vegetables supply chain, RFID tags can be embedded with specific sensors, such as moisture sensor, temperature sensor, humidity sensor, turbulence sensor, and placed inside the boxes and containers for real-time monitoring of temperature, humidity, pH, shock/vibration, and the presence of light. Moreover,

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RFID has an advantage over other conventional technologies, such as barcode and data loggers, as it does not require visual contact, and information can be read over 100 m of distance (Kumari et al. 2015). Nowadays, gas sensing technology is also gradually evolving to get more precise information about the quality of products during the supply chain as they can detect or sense the changes in volatiles, such as ethylene or acetaldehyde, formed due to the metabolic activity of fruits and vegetables (Wang et al. 2021). However, gas sensing technology needs further developments for its commercial application in real-time monitoring of the supply chain.

5.4.4

Automation in Waste Management

Wastage is one of the major problems in fruit and vegetable processing industries across the world which restricts their availability or increases shortage in the market. Here the term “wastage” includes the life span of fruits and vegetables which ranges from the production to its edibility for consumers. Therefore, sensor-based management systems are employed to sense the quality of products and assure it is available to the customer’s prior degradation. Today, wireless sensors are in higher demand to reduce the spoilage of fruits and vegetables by continuous monitoring of quality till it efficiently reaches the customer. Wireless sensors in integration with IoTs increase the resource knowledge among the stakeholders by remotely providing real-time status of perishable produce and thus, allowing them to take timely decisions in the supply chain (Sangeetha and Vijayalakshmi 2020). RFID-based sensing technology is widely used for remotely trace and track storage conditions and the supply chain of fruits and vegetables and minimize the losses by timely taking necessary action.

5.5

Dairy Industry

The dairy industry is one of the largest growing food sectors, holds six billion consumers around the world (FAO 2020). However, the profit of the dairy industry is going down as per the demand for milk which is due to the problems related to milk hygiene, accuracy, production rate, etc. Milk processing in the dairy industry includes different workstations to perform various activities such as livestock management, milk collection, disinfection, packing, storage, assembling, transportation, etc. Conventional processes are not enough to handle bulk processing efficiently as it requires huge manpower to perform various activities. However, automation of processing facilitates operators/humans to perform multiple tasks within the best time management (Sain et al. 2020). Therefore, depending on the processing application requirements different levels of automation such as fixed, programmable, or flexible automation are adopted in the dairy industry. The commercial application of robotics and automation in the dairy industry is ranging from monitoring of milking

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animals to retail vending of milk for end consumption (Meshram et al. 2018). The automation at different levels of the dairy industry is as follows:

5.5.1

Automation in Livestock Management

Livestock management is one of the laborious tasks of the dairy industry which requires skilled and dedicated manpower for feed, water arrangements, and assessing the health of farm animals. The advancement in artificial intelligence (AI) and computer vision (CV) technologies provided opportunities for automatic surveillance of the behavior and needs of each animal. The manual inspection of animal body conditions and other health problems is subjective, time-consuming, and often requires experienced employees. Furthermore, finding and treating lameness and/or other health problems in cattle is very important for farmers, as these are associated with milk safety and productivity. The identification of such problems in animals at a herd level requires a trained observer; however, it often remains undiagnosed until the problem has become severe. An artificial intelligence-based machine vision system reads ear tags to identify facial/muzzle/coat pattern features of animals for identification (Halachmi and Guarino 2016). A 3D vision system can automatically diagnose the behaviors associated with lameness or other health issues of the animal using locomotion scoring and body condition scoring algorithm and alert farmers on a real-time basis to take necessary action. However, the automatic measurement of animal behavior using a 3D vision system is still in its early development stage and requires more research for efficient application at animal farms (O’Mahony et al. 2019). Proper feeding of cattle is another big challenge in farm management as it is associated with animal health and milk production. A conventional feeding system (CFS) is tedious, requires high manpower for mixing and placing the feed for each animal, and has a rigid work schedule. However, in large farms, mixing and distribution of feed are usually done through tractors. Today, many dairy farms are shifting from a CFS to AFS (automatic feeding system) to reduce the overall operational time. AFS includes a stationary feeding hopper, a mixing unit, and a distribution wagon operating on a rail. The main advantage of AFS over CFS is low labor requirement with a possibility of high feeding frequency (Da Borso et al. 2017). The accurate and timely identification of the estrus cycle of an animal at the farm level is another important aspect of livestock management. Traditionally, the estrus cycle of dairy cattle is identified by repetitive monitoring of animals at standing state while being mounted. It requires a considerably trained and experienced farmer to achieve a reasonable level of efficiency in identification. Several automated estrus identification systems have been developed to overcome the limitations associated with traditional methods. Automated estrus monitoring technologies can identify even slight changes in animal behavior during both day and night, and recommend optimal insemination time to the farmers. However, the low rate of adoption of such

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automated technologies by farmers is due to the lack of knowledge about the benefits of investing in such detection technologies (Adenuga et al. 2020).

5.5.2

Automation in Milking of Dairy Animals

Milking of animals is a challenging task due to the rapidly increasing number of animals and hygiene conditions during the handling of milk, mainly at the farm level. In a conventional process, a substantial number of trained human resources is required for milking a large number of cattle. It affects the overall profitability due to slow production rate, compromise with hygiene conditions, and high labor cost. Dairy industries are shifting towards automation and robotics-based milking processes to control problems associated with conventional milking. Robotics in milking facilitates adequate hygiene with high production efficiency per cow and reduces the labor cost of processing (Sain et al. 2020). Milking robots are integrated with lasers and vision system technology to automatically locate the cow teats and extract milk from them. In western countries like Sweden, robotic milking of animals received a wider acceptance. A Swedish dairy equipment company established the world’s first commercial laser technology-based robotic milking rotary with a milking capacity of 90 cows/h. Such type of robotic system performs multiple tasks ranging from the milking of animals to precisely filling of milk in retail containers without human intervention (Meshram et al. 2018).

5.5.3

Automation in Cleaning and Hygiene of Equipment and Working Area

Cleaning and hygiene of equipment and working area are the most important aspects in the dairy industry. Conventional cleaning of plant equipment involves human interactions which are time-consuming and prone to hygiene risk. However, today most of the dairy industries implemented automatic or semi-automatic cleaning-inplace (CIP) systems to achieve a higher hygiene level of production facilities without dismantling the system. CIP is defined for cleaning and sterilizing processing equipment mainly holding tanks, valves, and pipes using cold and hot water, and acid and base solutions with high energy efficiency. Automated CIP is a sensorbased centralized cleaning system in which the operator can select the desired washing program or alter the program and/or increase the number of the washing cycle according to the requirement, through digital PLC and SCADA (supervisory control and data acquisition) system (Kale et al. 2017). The CIP system reduces the requirement of water and detergent for cleaning of dairy equipment by reducing flushing time and/or reuse flushed water and detergent (Dhage and Dhage 2016).

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Automation in Quality Testing

Milk quality inspection in the inbound supply chain is important to produce premium quality milk products. However, the existing milk quality monitoring systems are based on manual testing of milk in labs located at the processing plants. Therefore, milk supplies are mixed before reaching quality testing to ease the transportation and delivery system (Sain et al. 2020). Today, several IoT types of tools and sensors are under development for automatic monitoring of milk quality at the farm level before pick up. Ahmad and Jindal (2006) developed a mathematical model for automatic rapid online assessment of raw milk microbiological quality. The estimated standard plate count and methylene blue reduction rate by measuring the change in output voltage through a specially designed light-sensing probe and a mathematical model. Near-infrared (NIR) spectroscopic sensing systems could be another effective tool for assessing the quality of milk from an individual cow during milking at dairy farms. Iweka et al. (2020) developed a NIR spectroscopic sensing system and successfully assessed the major constituents of non-homogenized raw milk, i.e., fat, protein, lactose, solid not fat (SNF), milk urea nitrogen, and somatic cell count using at a wavelength ranging 700–1050 nm. The further development and application of such technologies could be helpful for farmers in getting real-time information of milk quality and physiological condition of each animal and for managing farms more efficiently.

5.5.5

Automation in Packaging

Today, robots and sensors became an important part for automatic packaging of fluid milk and milk products. Robots are mainly used for picking and placing the milk from one place to another. However, sensors are integrated for real-time monitoring of package position during filling and sealing as well as for signaling the machines and robots when to perform the activity. The most commonly used robots in milk packaging are as follows: 1. Articulated robots: These are usually six-axis robots having ten or more interacting arms, providing more flexibility in milk packaging operations like filling, sealing, handling, assembling, etc. (Sain et al. 2020). 2. Delta robots: It is the category of modern-day robotics also known as Parallel Link Robots. These types of robots are employed to work in extreme conditions, such as very high or low temperature and pressure conditions, where manual work is not possible to get efficient output (Meshram et al. 2018; Sain et al. 2020). 3. SCARA (selective compliance assembly robot arm): These are stationary robots with movable arms which perform certain functions, pick and place operations, with a very high speed and accuracy. In the dairy industry, such types of robots are used for picking, placing, and rearranging the milk containers (milk bottles,

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packets) according to the need in the processing line (Meshram et al. 2018; Sain et al. 2020). 4. Palletizing robots: Palletizing robots, e.g., Kuka robots, are used for arranging the products according to prefixed angle during packaging and labeling. These robots are also used for palletizing operations in cold stores, freezers, removing frosting, etc., where manual palletizing is difficult due to extreme conditions (Abdeetedal and Kermani 2019; Sain et al. 2020).

5.5.6

Automation in Retail Milk Distribution System

In a conventional system, raw milk is often distributed at the doorstep to regular customers either early morning or/and nighttime by the milkman and also sold at milk stores. The selling of milk at milk stores often results in long lines hanging tight for getting milk. To avoid the big queue for collecting milk and paying money at milk centers, an automatic milk vending machine is one of the recent advancements in the retail milk distribution system. Initially, the automatic milk vending machines were integrated with sensor-based milk measuring and dispensing systems with currency recognition and collection systems. However, later the RFID-based userfriendly smart milk vending machines were developed for cashless transactions and eliminate human intervention (Manmohan et al. 2019; Vijayaragavan et al. 2020). Smart vending machine card systems are used for cashless transactions and dispensing of milk. The cards used in smart vending machines are embedded with RFID prepaid tags, which could be recharged, and an RFID reader present at the milk vending machine detects this card identity for payment before dispensing the milk. However, such types of milk vending systems are currently not in use due to some associated milk safety risks and awareness among farmers and customers. Tremonte et al. (2014) reported that the refrigeration conditions applied to milk stored in automatic vending machines could not guarantee its microbiological safety. Giacometti et al. (2012) highlighted that many consumers did not carry raw milk at home in insulated bags and even transport time often exceeds 30 min. Hence after carrying the milk at home, it must be heated or boiled for sufficient time before consumption. Furthermore, the lack of awareness among farmers and customers, lack of processing and marketing capacities, the difficulty of networking and collaboration with other key holders are also some of the major reasons for the failure of milk vending machines (Pereira et al. 2018).

5.6

Meat, Poultry, and Seafood Industry

With increase in the population, the demand for meat and poultry has also seen a substantial surge. Similarly, there is also an increase in seafood consumption (Abad et al. 2009) and fish consumption which may also be attributed to their nutritional

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properties. Meat, poultry, seafood, and fishes are also quite perishable thereby requiring newer innovative technologies to help increase their shelf-life and also to help ramp up the production to cater to the increasing demand. To increase the production, the manufacturers have started investing in technologically advanced machinery due to the above-said factors and many other factors such as shortage of labor and increasing competition among companies, etc. (Chooi et al. 2013).

5.6.1

Automation in Meat Processing

An increase as high as +25% in productivity has been observed by food industry manufacturers when the work done by humans was replaced by using robotics (Iqbal et al. 2017). Robots have started to be used in different areas in the meat industry such as meat processing which includes meat cutting, animal slaughtering, and meat selection (Chooi et al. 2013; Khan et al. 2018). In India, many types of machinery from basic ones such as grinders, mixers, bowl choppers, tumblers, etc. to some advanced ones such as an automatic machine for deboning, patty making machines, multi-needle injectors, etc. are now being used (Zhang et al. 2017). Many other automatic types of machinery such as hide pullers as well as robotic carcass splitters are not being replaced by manual operation as they increase production (Khan et al. 2018). To determine the cutting position based on skeletal structure, imaging of vision techniques such as X-ray, Tera-hertz scanning, ultrasound, etc. are required (Chooi et al. 2013; Kohler et al. 2002). The production line also utilizes metal detectors along with X-ray machines which are used to detect the nonmetallic contamination such as bone, glass, fibers, and plastics (Caldwell et al. 2009). Another latest trend in the manufacturing process is to coat the conveyor belt with blue habilene. This modified polyolefin containing antimicrobial additives can be immensely useful to prevent the development of bacterial biofilms such as staphylococcus, salmonella, etc. on the conveyor belt surfaces (Mahalik and Nambiar 2010). Certain digitized Food Waste tracking systems based on Internet of things (IoT) have also been developed and utilized in the meat and the poultry sector which help in tracking and reducing food wastage significantly (Jagtap and Rahimifard 2019). Many artificial intelligence techniques have also been designed to grade eggs. Artificial neural networks and certain algorithms have also been used to determine egg properties such as volume, weight, and size which help in egg grading. Image processing has also been explored in this area where digital cameras are utilized to capture images of candled egg in a dark room and this image is further used for egg grading (Thipakorn et al. 2017) and has also been able to detect blood spots and cracks (Omid et al. 2013). Usui (2003) also suggested that near IR spectroscopy can be utilized to find out blood spots in eggs as the hemoglobin particles in the blood will have a different absorbance band and thus can be detected. Another technique that can help detect cracks in the eggs is the acoustic impulse technique. This technique involves the use of a pendulum that strikes the egg producing an acoustic

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signal which is received with the help of a microphone and then amplified. Digital signal processing is then done by transferring it to a computer and further egg quality is determined with the help of neural network or regression models (Cho et al. 2000; Omid et al. 2013). 3DP (Three-dimensional printing) has also made its way in the meat industry. There are generally very few cuts in the meat considered of higher quality. Small off-cuts and trimmings are many times considered as waste or low-value products. Hence, in order to utilize such meat cuts, 3DP which utilizes computer-aided design (CAD) software to fabricate customized meat is gaining importance (Dick et al. 2019). As a result, many researchers are working continuously to produce even better 3DP for these fibrous food products.

5.6.2

Automation in Safety and Quality Control

Maintenance of meat quality even during its storage has utmost importance and thus automation can help provide a great opportunity for quality control. One relevant method to determine the freshness of meat during storage can be by determining the concentration of two main metabolites produced by microbial decarboxylation of amino acids, i.e., biogenic amines and sulfurous compounds (Li and Suslick 2016; Xiao-Wei et al. 2016). A significant measure of meat deterioration will thus be to measure the emitted relevant volatile organic chemicals (VOC) (Liu et al. 2015; Salinas et al. 2012). For the same, several advance analytical methods such as FT-IR spectrometry (Chae et al. 2015; Ellis et al. 2004), GC-MS (Nicolaou et al. 2012; Sirocchi et al. 2014), HPLC (Argyri et al. 2011), and even chemifluorescence (Gao et al. 2016; Hu et al. 2016) have been utilized. For the same purpose, one of the latest technologies which has come into use is the electronic nose which is an instrument consisting of an array of chemical sensors which are capable of detecting odors (Musatov et al. 2010; Wojnowski et al. 2017). Even biosensors play a similar role in detecting the amines in the meat, prawn, and fish (Mello and Kubota 2002). Indicator devices which are integrated with the smart packaging are also used. The most common in the meat industry is time-temperature indicators which have been used for meat, poultry, and seafood as quality indicators (Lu et al. 2013). They provide visual information about the safety of food for consumption by irreversible color change as they monitor and record the thermal history of a food (Ahmed et al. 2018).

5.6.3

Automation in Traceability

Many automatic identification systems are also being used these days especially for chicken and poultry (Khashman 2012). TAG systems are being used for seeking information such as meat origin and animal traceability which utilize the technology

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of DNA fingerprinting. At present, the most common techniques being used include radio frequency identification system (RFID) and barcoding and utilize unique ID tags which are printed and then attached to packaging material. These tags store and retrieve the data using radio waves. These RFID tags consist of two parts—an antenna that receives and transmits the radio signal and an integrated circuit which stores and processes the RF signal (Cheruvu et al. 2008). These RFID tags have also been useful in the fish logistic chain where they can be kept in boxes containing the fishes. They can help detect temperatures below 0  C and the data can be read during any time of the logistic chain without opening the boxes and they include relative humidity as well as temperature sensing abilities (Abad et al. 2009).

5.6.4

Automation in Packaging

Automation machines are being used even for packaging. To extend the shelf-life of meat products, many techniques such as freezing, canning, the addition of preservatives, chilled storage as well as vacuum packaging/modified atmosphere packaging are being used (Arvanitoyannis and Stratakos 2012). However, among all these technologies, vacuum packaging/MAP has gained the most interest recently with the increasing interest in minimally processed food (Paramithiotis et al. 2009). The antimicrobial properties of CO2 help increase the shelf-life of meat. Also, MAP is known to maintain and sometimes even help improve the color of meat which is one important quality attribute for the consumer. The three main gases used in MAP which are oxygen, carbon dioxide, and nitrogen can be used in the following ways— (1) inert blanketing using N2, (2) semi-reactive blanketing using CO2/N2 or O2/CO2/ N2, or (3) fully reactive blanketing using CO2 or CO2/O2 (Arvanitoyannis and Stratakos 2012). Indicators such as Integrity indicator which can help indicate any leak in the package through visual colorimetric changes can be used along (Ahmed et al. 2018). Active packaging includes the incorporation of certain components in the package which either release certain substances into the packed food or surrounding atmosphere or absorb certain substances from the food which is packaged or from the surrounding atmosphere to help extend its shelf-life as well as to maintain its safety, quality and sensorial attributes. Certain active packaging systems which find use for meat products include moisture absorbers, carbon dioxide generators/ absorbers, antimicrobial agents as well as oxygen scavengers. Oxygen scavengers have been found immensely useful to prevent the growth of molds as well as the aerobic microorganism and also prevent meat discoloration by preventing oxidation of flavors and pigments. Similarly, in order to maintain an optimum CO2 concentration, CO2 absorbers/releasers are used. With the absorption of CO2, meat products tend to exudate liquids and thus moisture absorbers such as absorber pads placed in the package also play an important role. To impart antimicrobial properties sulfites, nitrites, etc. are also utilized (Arvanitoyannis and Stratakos 2012).

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Smart packaging such as the use of nanotechnology in the packaging is also drawing attention. The poisonous E. coli 0157 can be detected in meat with the use of nanoscale silica spheres which are filled with fluorescent dye molecules. Even Salmonella bacteria in the meat can be detected with surface chemistry skills, immunoassay techniques as well as integrated optics incorporated in biosensors (Mahalik 2009). The use of robotics is also slowly making its way in food processing and among one of the earliest uses is the packaging of meat products which is being utilized in Western Europe to package fresh meats into trays (Mahalik 2009). Many other machines such as wrapping machines and automated thermoform-fill-seal systems which help wrap and label fresh meat, fish, etc. and produce skin tight packaging, respectively (Cheruvu et al. 2008).

5.6.5

Challenges

The adoption of these automations which undoubtedly come with numerous benefits has many constraints too, one of the biggest one being difficulty in justifying high initial cost restricting a lot of medium-scale companies and small companies to switch to highly efficient and automated machinery or even smart packaging with indicators and sensors. Many other factors make the adaption of automation in food difficult as compared to other industries such as the variable composition of food products as well as its perishable nature. This especially holds for automation in the meat industry as just like humans, every carcass varies in size and shape (Templer et al. 2012). Technical reasons such as lack of skilled technical personnel along with certain other factors such as time, management commitment, and cost also hold food manufacturers aback. In a survey by Ilyukhin et al. (2001), time and cost were identified as the biggest obstacles for adopting newer technologies for small production plants whereas cost along with management commitment was the biggest obstacles for larger production plants. Another challenge remains to select the bestfit technology for your industry. The responsive surfaces of these in touch with the food material also have to be completely inert to food and non-toxic which again rules out the possibility of using some possible options such as sensing devices based on metal or carbon nanoparticles (Muncke 2014; Parisi et al. 2015). The use of biosensors and e-nose possess a number of challenges such as the results being affected by the ambient gas as well as due to sensitivity of these sensors to changes in the relative humidity and temperature. Electronic noses based on mass spectroscopy can be used to help overcome this problem. However, other than this, a few more disadvantages with the use of e-nose persist requirement of frequent calibration, consumption of high power, insufficient stability in measurement, as well as changes in the chemical sensor’s response signal becoming less reliable with the passage of time (Wojnowski et al. 2017).

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Barcodes also possess problems as they can provide inaccurate data due to reasons such as mist, dirt, and out of line-of-sight. Hence, RFID tags are becoming more popular as they can withstand harsh conditions (Mahalik 2009). However, problems such as lack of standardization and cost of tag remain an obstacle (Abad et al. 2009). Egg graders based on artificial intelligence techniques also possess certain errors such as 100% accuracy is rare and a certain percentage of eggs may be misgraded, detection may be difficult for brown eggs, etc. (Omid et al. 2013). All these also involve the use of complex mathematical models, digital cameras, computers, and neural networks; one needs to have proper knowledge of all the related technical needs. 3DP, though an emerging and interesting automation, comes with its drawback when it comes to meat and meat products. Meat is a non-printable fibrous material in nature (Liu et al. 2018) and thus requires prior modification in its mechanical as well as rheological properties. Hence, in order to convert it into extrudable flow-like material, the addition of certain flow enhancers such as gelatin solution is required (Dick et al. 2019). These problems, however, can be overcome by more studies and research. Once the problems are overcome, these techniques will surely help in the advancement of the meat industry and help to meet increasingly higher demands in the most efficient way.

5.7

Beverage Industry

The beverage industry is growing continuously across the world with increasing demand for convenient foods and an increasing population. The beverage industry is a subset of the food industry (Guimarães et al. 2012) and comes under the FMCG, i.e., the fast-moving consumer goods category. The increasing demand is common to all the beverages such as tea (Majuder et al. 2010), coffee (Murthy and Naidu 2012), fruit juices (Priyadarshini and Priyadarshini 2018), alcoholic beverages (Jernigan 2009), milk (Douphrate et al. 2013), etc. Numerous factors such as efficient use of resources and energy (Otles and Sakalli 2019), lack of labor (Paraschos et al. 2013), cost reduction, hygienic production, high productivity, etc. accelerate the use of automation and modernized machinery in the beverage industry. As per the 2015 executive summary of World robotics, there was a tremendous rise of 27% in robot order for the food and beverage industry in the year 2015 (Khan et al. 2018). Thus, the latest industrial revolution, i.e., Industry 4.0 is making its way even in the beverage industry. This is promoted by the reduction in the cost of RFID, wireless communication, sensor networks, NFC (near field communication), and applications making their utilization more accessible in the beverage industry (Otles and Sakalli 2019).

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Automation in Beverage Processing

One of the biggest shifts in automation and advancement is observed in the dairy sector. A new innovative technology such as robotic milking to automatic feeding systems is now being utilized. Electronic cow identification along with temperature, movement, and location sensors can help provide important information such as the reproductive health of the animal, the amount of feed to be given, etc. Automation is also finding its way for tea plucking as it is a tedious, labor-intensive, and timeconsuming task; the possibility of using an automatic harvesting system that can maintain the same level of quality as manual picking is continuously being explored. Hence, robots are being trained for the Oritsumi technique of leaf picking, i.e., using the thumb along with the forefinger to grasp the leaf and pluck the leaf while rotating the hand (Paraschos et al. 2013). The production process of both soft drinks and beer shares few common features. Specialized mixing tanks are used along with many other sophisticated types of machinery which have different functions such as getting the perfect recipe with the controlled flow of ingredients, carbonation machines, etc. Towards the end, a filling line consisting of series of machines and conveyor belt performs all other function such as washing as well as disinfecting the containers followed by filling, sealing, and labeling the soft drinks or beer packed in glass bottles, PET bottles or cans of different sizes (Guimarães et al. 2012; Tsarouhas and Arvanitoyannis 2010). IoT, i.e., Internet of things-based systems also find use in different areas of the processing to collect, store, handle, and analyze data. One such example can be a reduction of energy wastage in a beverage company by using IoT based smart energy systems (Jagtap et al. 2019). Even when it comes to alcoholic beverages such as beer, technology is now being used at almost all steps such as malting, fermentation, etc. Advanced computer vision, image analysis techniques, IoT at various steps, sensors, etc. are being utilized (Violino et al. 2020). MRI, i.e., magnetic resonance imaging technique is also useful in determining characteristics of concentrates, pastes as well as fluids (Mahalik 2009). Biosensors also play a crucial role in the beverage industry in numerous areas. They can help detect acetaldehyde glycerol and aldehyde for monitoring fermentation of alcoholic beverages, ethanol content of beer and wine, lactose content in milk, fructose in milk, wine, juice, and cold drinks, glucose in soft drinks, milk, juice, wine, etc. (Mello and Kubota 2002). Factors such as the type of coffee, its cultivation place, etc. can all affect the taste and sensory properties of the coffee. Electronic noses or sensors can be integrated into the humanoid robots making it possible for them not only for the production but also for quality control of coffee. This method is called coffee-cupping and it involves the use of ANN, i.e., artificial neural network. It can even give information regarding the acidity level of coffee and can even take into account the effect of temperature on volatiles and thus coffee odors (Thazin et al. 2018).

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Automation in Quality Control

Automation finds numerous uses in quality control of products as well. X-ray technology can be used with a multi-layer detection algorithm to detect contaminants such as metal, stone, etc. in beverages packed in metal cans, plastic or glass in order. Biosensors can help sense penicillin in milk trucks while it is heading to the dairy processing unit (Mahalik and Nambiar 2010). Optimal immune-biosensors capable of detecting streptomycin residues (antibiotic compound) in milk are also used (Baxter et al. 2001). Many other sensors have been used in order to detect antibiotic and pesticide in milk, pesticide in juices, oxalate in tea, phosphate in drinking water, sulfite, and glycerol in wine, polyphenol in wine and green tea, ethanol in beer and wine, citric acid in fruit juices and sports drink, etc. Biosensors also find a huge application in monitoring aroma in alcoholic beverages such as brandies, gin, wine, etc. (Mello and Kubota 2002). The growth of microorganisms in milk can lead to production of metabolites causing sensory alteration in the milk such ass off-flavor and odor. These sensory alterations can be used to detect and enumerate microbial spoilage by many techniques such as electrical and microscopy methods, nucleic acid probing, polymerase chain reaction, immune assays, ATP bioluminescence as well as electronic nose. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is yet another technique that helps quantify bacterial contamination of milk (Nicolaou et al. 2012). The temperature of milk plays an important role in maintaining its acidity and thus quality. Therefore, indicators such as timetemperature indicators (TTIs) play an important role in many beverages especially in milk to detect the quality (Lu et al. 2013).

5.7.3

Automation in Traceability

When it comes to traceability, barcoding and RFID tags play an important role for beverages. RFID tags are, however, advantageous over barcoding as they can be read automatically using sensors (Mahalik and Nambiar 2010). Mu-chip tags which are the world’s smallest RFID tag (50 mm) help guarantee the genuineness of valuable products such as wine or liquor which are normally open to counterfeit abuse. This is done by integrating the mu-chip in the seal cap, where a reader can read the tag and ensure that no tampering has been done with the bottle’s content. Also, the removal of the seal cap leads to breakage of the antenna, and the chip’s unique ID number which is stored in the ROM, cannot be read again. This helps in preventing the reuse of the bottle once emptied as well (Jones 2006).

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Automation in Packaging

Robots also find use in the packaging, form-fill-seal machines are utilized for packaging milk in LDPE pouches, capping machines which are capable of capping 200 plastic or glass bottles per minute, automatic cartoning machines which can produce different size boxes, automatic labeling machines (Cheruvu et al. 2008), etc. are all used immensely in the food beverage sector. Cleaning, counting, filling as well as arranging of the bottles on the conveyor belt are all performed by automatic robots in the beverage industry (Iqbal et al. 2017), e.g., beer, flavored milk, etc. Palletizing robots that can accurately and precisely help in loading and unloading objects are also used and are ideal to be used even in cramped spaces and can handle up to four production lines and multiple products simultaneously. X-rays are used with an artificial neural network for recognition of shape which helps find underfilled as well as defective packs (Mahalik 2009).

5.7.5

Challenges

In a survey by Drewry et al. (2019), it was found that as low as 1.5% of the surveyed dairy farm workers were using robotic milking machines, pointing that many barriers still exist in adapting this automated machinery despite their numerous advantages. The reasons identified for the same were privacy/security concerns (61%), ability to keep up with technology change (55%), lack of comfort with technology (54.5%), and many other reasons such as poor infrastructure, lack of interest, etc. along with demographic factors such as age, finance, etc. One more source reveals that the concept of Industry 4.0 is still not known to around 94% of the manufacturers pointing clearly towards the need to increase awareness regarding the same (Otles and Sakalli 2019). Small food manufacturers often lack the funds and employees to switch to automation and digitization. Techniques such as ATP bioluminescence, polymerase chain reaction, etc. have slow sample turnaround times and require skilled labor which limits their use (Nicolaou et al. 2012). The issue with using robots such as the ability of the robot to work in wet environment, complexity, non-corrosive nature, and washable when it comes to coming in direct contact with food are few constraints (Khan et al. 2018). Another example here can be training robots for tea plucking where factors such as maturity of leaf, petiole, branch stiffness, etc. will affect the amount of the plucking motion required and the time of rotation of leaf and thus will require advanced systems such as Probable Movement Primitives (ProMPs) for acquiring the same (Paraschos et al. 2013). Moreover, robots work as per predestined functions and they lack the decision-making ability which is needed under certain circumstances. Other challenges continue to hold food manufacturers aback from upgrading completely to Industry 4.0. Problems such as lack of proper infrastructure and

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internet availability in all the areas allowing to utilize IoT and related techniques truly as they are meant “anywhere, anytime” still is somewhat far and continuous up-gradation for the same is required. However, a lot of advancement in automation has taken place in the beverage industry. Overcoming the present remaining challenges thereby being able to use the technology and machinery to their full potential in order to increase production to meet the rising demand of the increasing population and for many other numerous benefits they offer is the current challenge and need.

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Chapter 6

Non-thermal Food Preservation Technologies Ravneet Kaur, Shubhra Shekhar, Sahil Chaudhary, Barinderjit Singh, and Kamlesh Prasad

Abstract Recent food processing trends and preservation technology mainly focus on retaining freshness and minimizing nutritional and sensory losses during processing. Conventional processing techniques involve high temperature (thermal processing) for microbial inactivation and food preservation. Exposure to hightemperature results in the loss of heat-sensitive nutritional components and affects textural and sensory characteristics of foods. Therefore, to obtain high-quality minimally processed food products, non-thermal techniques are found to be better. Standard non-thermal preservation techniques include high-pressure processing, pulsed electric field, cold plasma, supercritical carbon dioxide, irradiation, and ultrasound. This chapter focuses mainly on the principles, processing, and application of non-thermal techniques in food preservation. Keywords Non-thermal food preservation · High-pressure processing · Pulsed electric field · Cold plasma · Supercritical carbon dioxide · Irradiation · Ultrasound

6.1

Introduction

Food preservation, safety, and quality are the significant goals of food processing industries to meet consumer demand as per the recent trends. Commonly used traditional food processing techniques involve thermal treatment for improving the production rates and shelf-life extension. Thermal processing is required to get the desired characteristics in processed food products but involves higher temperature

R. Kaur · K. Prasad (*) Department of Food Engineering and Technology, SLIET, Longowal, Punjab, India S. Shekhar Department of Food Process Engineering, National Institute of Technology, Rourkela, Odisha, India S. Chaudhary · B. Singh Department of Food Science and Technology, I. K. Gujral Punjab Technical University, Kapurthala, Punjab, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_6

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exposure, which leads to the loss of various volatile components and nutritional properties (Singh and Prasad 2012; Kumar and Prasad 2018; Kaur and Prasad 2021a, b). Therefore, to meet the consumer requirement for minimally processed and fresh food products, the focus is on utilizing processing techniques that can help to retain maximum nutritional and textural properties of food products during processing (Pereira and Vicente 2010). Recently, there has been an increased focus on non-thermal technologies for the processing and preservation of food products due to their economic benefits. They are more energy-efficient, have minimal impact on nutritional and sensory attributes of food products than conventional techniques, and improve shelf-life (Knorr et al. 2011; Morris et al. 2007). In addition, novel non-thermal technologies also offer environmental sustainability and play a vital role in food security by minimizing the energy and water requirement, thus reducing the water and carbon footprint (Khouryieh 2021; Knoerzer et al. 2015). Major non-thermal food processing/preservation technologies include highpressure processing (HPP), pulsed electric field (PEF), cold plasma, supercritical carbon dioxide, irradiation, and ultrasound. Low power ultrasound waves are commonly used for analytical purposes, whereas high power ultrasound waves are used for processing purposes (Prasad 2015a, b). These technologies do not involve direct heat treatment but may lead to an increase in the thermal energy of the food product. Thus, these may also be described as indirect thermal energy input processing (Ezeh et al. 2018). Besides the benefits of preventing thermal degradation linked with thermal processing, they are also responsible for destroying the spoilage-causing microbes by disrupting the cell membrane structure or destroying the genetic material within the cell. These techniques are also used for various other unit operations like freezing, drying, emulsification, sterilization, and extraction (Zhang et al. 2019; Sharma et al. 2015). Each technology has a different mechanism that determines its effect on food structure and quality. A combination of two or more techniques may also be simultaneously used to improve the effectiveness of the treatment. This chapter describes the mechanism of different non thermal processing techniques, its effect on food quality, and its role in food preservation.

6.2

High-Pressure Processing (HPP)

High-pressure processing is a promising novel non-thermal processing and preservation technique used for application in the food industry. It involves the application of high pressure instead of heat to inactivate or destroy spoilage-causing microorganisms. It is also known as high hydrostatic pressure (HHP) processing and ultrahigh pressure (UHP) processing. The first application of HPP was carried out to process milk to reduce the microbial load and increase the shelf life (Hite 1899). Milk was exposed to 600 MPa of pressure for 1 h, resulting in the delayed souring of milk. Afterward,

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the application of HPP for processing juices, milk, meat, and egg albumen was also studied by various researchers (Rendueles et al. 2011). In this technique, the food products are exposed to pressure from 100 to 1000 MPa. Water is commonly used as a medium for force transmitting for a specified time interval that can vary from a few seconds to minutes depending on the type of food material. Apart from processing and preservation, HPP can also be used for textural changes in various fruits, vegetables, and meat products. It is also helpful for carrying out the reactions where modification of chemical structures must be carried out (Norton and Sun 2008; Tao et al. 2012).

6.2.1

Principles of HPP

High-pressure Processing governs mainly on Le Chatelier’s principle and the Isostatic/Isobaric principle. According to Le Chatelier’s principle, the alteration in pressure at constant temperature leads to shifting the equilibrium towards the state that favors reducing volume. It is also known as equilibrium law. Due to high pressure, the volume for the same mass is reduced due to molecular ordering. Strong covalent bonds are not affected by HPP at low temperatures, but the damage is caused to the weaker bonds. Due to this reason, the quaternary and tertiary protein structures are affected by HPP, and there is no effect on primary and secondary structures (Muredzi 2012). The isobaric principle states that pressure is distributed uniformly over the product irrespective of its shape and size (Torres and Velazquez 2005). On the other hand, in the case of thermal treatment, heat transfer occurs through different modes: conduction, convection, and radiation. For products containing high moisture content, the pressure does not impart any changes in the structural configuration of the product. In contrast, the large air spaces products get deformed during processing due to compressibility differences (Orellana et al. 2017). Therefore, HPP’s effectiveness depends on pressure, holding time, and resistance of microbes towards high pressure. Some microorganisms are more resistant to high pressure than others because of differences in cell structures (Rendueles et al. 2011).

6.2.2

High-Pressure System and Processing

HPP system comprises the following parts (Hokmollahi and Ehsani 2017; Rastogi and Knorr 2013): • Treatment chamber. • Pressure generating system/pressure intensifier. • Process control system.

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Fig. 6.1 Schematic diagram of high-pressure processing system

• Pressure and temperature monitoring system. • Temperature measurement system. High-pressure processing can be carried out in batch or semi-continuous systems. Batch systems are mostly used for solid as well as liquid foods. Food is packaged in flexible packaging material and loaded in the pressure chamber, and the vessel is closed during the process (Fig. 6.1). The pressure transmitting medium is then filled inside the chamber for pressurization. Pressure is applied either by reducing the volume of the pressure vessel using a piston or by pumping more of the pressure transmitting medium into the vessel. After the desired pressure is achieved, it is maintained for the specified time, and this step is known as holding. After the completion of holding time, depressurization is carried out, and unloading of products is done (Balasubramaniam et al. 2008). Thus one “cycle” means the total time utilized for pressurization, holding, and depressurization. The most used pressure transmission medium is water due to its non-toxicity and low cost. However, some other media such as silicon oil, castor oil, corn oil, ethanol, and glycol blends are sometimes used for anti-corrosion and lubrication purposes. Different fluids have different compression heating values depending on the physical and thermal properties such as viscosity, specific heat, and compressibility. This affects the heat transfer across the fluid and the food product (Balasubramanian and Balasubramaniam 2003). However, during the process, the pressure is transmitted simultaneously and uniformly in all directions, so there is no effect on structural

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properties. Also, the sensory and nutritional characteristics of food are retained due to minimal thermal effects. Semi-continuous systems are used mainly for liquid food products that can be easily pumped like juices. In this system, three pressure vessels are connected. When one vessel is unloaded, the second vessel is under pressurization or compression, and the third vessel is loaded so that a continuous output is obtained. The unloaded or discharged product can be pumped aseptically into the filling line for packaging (Ting and Marshall 2002). A commercial semi-continuous HPP system is being used for grapefruit juice processing in Japan. It has a processing capacity of 600 L/h at a maximum pressure of 400 MPa (Palou et al. 2002).

6.2.3

Role of HPP in Microbial Inactivation and Food Preservation

High-pressure processing is used as a preservation technique as it can inactivate spoilage-causing and pathogenic microorganisms, thus improving the shelf life and safety of food products (McClements et al. 2001). Moderate pressure level generally decreases the growth rate of microorganisms by decreasing their reproduction rate, whereas for complete microbial inactivation, a higher level of pressure is required (Bajovic et al. 2012). The effectiveness of the process depends not only on the process parameters but also on the characteristics of microorganisms and the food matrix (Table 6.1). This is because some of the microbes are more susceptible to high pressure as compared to others. For example, bacterial spores are more resistant to high pressure than vegetative cells; eukaryotic cells are more sensitive to high pressure than prokaryotic cells. Thus, bacterial cells are more resistant to high pressure than yeast and molds (McClements et al. 2001). Thick peptidoglycan layer in the gram-positive bacterial cell wall provides more resistance to high pressure than gram-negative bacteria (Considine et al. 2008). Resistance of the microbial cells depends on the growth phase of the cell; microbial cells are more resistant to physical changes in their stationary phase than the exponential phase (Daryaei et al. 2016). The mechanism of microbial inactivation by HPP or sterilization is mainly due to the disruption of cell structure. Pressure treatment results in significant injury to the microbial cell membrane, which increases its permeability (Casadei et al. 2002). The disruption of the cellular membrane leads to the outflow of internal cellular constituents (Abe 2007). Disruption of cell membrane and alteration of its permeability due to pressurization leads to the efflux of ribosomes, nuclear material, and internal solutes leading to cell death. If the pressure applied is not sufficient, it may lead to reversible changes in cell structure that may recover after the storage of food for 1–15 days (Koseki and Yamamoto 2014). Irreversible changes in cell structure occur at higher pressure, where membrane permeability becomes the major reason for cell destruction. Pressure treatment at 500 MPa for 30 min at 25  C induced the

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Table 6.1 Non-thermal processing methods, application, and possible effects Methods High pressure processing

Application Drying of fermented sausages Carrot juice processing

Cheese

Processing of catechin fortified coconut milk Pulsed electric field

Pre-treatment of potato chips before frying PEF assisted osmotic dehydration of apple slices

Pretreatment of wheat seeds to improve germination. Beef

Cold Plasma

Pretreatment for drying of jujube slices

Fortification of rice with iron

Microbial decontamination of fresh cut carrots Coconut water

Effects – Increased inactivation of E. coli. – Reduction in time required for 5 log E. coli inactivation. – Highest enzymatic inactivation of PPO (polyphenol oxidase) and peroxidase (POD). – Enhanced total phenolic content. – Accelerating or arresting of cheesed ripening enzymes can be controlled. – Elimination of pathogenic bacteria. – Nano-encapsulated catechin was used to stabilize catechin during HPP. – Enhanced antioxidant activity and shelf life. – Reduced oil content of chips. – Improved textural properties, mainly crispiness and hardness. – Used as a pre-treatment for osmotic dehydration and its impact on freezing and thawing. – Acceleration of mass transfer properties. – Reduced freezing time – Increased water uptake – Increased antioxidant properties of plantlet juice from PEF treated seeds. – Increased protein digestibility of PEF treated cooked beef – Higher values of soluble protein. – Increased effective diffusivity and drying rate due to formation of intracellular cavities on treatment with cold plasma. – Prevents degradation of antioxidants. – Results in etching of rice surface, thus better penetration of iron. – Increased bioavailability of iron – Reduced cooking time – Reduction in spoilage causing microflora – Maximum quality retention – Reduced enzymatic activity – Increased phytochemical content

Reference Balamurugan et al. (2020) Szczepańska et al. (2020)

Nuñez et al. (2020) Ruengdech and Siripatrawan (2021) Zhang et al. (2021) Parniakov et al. (2016)

Ahmed et al. (2020) Bhat et al. (2019) Bao et al. (2021)

Akasapu et al. (2020)

Kumar Mahnot et al. (2020) Porto et al. (2020) (continued)

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Table 6.1 (continued) Methods Supercritical carbon dioxide

Application Pomegranate juice

Pasteurization of lipid emulsions

Irradiation

Extraction of oil from passion fruit and blackberry seed Microbial inactivation

Enzymatic inactivation

Quality and shelf-life enhancement

Sprouting inhibition

Ultrasound

Microbial and enzymatic inactivation

Emulsification

Drying pre-treatment

Effects – Pasteurization of pomegranate juice using SC-CO2 – Increased microbiological stability and color stability – SC-CO2 combined with high power ultrasound was used for pasteurization. – Increased activation capacity Better extraction yield of polar lipid fraction due to improved solubility – Gamma irradiation treatment of pistachios at 0.5 kGy, 1.0 kGy, and 3.0 kGy reduced E. coli O157:H7 by 0.99 log CFU/g, 1.50 log CFU/g, and 4.32 log CFU/g, respectively. – Maximum quality retention at 3.0 kGy. – Electron beam irradiation curbed down polyphenol oxidase activity of white button mushroom compared to that in control samples after 10 days of storage – X-rays (2 kGy) effectively increased shelf-life of ricotta cheese by up to 20 days, compared with no treatments lasting for only 3 days – Electron beam irradiation of five different potato cultivars suppressed sprouting and decreased loss in weight and firmness, when stored at 10  C for 180 days – Microbial inactivation in pear juice with sonication (65  C for 10 min) owing to acoustic cavitation phenomenon – Significant reduction in residual activity of POD, PME and PPO as 4.3%, 3.25%, and 1.91%, respectively – Fabricated geraniol and carvacrol loaded emulsions with increased solubility – Stable emulsion formation – Enhanced antimicrobial efficacy – The drying time for apple reduced by 13–17%

Reference GonzálezAlonso et al. (2020) GomezGomez et al. (2020) ArturoPerdomo et al. (2021) Song et al. (2019)

Duan et al. (2010)

Ricciardi et al. (2019)

Etemadinasab et al. (2020)

Saeeduddin et al. (2015)

Syed and Sarkar (2018)

Fijalkowska et al. (2016) (continued)

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Table 6.1 (continued) Methods

Application Mass transfer acceleration

Extraction

Meat tenderization

Effects – Improved brining efficiency of pork meat due to bubble implosion near the surface producing violent micro-jets improving mass transfer. – Ultrasound assisted pectin extraction yield from grapefruit peel reported higher (16.34%) compared to chemical extraction – Extraction time reduction by 37.78% using UAE – Ultrasound treatment (300–600 W at 40 kHz) for 30 min dispensed better tenderization of goose meat – 600 W treatment displayed lowest shear force and cooking loss

Reference Ozuna et al. (2013)

Wang et al. (2015)

Li et al. (2018)

inactivation of E. coli, S, aureus, Salmonella, and Shigella in milk caused by breakdown of the peptidoglycan layer of cell membrane (Yang et al. 2012). High pressure also results in conformational changes in ribosomes, cytoplasmic proteins, and nucleic acid. It is found to directly correlate with decreased viable cells of E. coli, as observed using scanning electron micrographs (Niven et al. 1999). Other than cell membrane, proteins and enzymes are also targeted by HPP responsible for microbial inactivation (Ulmer et al. 2000). High pressure results in irreversible aggregation of proteins due to denaturation and structural unfolding at a pressure above 300 MPa (Kalagatur et al. 2018; Abe 2007). Damage to the cell cytoplasm and mitochondria is caused at a pressure around 400–600 MPa (Smelt 1998). Inactivation of enzyme activity due to the pressure leads to reduced ribosome synthesis, which plays a vital role in replicating and transcribing genetic material (Lakshmanan et al. 2005). Microbial inactivation by HPP can be described by kinetic parameters like those used for thermal processing. These include (1) D-value, which describes the time taken to reduce the microbial load by one log cycle at constant pressure and temperature and is known as “decimal reduction time,” (2) ZP or pressure resistance constant that defines the increase in pressure at the constant temperature required for 90% reduction in the D-value, and (3) ZT or thermal resistance constant, that means an increase in temperature at constant pressure required for 90% decrease in D-value (Tsevdou et al. 2019). Inactivation of bacterial spores follows a slightly different mechanism than vegetative cells due to the presence of multiple protective layers, inactive metabolism, and low water content in case of spores that make them resistant towards high pressure (Georget et al. 2015). Therefore, as compared to vegetative cells, the inactivation of bacterial spores requires two processing steps. The first step is carried out to achieve germination of spores, and it mainly involves the treatment at pressure up to 600 MPa in combination with temperature up to 60  C. Then, in the second

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step, inactivation of germinated spores is achieved using pressure above 600 MPa, reaching a maximum up to 1200 MPa and temperature range from 60 to 120  C. Other than increasing the safety and shelf life of food products, HPP also improves product quality. Bioactive components of grape pomace, such as anthocyanins, proanthocyanidins, and soluble dietary fiber, are found to improve during processing at 200 MPa (Sheng et al. 2017). Pressure treatment between 400 and 600 MPa in cheese manufacturing increases the yield, promotes curd formation, reduces rennet coagulation time, and accelerates proteolysis at the time of ripening (Hokmollahi and Ehsani 2017). HPP at low temperature does not affect the ascorbic acid content but decreases if the processing is carried out at a higher temperature. During storage of HP treated orange juice at refrigeration temperature, the ascorbic acid loss rate was less than conventionally processed juice (Polydera et al. 2005).

6.3

Pulsed Electric Field (PEF)

A pulsed electric field (PEF) is a non-thermal processing technique that involves applying an electric field of high voltage for a short interval of time to the product. Usually, the electrical pulses of voltage ranging from 10 to 80 kV are used. PEF is an excellent technique for microbial decontamination and enzyme inactivation. It also improves the mass transfer phenomenon during drying operations (Toepfl et al. 2006; Onwude et al. 2017). It offers several advantages over conventional thermal processing treatments, such as better color, flavor, and nutrient retention, reduction in pathogenic microbes, and improved shelf life (Stoica et al. 2011). It is mainly used to pasteurize liquid and semi-liquid foods (Soliva-Fortuny et al. 2009). Commercial application of PEF for pasteurization of fruit juice has been made in the USA by Genesis juices. In addition, modification of food proteins can be carried out using PEF to improve their stability and functionality (Zhao et al. 2012). Besides food processing, PEF is also applied in biotechnology and genetic engineering for electrofusion and cell hybridization (Chang et al. 1992).

6.3.1

Principle of PEF

High-intensity pulsed electric fields ranging from 10 to 80 kV are used for micro to milliseconds. The food product is placed between the two electrodes and exposed to high-intensity pulses. The gap between the electrodes where the product is placed is known as the “Treatment gap.” The electrodes are connected using a non-conducting material. Depending on the processing requirement, the process can be carried out at room temperature or low or high temperatures (Zimmermann and Benz 1980). The transfer of electrical pulses through the food is based on the fact that food contains ions responsible for the electrical conductivity of food. Due to the presence of charged molecules, the transfer of pulses is relatively more effortless in liquid

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foods (Zhang et al. 1995). It can be effectively used for the pasteurization of milk, juices, yoghurt, and liquid eggs (Bendicho et al. 2003). Several events occur during food exposure to PEF treatment, including resistant heating, perturbation of cell membranes (Sitzmann 1995), and electrolysis (Hülsheger and Niemann 1980). Electroporation of microorganisms is a significant phenomenon that takes place during the PEF treatment that is responsible for the inactivation of microbes. PEF also finds its application in enzyme inactivation and extraction of several components and can also be used as a pre-processing technique for unit operations like drying. It causes minimal changes to the nutritional and sensory characteristics of foods.

6.3.2

Equipment and Process Design

PEF treatment system includes the following: • • • • •

Pulse power generation system. Temperature control system. Material transport system Treatment chamber. Operating system.

Pulse generation system is used to convert alternating current (AC) into direct current (DC). The capacitor stores energy and a switch are used to control the discharge of electrical energy into the chamber (Fig. 6.2). The type of wave or shape of the pulse may vary depending on the configuration of the discharge circuit or pulse forming network (PFN). It can be a short square wave, exponential, or

Fig. 6.2 Diagrammatic representation of pulsed electric field food processing system

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sinusoidal wave (Morris et al. 2007). Square wave and exponential decay pulses are commonly used. There is an increase in voltage and current intensity in the exponential decay pulse form that reaches up to a maximum. Then there is a slow decay approaching zero, resulting in a low electric field. Thus, the sample is exposed to a spectrum of electric fields rather than a constant electric field. On the other hand, a continuous voltage peak is maintained throughout the pulse duration in the case of the square wave (Wouters et al. 2001). The electric field strength (kV/cm) is defined by the voltage delivered per unit distance between the electrodes in a treatment chamber. The pulse width of exponential decay pulses is described as the time taken by the input voltage to decay up to 37% of its maximum value. The numbers of pulses applied per unit time are described as frequency (Hz). Specific energy of the pulse is reported as kJ/kg depends mainly on voltage and pulse width and geometry and conductivity of material responsible for the resistance of the treatment chamber (Wouters et al. 2001; Zhang et al. 1995).

6.3.3

PEF for Food Processing and Preservation

The mechanism of microbial inactivation by PEF is based on electroporation. The cell membrane breakdown due to a high-intensity electric field creates pores in the cell structure (Simonis et al. 2019). Various theories govern the structural changes of the cell membrane during PEF treatment, based on electric field-induced tension, trans-membrane potential, electromechanical compression, and osmotic imbalance (Toepfl et al. 2014). During the initial phase of PEF treatment, there are no changes in the cell membrane. However, as the intensity increases, there is reversible permeabilization of the cell membrane, just below the critical level of the electric field. Furthermore, an increase in intensity beyond the critical level leads to irreversible electroporation of microbial cell membranes (Martin-Belloso and Elez-Martínez 2005). Dielectric rupture theory was developed to explain the phenomenon of electroporation. It was based on equivalent circuit models and physical properties of cells, where the cell membrane was designated as dielectric capacitor and suspension of cells was considered as resistors and capacitors (Sale and Hamilton 1968; Zimmermann 1986). According to this theory, the transmembrane potential that occurs across the cell membrane increases due to an external electric field, and there is a reduction in membrane thickness. The membrane breakdown occurs when there is an increase in electro compression compared to restoring viscoelastic forces across the membrane. According to the dynamic model for explaining pore formation, it was described that in order to expand the pores to critical diameter and become irreversible, a sufficient amount of critical voltage needs to be applied (Schoenbach et al. 2000). The cell membrane has a lipid bilayer structure. It has its net electric charge. The conformational changes occur in the lipid molecules due to the electric field that

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further expands existing pores and creates new pores (Jeyamkondan et al. 1999; Tsong 1989). Protein channels are also present in the cell membranes that are affected by the electric field. Irreversible denaturation of protein channels occurs due to applying a high-intensity electric field (Tsong 1989). Eukaryotic cells require less intensity of electric field to cause irreversible changes than prokaryotes (Heinz et al. 2001). Bacterial spores are highly resistant to PEF compared to vegetative cells, mainly due to a thicker outer cortex and their dehydrated state (Barbosa-Cánovas and Altunakar 2006). PEF can be combined with other preservation techniques to improve its effectiveness for microbial inactivation. For example, when the inlet temperature of sample was kept 10  C, there was 0.9 log cycle reduction of E. coli at 30 kV/cm, whereas on increasing the inlet temperature to 30  C, there was 1.7 log cycle reduction (Aronsson and Rönner 2001). Application of exponential pulse with 20 kV/cm electric field strength at the rate of 10 pulses of 30 μs pulse width led to a reduction in 3 log cycles of E. coli. No lethal effect on E. coli was observed when electric field strength below the threshold value of 3 kV/cm was applied (Evrendilek and Zhang 2005). Application of PEF for apple juice treatment resulted in an increase in the diffusion coefficient of soluble solids (Jemai and Vorobiev 2002). PEF treatment increases the ascorbic acid retention in carrot-orange juice during storage. For example, treatment of carrot-orange juice at an electric field strength of 25 kV/cm for a pulse width of 280 and 330 μs resulted in 90% retention of ascorbic acid, whereas 83% for thermally pasteurized juice (Torregrosa et al. 2006). PEF also finds its application in enzyme inactivation. It results in changes in the conformation of the tertiary structure of the enzyme. Pectin methyl esterase (PME) is the enzyme that leads to the degradation of pectin in citrus juices and thus reduces the cloudiness and viscosity of juice. Therefore, the treatment of citrus juice with PEF leads to the inactivation of PME and helps maintain cloud stability and viscosity (Aguilo-Aguayo et al. 2009). This technique can also be effectively used for the modification of structural and functional properties of various macromolecules such as starch and protein (Hong et al. 2016). It can also be used for improving the extraction process from the plant by-products such as bioactive components and pigments (Azmir et al. 2013; Boussetta and Vorobiev 2014).

6.4

Cold Plasma

The term “Plasma” was described in 1928 by the chemist Irving Langmuir. It is a Greek word that means “moldable substances” (Rajvanshi 2008). Plasma is the fourth state of matter known after three distinct forms: solid, liquid, and gas. There is a change in state from solid to liquid and liquid to gas on increasing the energy. Furthermore, an increase in energy of the gaseous state beyond a specific limit leads to ionization of molecules and yields the fourth state of matter called plasma (Luo et al. 1998). Plasma is an ionized gas composed mainly of reactive species such as

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electrons, protons, neutrons, and various ions such as hydroxyl ions, atomic oxygen, and nitrogen species (Misra et al. 2016). Cold plasma is one of the recent technologies that find its application in food preservation and safety. The generation of cold plasma is mainly carried out by applying electric current to the mixture of gases or a pure gas, which results in the formation of reactive species, and plasma glow is generated. Active plasma components interact with microbial cells and thus cause damage to the cells. It can be effectively used to inactivate microbial cells, thus increasing the product’s shelf life.

6.4.1

Principle of Cold Plasma

Plasma can be thermal or non-thermal depending on the source and method used for plasma generation. Generally, plasma is generated using conventional devices such as spark plugs and welding arcs, and thermal plasma is generated, where the energy of particles is sufficiently high that they are in equilibrium. There is no energy transfer among them, and highly reactive species are in hot plasma (Fridman et al. 2005). The non-thermal or cold plasma, such as in plasma display screens and neon signs, transfers energy among particles after every collision. Some species are more reactive than others in cold plasma. The composition of gases in plasma generation is an essential factor determining the type of chemical reactions plasma can initiate (Niemira and Gutsol 2011; Lieberman and Lichtenberg 2005). The ionization of gas is a significant step for the generation of plasma. Ionization can be carried out by various thermal, magnetic, electric fields, radio waves, and microwaves (Conrads and Schmidt 2000). The external application of high energy to the atoms present in gas leads to the stripping away of electrons from atomic nuclei, thus forming reactive plasma species (Misra et al. 2016). The type and concentration of reactive species depend on the gaseous mixture’s characteristics used for plasma generation. For example, the concentration of reactive oxygen species is more if oxygen is present in the mix (Smet et al. 2018). Cold plasma does not rely on the thermal inactivation of microorganisms and is generated at room temperature, thus not affecting the product’s quality attributes (Fernandez et al. 2013). Cold plasma occurs in a non-equilibrium state where the massive particles are generally at room temperature. In contrast, sufficient kinetic energy is present in free electrons for ionizing collisions and bond breaking. While on the other hand, thermal plasma exists in completely thermodynamic equilibrium (Kennedy and Fridman 2011).

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Generation of Cold Plasma: Equipment and Process Design

Plasma generation is carried out by ionization of gases using different methods. Typical plasma generation system includes the following: • • • • •

Power source. Plasma discharge device. Carrier gas. Treatment chamber. Gas and pressure control system.

Different types of discharge systems can be used for processing operations. These include dielectric barrier discharge, glow discharge, corona discharge, arc discharge, radio frequency, and microwave discharge (de Castro et al. 2020; Romani et al. 2019; Tabibian et al. 2020; Corradini 2020). Dielectric barrier discharge involves the use of metal electrodes covered with a dielectric material. In this system, one electrode is grounded, while another is connected to high voltage and a mixture of gases flows between the two electrodes. It is also known as “Silent discharge” (Shimizu et al. 2018). In glow discharge plasma, alternating current is passed through a gaseous mixture by applying high voltage. When the pressure inside the vacuum chamber reaches 2 Pa, plasma generation occurs (Romani et al. 2019). In corona discharge plasma, the non-uniform electric field is used under atmospheric pressure. Plasma generated using this method has some luminosity, which is weakly ionized. The magnetron is used to produce a high-frequency electromagnetic field in case of microwave plasma discharge. It is commonly used in the case of hightemperature processing because heat is generated during the collision of electrons with gaseous atoms and molecules. As a result, a high degree of ionization takes place (Roy 2017). The plasma that is generated is then discharged for treatment of food, using micro-needle, plasma jet or plasma chamber (Sakudo et al. 2020). Cold plasma can be used in three different exposure methods for food preservation: direct exposure, electrode contact, or remote exposure. In the direct exposure method, food materials are placed in direct contact with the plasma generation system, also known as active plasma, which contains long- and short-lived reactive plasma species. In the case of the remote exposure method, firstly, the plasma is generated. After that, it is used for treatment, and the food product is not placed in the same chamber where plasma is generated. Therefore, the reactive species act on food placed in a separate chamber and are thus not in direct contact during plasma generation. Plasma generating electrodes are used in the case of electrode contact method, and food is placed between the electrodes, which emit reactive species, and ion bombardment takes place (Niemira and Gutsol 2011).

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171

Application of Cold Plasma for Food Preservation

Cold plasma is an emerging non-thermal technology that can be effectively employed for food preservation without causing any damage to the quality of food products. The mechanism of microbial inactivation using cold plasma is based on the action of reactive plasma species on components of the microbial cell (Phan et al. 2017). The presence of reactive free radicals in plasma causes oxidative damage to polyunsaturated fatty acids present in the lipid membrane, as they are more sensitive to reactive oxygen species (Alkawareek et al. 2014). Lipid peroxidation leads to the formation of fatty acid radicals that react with oxygen converted into lipid hydroperoxide. The lipid peroxides further cause damage to proteins and DNA through the irreversible formation of covalent adducts (Del Rio et al. 2005; Joshi et al. 2011). UV photons are also formed during plasma generation, responsible for causing damage to the genetic material and inhibiting DNA replication (Guo et al. 2015). In addition, photons emitted by the plasma lead to the formation of thymine dimers and oxidation of nucleotides by reactive oxygen species. Apart from protein and lipid alterations, free radicals also lead to the breakdown of structural bonds like C-O and C-N bonds present in cell wall components, such as peptidoglycan, leading to damage to the cell wall (Misra et al. 2016). Permeabilization of cell membrane leads to the leakage of cellular components from cell-matrix, and the reactive species further cause damage to the intracellular nucleic acids and protein. It is also the reason for the bactericidal effect of cold plasma (Mai-Prochnow et al. 2014). Therefore, scanning electron microscopy was employed to study the morphological changes caused in bacterial cells, like membrane damage, electroporation, and cell wall damage (Misra et al. 2016). Cold plasma can effectively be used for microbial inactivation as it is found lethal to vegetative cells and spores (Feichtinger et al. 2003). In solid foods, the focus is on surface decontamination, whereas in liquid foods, every particle is in contact with the plasma (Fig. 6.3). Cold plasma also finds its application in the decontamination of packaging materials. For example, radiofrequency pulse discharge on the air inside PET (polyethylene terephthalate) bottles resulted in 3 log cycle reduction of microorganisms and had deodorization effect in the few milliseconds exposure (Koulik et al. 1999; Deilmann et al. 2008). Various researchers have studied the use of plasma for microbial decontamination of packaging materials such as polypropylene (Gadri et al. 2000), PET foil (Schneider et al. 2005), polystyrene, multilayer packages (Muranyi et al. 2010), glass, polyethylene, and paper foil (Lee et al. 2015). Reactive plasma species also cause the volatilization of spore surface components, also known as “Etching” (Philip et al. 2002). It was found that there was 3.5 log cycle reduction in B. subtilis population on exposure of 5 min by plasma generated at 200 W (Hury et al. 1998). Products with grooves on their surface require more exposure time for microbial inactivation (Fernández et al. 2012; Ziuzina et al. 2014). On exposure of apple juice for 480 s, there was five log cycle reduction in population of Citrobacter freundii by plasma generated using argon and

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Fig. 6.3 Flow sheet depicting the generation of cold plasma and action on microbial cells

0.1% oxygen (Surowsky et al. 2014). Significant reduction of E. coli and Salmonella was observed on the surface of apples, mangoes, and melons after plasma treatment (Tappi et al. 2016). Cold plasma can also be applied to prevent enzymatic browning in fruits and vegetables. Seventy percent reduction in polyphenol oxidase activity of guava pulp was observed, which was treated with cold plasma for 300 s at 2 kV (Thirumdas et al. 2015). Plasma leads to the breakdown of peptide bonds, thus changing the 3-D conformation of the protein (Dobrynin et al. 2009). It also increases the ascorbic acid retention in tomato juice on treatment for 10–15 min compared to other non-thermal techniques like ultrasonication (Mehta et al. 2019). Starch modification can also be carried out using cold plasma technology. Plasma treatment significantly affects the crystallinity of starch granules (Zhang et al. 2014). Graft polymerization of ethylene using glow plasma was carried out on rice and cassava starch (Lii et al. 2002). The germination rate of pulse seeds was found to increase by 10–20% on treatment with cold plasma (Filatova et al. 2011). Cold plasma can also be applied to change the functional properties of packaged meat as it decreases the water immobilization in the myofibrillar network (Wang et al. 2016). Microbial decontamination of eggshells can be done using this technique (Ragni et al. 2010). Cold plasma technology has excellent potential for commercialization in the food industry. Optimization of operational parameters is essential to meet the safety regulations.

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173

Supercritical Carbon Dioxide (SC-CO2)

Supercritical fluids are among the recent advances in food processing and preservation. They are characterized by the properties of both liquid as well as gas and are nowadays widely used as a solvent for extraction purposes. They have high density like liquids but possess low viscosity and intermediate diffusivity like gases (Knez et al. 2014; Raventós et al. 2002). When a substance is brought beyond its critical temperature and pressure, it reaches a supercritical region where its gas and liquid phases are in equilibrium (Cavalcanti et al. 2012). Carbon dioxide comes under the GRAS (generally recognized as safe), due to its inert, non-toxic, and non-corrosive properties. Recent trends in food processing are focused on the utilization of supercritical fluids. The utilization of carbon dioxide (CO2) for extraction, processing, pasteurization, microencapsulation, and sterilization is widely studied (Kulkarni et al. 2017; Osorio-Tobón et al. 2016; Silva and Meireles 2014).

6.5.1

Principle and General Aspects of SC-CO2

Supercritical CO2 is an emerging non-thermal food processing technology and is considered a sustainable and green technology. It is safe and environmentally friendly as it can be re-circulated in the system. By reducing the pressure, removing CO2 from the food matrix can be easily carried out. The critical temperature of CO2 is 31.2  C, and the critical pressure is 7.38 MPa. Due to its low critical temperature, it maintains the fresh-like quality characteristics of food and does not lead to thermal degradation of food components. Also, the energy requirement for maintaining pressure is less than high-pressure processing and other supercritical fluids due to its moderate critical pressure (Cavalcanti et al. 2012, 2016; Viganó et al. 2015). It is also known as “Dense phase carbon dioxide.” SC-CO2 is the state where carbon dioxide exists as liquid as well as gas, and both the phases are in equilibrium because as the temperature and pressure reach beyond the critical point, molecules have kinetic energy that is sufficient to overcome the forces that are responsible for condensation of fluid (Werner and Hotchkiss 2006). Due to high diffusivity of SC-CO2, it can easily penetrate the microbial cells and thus finds its application in microbial inactivation. It can also be effectively used to extract various components from food waste due to its solvent capacity (Wang et al. 2020). This technique’s mechanism of microbial inactivation is based on change in cytoplasmic pH, cell wall rupture, modification of cell membrane components, inactivation of enzymes, etc. (Fig. 6.4). This is also known as “cold pasteurization.” It can be used in batch, semi-continuous or continuous systems. Small changes in temperature and pressure can adjust solvent properties and chemical reaction rates of carbon dioxide in a supercritical state. A change can vary the fluid density in pressure as it has high compressibility at a critical point. This

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Fig. 6.4 Possible mechanisms of SC-CO2 to microbial inactivation

can further lead to change in viscosity, diffusivity, and solvation properties (Nikolai et al. 2019).

6.5.2

Equipment and Processing

SC-CO2 system can be operated in batch, semi-continuous or continuous mode. Processing in semi-continuous and continuous systems offers more advantages during processing large sample volumes (Paniagua-Martínez et al. 2018). The SC-CO2 processing system comprises of: • • • • • •

CO2 Tank. CO2 pump. Pressure vessel. Pressure regulator. Exhaust system. Temperature controller.

A carbon dioxide pump is used to pump the gas from the tank to the pressure vessel, where it comes in contact with the food, and a temperature controller is used to regulate the heating or cooling temperature. An exhaust system is used for

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degassing of the product after the treatment. Liquid food products are more effectively treated using this technique. Pressure and temperature are the major parameters that affect the solubility of CO2. Solubility of CO2 is found to increase with the increase in pressure whereas it is found to decrease as the temperature increases (Calix et al. 2008). In a batch system, the food product to be treated is placed in a pressure vessel and is saturated with CO2 at the required pressure and temperature. The product is kept in contact with the gas in a vessel for a period. After the desired contact time, the pressure decreases, and the exhaust system is used for degassing the product (Damar and Balaban 2006). In a semi-continuous system, a series of vessels are connected, allowing energy recovery, and reducing the processing time. In this system, CO2 flows continuously through the pressure vessel. During the processing, one vessel is kept under constant pressure, the second is under depressurization, and the third is unloading (Porto et al. 2010). In a continuous system, the liquid food product and the CO2 are mixed after being pumped through the system, and then the mixture is passed through the high-pressure pump to achieve the desired processing pressure. The residence time or contact time is maintained by adjusting the flow rate of gas and the liquid sample (Werner and Hotchkiss 2006; Damar and Balaban 2006). Co-solvents can also be used along with CO2 to increase the efficacy of the treatment. Various co-solvents that can be used include ethanol, water, peracetic acid, and nisin (Park et al. 2013; Sikin et al. 2016; da Silva et al. 2016).

6.5.3

Application of SC-CO2 in Food Preservation

SC-CO2 has potential for application in food preservation due to its non-toxicity and can be easily removed from food products after processing by depressurization. It can be effectively used to inactivate enzymes and spoilage-causing microorganisms and maintain the fresh-like characteristics of food products. Microbial inactivation due to SC-CO2 processing can be attributed to several factors such as cell rupture due to CO2 expansion within the cell and rapid pressure release, lowering of extracellular pH, extraction of lipids from cell membrane due to solvent properties of CO2, and loss of enzyme activity (Ballestra et al. 1996; Spilimbergo et al. 2003; Meyssami et al. 1992). The formation of carbonic acid occurs due to the reaction between CO2 and intracellular water, thus lowering pH (Shieh et al. 2009). Treatment with CO2 also causes the “Anesthesia effect,” which is one mechanism for microbial inactivation. According to this phenomenon, as CO2 penetrates the phospholipid bilayer, it increases permeability and fluidity. In addition, due to its lipophilic solvent properties, it can easily bind with lipid molecules in the cell membrane, thus leading to leakage of cytoplasmic components (Spilimbergo et al. 2003). Cell rupture, the disintegration of the cell wall, and puncture holes were visualized using SEM images of SC-CO2 treated Staphylococcus aureus and Serratia marcescens (Hossain et al. 2013). In the case of spore inactivation, initially, there is

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disruption of spore coat and cortex, which leads to increased permeability. After that, subsequent penetration of CO2 into the core of the spore results in the release of cellular components, thus leading to inactivation (Rao et al. 2016; González-Alonso et al. 2020). Processing of mandarin juice with SC-CO2 resulted in 3.47 log cycles reduction of the microbial population when process parameters were, the temperature of 35  C at a pressure of 41.4 MPa for 9 min of retention time and 7% CO2 (Lim et al. 2006). 6.93 log cycle reduction of Lactobacillus caseii apple juice was observed when SC-CO2 was carried at 55  C for 30 min at a pressure of 10 MPa and CO2 to juice ratio was 70% (Silva et al. 2018). Enzymatic inactivation by this technique is caused mainly due to protein denaturation and disruption of secondary and tertiary structures. Enzyme inactivation of fresh juice is required to maintain the nutritional and sensory characteristics (BenitoRomán et al. 2019). Conformational changes take place in the protein matrix of the enzyme during processing. Peroxidase, polyphenol oxidase, and pectin methyl esterase are the major enzymes that must be inactivated in fruit and vegetable juices to maintain their freshness. This non-thermal preservation technique also helps to retain the maximum amount of ascorbic acid in orange juice. It was observed that 88% of the ascorbic acid was retrained in orange juice treated at 40  C at a pressure of 25 MPa using SC-CO2. In contrast, only 57% of ascorbic acid was retained in orange juice thermally (Oulé et al. 2013). This is an emerging non-thermal processing technique, and it also finds its application in the extraction of bioactive components from food products. It can also be used for drying operations.

6.6

Irradiation

Food irradiation technology has gained interest worldwide and has been increasingly used on different food products. Food irradiation is an efficacious non-thermal process for the safety and shelf-life expansion of foods (Fig. 6.5). It is a process that uses a small amount of ionizing radiation to treat food and feed products to kill the pathogenic microbes present in the products. The irradiation of food safely reduces spoilage bacteria, insects, and parasites in certain fruits and vegetables and efficiently inhibits sprouting and delays ripening (Ashraf et al. 2019; Bisht et al. 2021). Also, in many countries, irradiation is used primarily for spices and herbs. As known, spices are often heavily contaminated because they are dried out in the open, and birds and insects contaminate the food. The irradiation technology is eligible and suitable enough for its utilization in the agro-food sphere. Adequate application by choosing the right wavelength and dosage these rays can prevent sprouting, maintain freshness, and eliminate harmful microbes present in foods. They also can rid fresh fruits and vegetables of insects that might otherwise hitchhike spreading to other areas where they could instigate harmful effects on the environment and humans

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Fig. 6.5 An overview on irradiation technology for food processing

(Lacroix 2014; Pedreschi and Mariotti-Celis 2020). Food irradiation was recognized by leading regimes associated with agriculture, food, and health (FDA, USDA, WHO, FAO, etc.) gleamed from extended research work. The Food and Agriculture Organization/International Atomic Energy Agency/World Health Organization (FAO/IAEA/WHO) joint committee on the wholesomeness of irradiated food approved in 1981 the irradiation technology (Junqueira-Gonçalves et al. 2011).

6.6.1

Principle

Irradiation as a food processing approach culminates in the subjection of food to certain dose radiation, causing the elimination of pathogenic microbial load, which keeps food product entirely for longer by halting spoilage. The mechanism by which ionizing radiation inactivates microorganisms is mainly due to the direct or indirect damage of the nucleic acids (DNA) of microbes, which is affected by free radicals (OH) derived from the radiolysis water. Food treatment with irradiation allows energy to pass through food and hit all the molecules that are present in the food. Microorganisms and insects possess nucleic acids (DNA). When the actuated

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radiation hits DNA, it destroys due to ionizing particles but without inciting any substantial temperature escalation of foodstuff, subsequently avoiding the division of cells by inhibiting DNA synthesis. In contrast, indirect radiation, the interaction with water molecules provokes rendering of active molecules such as hydroxyl and hydrogen radicals, and hydrated electrons, leading to cell lysis (Lacroix 2014; Ravindran and Jaiswal 2019).

6.6.2

System and Processing

Electromagnetic radiation covers a broad spectrum of wavelengths, and these waves have disparate uses according to their energy. Radio waves and microwave waves find wide application in the field of communication, although microwaves are also used in thermal processing of food, while visible light illuminates and is essential for food production, X-rays on the other hand used for analytical purposes as well as treating illnesses through radiotherapy and gamma rays are used as radurization purposes (Prakash and de Jesús Ornelas-Paz 2019; Prakash 2020). Radiation can be categorized as ionizing and non-ionizing, depending on its energy. Ionizing radiations are shorter in wavelength but entail higher frequency and energy, contrary to non-ionizing energy. The visible light spectrum, radio waves, micro-waves, and infrared waves hold adequate energy to instigate molecular vibrations but not ionization (Bisht et al. 2021). Contrastingly, X- and gamma-rays comprehend higher energy, which can potentially discharge electrons from atoms, inducing the ionization of molecules. These radiations can also terminate chemical bonding in molecules, inhibiting normal cell functioning. The label “food irradiation” emphasizes the purposive subjection of eatables to ionizing radiation (Prakash and de Jesús OrnelasPaz 2019). Ionizing radiation factors in different irradiation origins, viz. gamma-rays, X-rays, and e-beam. Gamma radiations are yielded up employing radionuclide sources such a cobalt-60 or cesium-137; accelerated electrons (forming electron beams) with a maximal energy of 10 MeV (Hernández-Hernández et al. 2019). Gamma irradiation is conventionally acknowledged as a microbial decontamination agent owing to its property to annihilate covalent bonding of bacterial DNA (Mittendorfer 2016). Irradiation using cobalt-60 radioisotope is commonly used for food treatment purposes owing to its insolubility in water, thwarting environmental contamination, and hazards (Bisht et al. 2021). Contrarily, X-rays are produced without any association with radioactive resources but are generated by the bombardment of a dense target material using high energy accelerated electrons, resulting in a continuous energy spectrum. X-rays with a maximum energy of 5 MeV can be used to irradiate foods, which yield similar penetrating power as Co-60. X-rays can ably penetrate thick material (30–40 cm), which benefits its utilization for in-package product treatment, preventing food re-contaminations (Ashraf et al. 2019). X-rays irradiation at 10 kGy dosage can be used to scale down microbial load (Bisht et al. 2021). Electron beams are propelled as a result of high-energy

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electrons in an accelerator (such as a linear accelerator or Van de Graaff generator) that produce accelerated electrons at nearly the speed of light. Electron beam delivers higher output at lower cost but also has low dose uniformity and a penetration depth. Therefore, it is commonly used for the treatment of foodstuffs with lower thickness (Ashraf et al. 2019). However, food irradiation treatment’s productiveness also considers the nature of these types of ionizing radiation, medium composition, water activity, and O2 ubiquity, dose absorbance, thickness, and density of food matrix (Hernández-Hernández et al. 2019).

6.6.3

Applications in Food Industry

As consumer demands and food safety issues have changed, so have the food processing technologies to ensure food safety. Irradiation is a versatile technology that caters to numerous applications. The agro-food realm is highly efficient and suitable technology in holding comprehensive efficacy against different non-sporing bacteria and insects. Irradiation treatments of foods do not integrate any utilization of chemical additives, which is another significant advantage considering the growing consumer awareness and demand for chemical-free food. Apart from this, irradiation of food stuffs can be efficiently done in their final packaging without affecting microbial inactivation potential. Also, in-package irradiation of food products prevents re-contamination of foods (Bisht et al. 2021; Hernández-Hernández et al. 2019). The food industry deals with the different raw materials and matrices for food production and processing, which unintentionally invites microbial incidence. This resultantly leads to the entry of pathogens in the food production and processing stages. Upon treatment of food with radiation, energy passes through the food and strikes all the molecules present in the food. Microbes and insects possess DNA, and radiant energy upon striking DNA induces damage preventing microorganisms from multiplying. Differences in food radiation sensitivities among the microorganisms are related to differences in their chemical and physical structure and their ability to recover from radiation injury. Numerous studies in past times have disclosed the applicability of irradiation to obliterate microbial populations on various food items. In an investigation, the aftereffect of gamma irradiation (1, 3, and 5 kGy) on microbial load of pomegranate arils was analyzed and the obtained results reported that different irradiation doses considerably downsized the microbial population. Authors also reported that with elevation in dose, the impact of irradiation on the decrement of both bacteria and fungi improved markedly, with 5 kGy dosage yielding leading results (Ashtari et al. 2019). Similar outcomes concerning microbial population depletion for the tested gamma irradiation doses (1, 3, and 5 kGy) were observed for date fruits stating better elimination with the highest dose (Zarbakhsh and Rastegar 2019). A study conducted to evaluate the effect of γ-irradiation (0, 1, 3, 5 kGy at 26  2  C) on the mango juice resulted in curtailed total aerobic bacteria (TAB) counts of fresh and stored juice from 4.2  0.3  104 and

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6.5  0.2  105 CFU/mL to 2.6  0.2  10 and 1.1  0.8  10 CFU/mL, respectively, when treated with 3.0 kGy dosage. On the other hand, a higher dose of 5.0 kGy exhibited no bacterial incidence in fresh and stored samples (Naresh et al. 2015). Red raspberries on exposure to e-beam (3 kGy) as post-harvest treatment brought about reduction of mesophilic bacteria and filamentous fungi by 2 log CFU/g and 3 log CFU/g, respectively, in comparison to untreated samples during 7 days of refrigerated storage conditions (Elias et al. 2020). Apart from this, irradiation also finds its utilization in microbial elimination in raw and processed muscle-based products. Meat-based products being nutrient rich matrices are highly prone to microbial incidence. Enzymatic activity influences the sensory and nutritional quality of the food. It can be roused at any stage during harvesting, transportation, storage, and processing. Golden Empress cantaloupe juice was exposed to cobalt-60 irradiation (1–5 kGy). The obtained results for enzyme activity determination displayed that lipoxygenase was the easiest to be inactivated by irradiation, followed by polyphenol oxidase and peroxidases. However, all enzymes remained active even at 5 kGy (Wang et al. 2006). In another study, white button mushrooms on exposure to e-beam irradiation (1–4 kGy) displayed lower polyphenol oxidase activity than in control samples after 10 days of storage (Duan et al. 2010). Quality and shelf-life are the crucial factors of concern while dealing with any preservation technology. The shelf-life of different foods is affected by several intrinsic and extrinsic factors. However, irradiation can be an important factor in extending the shelf life of other food products. Various studies have also proved the applicability and efficaciousness of irradiation technology on the different products such as raw and processed meat products, fruits, vegetables (Lacroix 2014), nuts, spices, grains (Hernández-Hernández et al. 2019). In a study, Mulmule et al. (2017) examined the effect of electron beam irradiation (EBI) at 2.5, 5, and 7.5 kGy and combination of EBI (2.5 kGy) with thermal-treatment (80  C for 20 min) to prolong the storage life of Idli (fermented food) and obtained results illustrated an increased shelf-life stability of 60 days for the Idlis’s irradiated at 7.5 kGy in combination with thermal treatment. In another study, Yoon et al. (2020) checked the efficacy of X-ray irradiation (0, 0.15, 0.4, 0.6, and 1 kGy) on different qualitative attributes of Korean strawberries during storage at 15  C for 9 days and the results showed that irradiation at 1 kGy effectively retarded decaying and inhibited negative physicochemical alterations in fruit with improved shelf life but maintaining the sensorial quality of strawberries. Maraei and Elsawy (2017) treated strawberries with γ-irradiation at (0, 300, 600, and 900 Gy) different doses and the results revealed reduced weight loss, spoilage rate at storage, in contrast to untreated ones. Fruits subjected to 600 Gy irradiation attained maximal total phenolic content and antioxidant activity ahead of 300 Gy. However, all treatments lowered vitamin C extents during storage, but anthocyanins escalated gradually during storage. Similarly, Aftab et al. (2015) evaluated the efficacy of gamma irradiation (Cobalt 60) at 0.5, 1, and 1 kGy on goat meat. Results determined that gamma irradiation at 1.5 kGy significantly maintained meat quality under refrigerated conditions as it prolonged the shelf-life by 9 days.

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Other than increasing the safety and shelf life of food products, irradiation also inhibits sprouting. Sprouting activity is a significant problem contributing to reduced shelf life and elevated sugar content, affecting the marketability of seeds and tubers. Various researchers have studied the use of irradiation technology for the suppression of germination. In a study conducted to analyze the effect of electron beam irradiation (0, 500, and 1000 Gy) on five cultivars (Arinda, Burren, Sante, Agriya, and Marfona) of potatoes resulted in the suppressed sprouting. It decreased loss in weight and firmness, irrespective of the varieties, when stored at 10  C for 180 days (Etemadinasab et al. 2020). In a different study, exposure of potato tubers to electron beams (200 Gy) at 4  C or ambient temperature resulted in inhibited sprouting up to 110 days of tested storage (Blessington et al. 2015). Although, sprouting suppression of tubers is reported to require lower irradiation doses which permits substantial shelf-life increment (Roberts 2014), but among the other critical factors which influence sprout-inhibition are rate of radiation, time delay between harvest and irradiation, variety, and the storage conditions (temperature, relative humidity, and duration). Apart from potatoes, garlic and onions are also treated with irradiation to prevent sprouting. They are sprouting inhibition, better quality retention, improved shelf-life under the dearth of chemical additives, and associated harmful residues set up irradiation as productive technology for the shelf-life expansion of tubers, onions, and garlic (Prakash 2020).

6.6.4

Consumer Perception on Food Irradiation

In the past decades, irradiation has gained considerable importance for its utilization in the agro-food industry in different countries. Food irradiation is reliably endorsed as a promising preservation approach and is affirmed by various associated agencies. Although irradiation proposes numerous benefits such as eliminating microbes and insects, quality and shelf-life enhancement, in-package treatment, no chemical input, and does not instigate any toxic changes to the food itself, etc., however, this cannot be denied that this technology also equates to some limitations such as higher investment cost for commercial-scale application of technology to foods. Also, irradiation applies to only a certain range of foods but not all genres. Apart from that, one of the focal issues regarding the acceptance of technology is the consumer perception about the irradiated foods and their psyche that foods might contain radiation which ultimately leads to rejection (Lima Filho et al. 2015; Ravindran and Jaiswal 2019). Associated fraternity such as FAO and WHO have already validated for utilization and safety of irradiation technology for food up to 10 kGy dosage. Labelling of irradiated foods is also made mandatory by governing associated bodies; however, only a few countries make its use obligatory, while a few states permit the optional use. Stamping of “Radura” a green-colored symbol, is used for irradiating treated food products to heave consumer confidence and acceptance of irradiated products. Apart from that, there is a certain need to raise awareness and shift consumers’ attitude (Junqueira-Gonçalves et al. 2011; Roberts 2014).

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Ultrasound

All sound is created when molecules in the air, water, or other medium vibrate in a pulsing wave. Ultrasound waves are more often like sound waves but vary in terms of frequencies. The distance between each peak determines the frequency which is measured as cycles per second or hertz. Sound waves that are detectable to the human ear (16 Hz to 16–20 kHz) lie to a somewhat lower frequency range than ultrasound frequency above 20 kHz (Gallo et al. 2018; Bhargava et al. 2021). Ultrasonication is a dynamic and serviceable technology, applicative in numerous fields. It is used as a non-invasive way to examine inside patients’ bodies; ultrasound imaging is used to evaluate organ damage, measure tissue thickness, detect tumors and blood clots, etc. Apart from application in the medical realm, this non-thermal technology is utilized in biotechnology, food, nutritional enhancement, and cosmetics. Ultrasound embodies waves of mechanical attributes that require an elastic medium for their movement and spreading. Ultrasound waves instigate via an oscillation pattern in an equilibrium position, channeled with energy shift from one to another particle. These longitudinal genre waves travel through a certain medium in a continual compression and rarefaction motion, dispensing pressure differences in the medium, which yields benefits of interest (de São José et al. 2014).

6.7.1

Principle

The fundamental principle of low-power ultrasound comprehends propagation of sound waves across the food matrix, bearing mechanical character inducing alternate compression and decompression, distinguished by specific wavelength, velocity, frequency, pressure, and time-period (Dolas et al. 2019). When specific sound waves knock onto the surface, it stimulates drifting in the velocity and attenuation of the sound through absorption and scattering mechanisms. Though comprehensive results from different ultrasound frequencies are conclusively associated with the evoking cavitation in the treated medium, viz. build-up of vapor bubble, which violently outbursts in low-frequency applications, which causes liberation of high pressures (>500 bar) and temperatures (up to 5000  C) provoking high shear forces. Cavitation also is steadier (less vigorous wreckage of smaller bubbles) when engaging higher frequency ultrasound, translating into more micro-streaming (Rastogi 2011; de São José et al. 2014; Zhu et al. 2020). These cavitation events drive chemical, thermal, and mechanical effects. Chemically, it induces free radical generation as H+ and OH imputable to annihilate the water molecule in aqueous solutions. Secondly, accounts for single electron shift amidst the cooling phase and recoalescence of hydrogen atoms and hydroxyl radicals to lay hydrogen peroxide (H2O2) which possesses bactericidal action. While thermal effect (converted heat) induced by cavitation can be used fruitfully in thawing, drying, and pasteurization aids and mechanical effect due to produced mechanical shocks instigating

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disfigurement and ruination of cell structure fostering lysis, causing inactivation of enzymes on account of depolymerization effect (Ashokkumar 2015; Dolas et al. 2019).

6.7.2

System and Processing

Ultrasound waves can be classified on the basis of low- and high-energy owing to the frequencies, i.e., low energy ultrasound covers frequencies above 100 kHz at intensities below 1 W/cm2 contrary to high energy which constitutes intensity loftier than 1 W/cm2 with lying frequencies in between 20 and 500 kHz. However, the commonly adopted frequency range for ultrasonic-technology applications stands between 20 and 500 MHz. The different ultrasound-systems, frequency limits, and conditions can be put into service for a broad array of food-applications, viz. high frequency ultrasound is more often used to insight particulars on food constitution (acidity, firmness, sugar-content, ripeness, etc.) while ultrasound hosting lower frequency ranges are employed to induce cavitation bubble that bring about chemical and mechanical effects through energy generation, intending for microbialinactivation in food stuffs by triggering compression, strain and temperaturedifference in the propagating system itself (Bhargava et al. 2021). Basic lab-scale ultrasound equipment consists of an ultrasonic generator, oscilloscope, and sample room. However, most US treatments have not reached the industrial level, though US applications in food research have shown promising effects. This is mainly because ultrasonic equipment must be specifically designed for every application, which leads to a lack of appreciation by the food industry. Therefore, a collaboration between laboratory research and industrial scale is necessary for the future (Awad et al. 2012; Misra et al. 2017).

6.7.3

Applications in Food Industry

Ultrasound is a comprehensive technology with extensive applications in the food processing sector. It regulates production processes, analyses food properties, determines defects, and improves extraction, microbial inactivation. This multifarious non-thermal technology has been exercised to enhance conventional foodprocessing executions that only benefit with reduced energy and chemical requirements and moderate harmful emissions, hence catering a greener alternative (Tao and Sun 2015). Food is prone to microbial spoilage, which induces both qualitative and quantitative losses. Ultrasound has effectively been used as a technology for microbial inactivation for different food systems not only to scale down spoilage causing pathogens but without inducing any detrimental effect to nutritional and sensorial attributes of the food at ideal processing specifications, as preservation of quality

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aspect is one the pivotal component while adopting any technology. Ultrasound efficacy on the inactivation of Enterobacteriaceae bacteria in raw milk was tested and the results reported notable inactivation of Enterobacteriaceae count (1.06151 log cfu/mL) when treated with 120 μm amplitude at 60  C temperature for 12 min (Juraga et al. 2011). On exposure of apple juice to ultrasound, obtained results displayed highest enzyme (polyphenolase, peroxidase and pectin methyl esterase) inactivation with 20 kHz frequency at 60  C for 10 min.; and microflora (Abid et al. 2014). In a different investigation, a significant reduction in residual enzyme (POD, PME, and PPO) activity as 4.3%, 3.25%, and 1.91%, respectively, was reported when pear juice was subjected to sonication (65  C for 10 min) (Saeeduddin et al. 2015). Microbial inactivation is attributable to the acoustic cavitation phenomenon that induces damage to spoilage cell walls, causing pathogens. Though, certain key factors determine the inactivation efficiency of treatment, such as amplitude, exposure time, temperature, food composition and volume under treatment, target micro-organism (Gallo et al. 2018). Ultrasound technology also finds its utilization as technology to enhance the quality and shelf-life of different food products. In a study, Johansson et al. (2016) reported lipid oxidation derived volatiles below the human sensory detection level in all cases with no oxidation observed in milk treated with ultrasound (frequency: 1 MHz, 348 W and 2 MHz, 280 W) technology. Other studies also reported an increase in total phenols, antioxidant activity, flavonoids in fruit and vegetable-based juices treated with ultrasound (Abid et al. 2013, 2014). Ultrasound, owing to its principle, confers homogenous heat transfer, which ultimately benefits various food processing operations such as thawing, drying, freezing, and crystallization. It has been promoted for advanced results compared to traditional methods (Gallo et al. 2018). The effect of ultrasound (40 kHz) pre-treatment for 20 and 30 min on drying pineapple slices was analyzed. The obtained results displayed an improved drying rate and accordingly reduced drying time compared to the untreated sample. Pre-treatment for 30 min emerged to induce the least total color change and lower browning in the sample during drying (Rani and Tripathy 2019). In a different study, the efficiency of ultrasound (40 kHz; 30 min) pre-treatment for the vacuum freeze-drying of okra was analyzed. The obtained results reported for improved drying rate of the sample after ultrasound pre-treatment is considerably better than that of the un-pre-treated sample. Improved drying rate is attributable to the ultrasound-induced micro-structure transformations, which stimulated micro-pores formation among cells. Also, ultrasound pretreatment obliterated the structure of fiber ducts, which slackened tissues and improved porosity, which ultimately led to increased water diffusion from inside to outside of okra tissues. Also, okra pre-treatment with ultrasound displayed lesser chlorophyll degradation (5.05%) and higher total-phenolics, flavonoids, and pectin content compared to other un-pre-treated and other methods (Xu et al. 2021). Extraction is a basic process for the separation and recovery of bioactive compounds from bio-matrices. Ultrasound is efficiently used as extraction technology from different plant sources. Ultrasound-based extraction approach culminates in ultrasonic energy (>20 kHz) engaging either ultrasonic bath and/or ultrasonic probe.

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Ultrasound-aided extraction enacts on the cavitation bubble formation principle, which collapses and contrives higher shear, resulting in extraction enhancement. It has been widely explored for its application on natural matrices. In a study, anthocyanin extraction from purple yam using an ultrasonic homogenizer at 750 W (30  C; 10 min) resulted in improved anthocyanin content yield compared to the conventional approach (Ochoa et al. 2020). In a different study, ultrasound (40 kHz) aided polyphenols extraction from the whole mung bean, hull, and cotyledon using other solvents. The reported outcomes displayed higher yield in hull, and the obtained outcomes yield was higher than the conventional method (Singh et al. 2017). Furthermore, ultrasound technology in recent decades has been exploited in one way or another efficiently for numerous engagements in food industries. The physical and chemical effect on solid, liquid, and gaseous media has yielded better and advanced results compared to conventional methods (Bhargava et al. 2021) in areas such as cutting, meat tenderization, emulsification, filtration, de-foaming, and degassing (Tao and Sun 2015; Firouz 2021).

6.7.4

Future Prospectus

Ultrasound technology is highly investigated due to consumer demand for minimally processed food products without any chemical additives but of high overall quality and safety. The potential of ultrasound as non-thermal technology for food processing and preservation has led to its utilization in the diversity of food systems with numerous application areas. Over the decades, multitudinous research studies have confirmed the productiveness of ultrasound as a substitute and/or refinement of various conventional processing approaches in the agro-food domain. Ultrasound also confirms its utilization in combination with other technologies, which generates better results. Further research should focus on the effect of technological alliance with ultrasound on the overall quality of foods. In general, sound waves are reputed to be safe, harmless, and eco-friendly, and edge over other techniques. Though some limiting elements exist for example, high-intensity ultrasound equipment induces nutritional and organoleptic quality loss. In addition, high-energy and expensive requirement criteria limits the commercialization of techniques. Further, industrial level enactment requires parameter optimization of ultrasound technology solely or in combination with other methods for different food products and ground-level research to scrutinize acoustic treatment effect on the commercial-scale food processing. Finally, commercial-scale investigations should also focus on the consumer view towards ultrasound-treated foods, which will purposely evaluate the technology’s commercial success.

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Summary

Non-thermal techniques find potential applications in food processing industries to meet the demand for safe and high-quality food products. Some of the methods like high-pressure processing are already being commercially used in the food industry. In contrast, other applications need further studies for commercialization. Besides being used as preservation techniques, these techniques also find applications in pre-processing of various products, such as drying, freezing, emulsifying, and extracting several bioactive components. These techniques can also be used in combination with thermal processing to increase efficacy due to the synergistic effect of the hurdle technology concept.

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Viganó J, da Fonseca Machado AP, Martínez J (2015) Sub-and supercritical fluid technology applied to food waste processing. J Supercrit Fluids 96:272–286 Wang Z, Ma Y, Zhao G et al (2006) Influence of gamma irradiation on enzyme, microorganism, and flavor of cantaloupe (Cucumis melo L.) juice. J Food Sci 71(6):M215–M220 Wang W, Ma X, Xu Y et al (2015) Ultrasound-assisted heating extraction of pectin from grapefruit peel: optimization and comparison with the conventional method. Food Chem 178: 106–114 Wang J, Zhuang H, Hinton A Jr et al (2016) Influence of in-package cold plasma treatment on microbiological shelf life and appearance of fresh chicken breast fillets. Food Microbiol 60:142– 146 Wang W, Rao L, Wu X et al (2020) Supercritical carbon dioxide applications in food processing. Food Eng Rev 13:1–22 Werner BG, Hotchkiss JH (2006) Continuous flow nonthermal CO2 processing: the lethal effects of subcritical and supercritical CO2 on total microbial populations and bacterial spores in raw milk. J Dairy Sci 89(3):872–881 Wouters PC, Alvarez I, Raso J (2001) Critical factors determining inactivation kinetics by pulsed electric field food processing. Trends Food Sci Technol 12(3–4):112–121 Xu X, Zhang L, Feng Y et al (2021) Ultrasound freeze-thawing style pretreatment to improve the efficiency of the vacuum freeze-drying of okra (Abelmoschus esculentus (L.) Moench) and the quality characteristics of the dried product. Ultrason Sonochem 70:105300 Yang B, Shi Y, Xia X et al (2012) Inactivation of foodborne pathogens in raw milk using high hydrostatic pressure. Food Control 28(2):273–278 Yoon YS, Ameer K, Song BS et al (2020) Effects of X-ray irradiation on the postharvest quality characteristics of ‘Maehyang’strawberry (Fragaria  ananassa). Food Chem 325:126817 Zarbakhsh S, Rastegar S (2019) Influence of postharvest gamma irradiation on the antioxidant system, microbial and shelf life quality of three cultivars of date fruits (Phoenix dactylifera L.). Sci Hortic 247:275–286 Zhang Q, Barbosa-Cánovas GV, Swanson BG (1995) Engineering aspects of pulsed electric field pasteurization. J Food Eng 25(2):261–281 Zhang B, Xiong S, Li X et al (2014) Effect of oxygen glow plasma on supramolecular and molecular structures of starch and related mechanism. Food Hydrocoll 37:69–76 Zhang ZH, Wang LH, Zeng XA et al (2019) Non-thermal technologies and its current and future application in the food industry: a review. Int J Food Sci Technol 54(1):1–13 Zhang C, Zhao W, Yan W et al (2021) Effect of pulsed electric field pretreatment on oil content of potato chips. LWT Food Sci Technol 135:110198 Zhao W, Yang R, Zhang HQ (2012) Recent advances in the action of pulsed electric fields on enzymes and food component proteins. Trends Food Sci Technol 27(2):83–96 Zhu Z, Zhang P, Sun DW (2020) Effects of multi-frequency ultrasound on freezing rates and quality attributes of potatoes. Ultrason Sonochem 60:104733 Zimmermann U (1986) Electrical breakdown, electropermeabilization and electrofusion. Rev Physiol Biochem Pharmacol 105:175–256 Zimmermann U, Benz R (1980) Dependence of the electrical breakdown voltage on the charging time in Valonia utricularis. J Membr Biol 53(1):33–43 Ziuzina D, Patil S, Cullen PJ et al (2014) Atmospheric cold plasma inactivation of Escherichia coli, Salmonella enterica serovar Typhimurium and Listeria monocytogenes inoculated on fresh produce. Food Microbiol 42:109–116

Chapter 7

3D Printing: Technologies, Fundamentals, and Applications in Food Industries Mohammed A. Bareen, Jatindra K. Sahu, Sangeeta Prakash, and Bhesh Bhandari

Abstract The current work aims to consolidate and analyze the technological progressions in three-dimensional (3D) food printing (3DFP) that holds the potential of personalization and customization of food. Important reasons for the success of 3D printing (3DP) in food applications are the driving factors like maintaining competitiveness through superior technology, dynamic growth, and the role played by researchers in strategically expanding the appropriate raw materials. 3DFP claims to accomplish a sustainable food production process by removing the eminent flaws in the conventional production practices. Another striking feature of 3DFP has been its role to include novel food material into specific diets for people with special dietary needs. By adopting this emerging 3DFP technology, an alternative nutritional control arrangement stipulating an economically healthier choice could be established. With 3DFP, it is possible to create products with great value, making it a novel technology of the future for the industrial customization of many food products. In this work, information on the operation of the existing 3DFP techniques including their most recent advancements is congregated and analyzed. Further, various reports on process/product variables that impact feature generation of edible materials are briefly explained. Further, it discusses the potential implications of the 3DFP technology in ameliorating different domains of food sectors and future scope in mass fabrication.

M. A. Bareen Food Customization Research Lab, Center for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi, India School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia J. K. Sahu (*) Food Customization Research Lab, Center for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi, India e-mail: [email protected] S. Prakash · B. Bhandari School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD, Australia © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_7

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Keywords 3D printing · Food texture · Food structure · Food customization

7.1

Introduction

3D printing (3DP) is a prevalent name of the technology that is a branch of the broader group of additive manufacturing (AM) techniques. It is also known as rapid prototyping (RP) or layer manufacturing technology. The fabrication method in 3DP involves data transfer from a computer-aided design (CAD) file, to replicate an object into a 3D model by building up successive layers of the printable material. The material loss during the fabrication is minimal with a rapid production rate, presenting a phenomenal advantage over subtractive manufacturing methods (Jyothish Kumar et al. 2018). Advantages of this digital mass manufacturing technique include high precision and repeatability, favoring a wide range of materials and large production capacity. Considering these competencies the manufacturing method is being utilized in diverse fields including mechanical engineering, aeronautics to design sciences, biomedical engineering, pharmaceutical industry, and biotechnology (Krujatz et al. 2017). Recently, the technology has been well acknowledged in food industries for the design of novel food products with improved textural, structural, functional, and sensorial quality attributes (Sartal et al. 2019). The way food is manufactured has evolved remarkably over the last few years. 3DFP technology is one of those upcoming manufacturing techniques that is quickly taking over the conventional processing techniques, owing to its ability to fabricate food of intricate patterns with fidelity, achieve unconventional texture, and structure along with personalized nutrition content (Rayna and Striukova 2016). It, further, eliminates the labor-intensive step-by-step extensive unit operations needed in the energy-inefficient conventional food production methods. The introduction of 3DP in food, to fabricate food via design made on a computer, can be associated with the use of 3DP as a “free form fabrication” method developed in 2001 that claimed food to be a substantial material for AM (Wu 2001). The investigation was aimed at extrusion printing of a mixture made from starch, yeast, corn syrup, and frosting. Eventually, a commercial 3D food printer termed as Fab@home™ constructed by Cornell University ventured into the market in 2010 (Lipton et al. 2010). The developed printer was a single-nozzle extrusion-based system employing a large open access, online model directory for easy design, and development of complex food geometries. Following this, deliberate research rendered in this field inspired the development of numerous commercial 3D printers around the globe. Hitherto, approximately 50 companies are involved in the production of printers for food printing. During 3DP, the properties of materials employed play a substantial part in defining the quality and acceptability of the end product. Food material is a heterogeneous system of many components with a wide deviation in the physicochemical characteristics, making it implausible to print all categories of food with just one standard printing process. Addressing this issue, several works have developed edible printing material with peculiar texture and design either by enhancing the

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material characteristics through additives and/or optimizing printing variables. Various food like chocolates, cereal products, dairy, meat gels, turkey meat puree and scallop, orange concentrate, and processed cheese (Lipton et al. 2015; Lanaro et al. 2017; Azam et al. 2018; Le Tohic et al. 2018; Severini et al. 2018; Dick et al. 2019; Ross et al. 2019) have been reported in the literature. Recent developments in 3DFP technology have made it possible to manufacture 3D food printers at a low cost, making them affordable to residences, restaurants, and hotel industries. As seen irrefutably, 3DFP has the ability to mass manufacture personalized food with unique functionality that is incomparable to the conventional production methods even with advanced processing technologies and process control. Although the convenience and benefits of the 3DFP are unequivocal to the hackneyed food manufacturing methods, some prevailing challenges such as consumer acceptability and awareness among producers still need to be addressed before it is recognized as an ideal food fabrication method. Over the past few years, studies on innovative food manufacturing processes have been considered to address productivity, personalization, and environmental issues. 3DFP technology with its ability to deliver personalized nutrition and the eco-friendly process could aid in the active replacement of in-use processing methods. 3DFP has evolved significantly over the decade as “globalization” widens food availability and technology integration. While several research works have been published in the literature, the majority of which emphasize on investigating the different characteristics of food materials to improve their printability. Very few attempts have been made to develop a universal food printer committed to largescale production of customized foods integrating the best available AM techniques. The objective of the current chapter is to provide an overview of operation planning for 3DP, the potential relevance of various AM techniques in printing food, and analyze published articles related to the advancement in the state of science and applications of 3DFP with the aim of gaining insight into future possibilities of developing novel 3D printer to design functional foods.

7.2

Operation Planning of 3D Printing

The basic objective of employing 3DP is to access its capability attributed to the effortless and economical manufacturing of any complicated build for food fabrication. A 3DFP process can be divided into well-defined sequential steps necessary to build 3D printed constructs using raw material in the form of liquid, solid, or powder (Fig. 7.1). These steps are imperative regardless of the AM process selected for fabrication based on the material properties. 1. The primary task is to design the required 3D model with the help of design compatible systems like AutoCAD, Blender, CATIA, or Solid Works. This is performed by either generating the required 3D illustration in conceptual design (CAD model) or analyzing a real-world object to extrapolate its surface pattern

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3D printed product

3D Prinng

Generate G code

Convert to STL

Prepare a CAD 3D

Fig. 7.1 Schematic diagram of a typical 3D food printing process

and appearance through a 3D scanner and recreate a complete 3D model, an approach recognized as reverse engineering (Chua et al. 2010). A good model should be compatible with the printing parameters of the process and used material(s). This CAD file is saved and exported to an STL (Stereolithography or Standard Tessellation Language) format. STL is a standard file type utilized by most 3D printers. It is a triangular representation of the object modeled in CAD. 2. Following modeling, slicing operation is practiced by adopting any slicing software to slice the CAD model into the uniform stack of parallel planes. Subsequently, a G-code is generated, which is a computer numerical control (CNC) programming language. It encompasses instructions about the print location, a path to be followed, and the movement speed of the motors in the printer during printing. Based on the literature review, Cura and Slic3R software packages have been extensively used for food printing. However, numerous options are available online on open-source platforms which perform the same function. The G-code has a huge impact on the smoothness as well as the mechanical

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robustness of the printed product. The software used requires alteration of printing and non-printing movements such as slice thickness, nozzle temperature, infill percentage, retraction time, and printing speed suitable to the consumer needs. A food material being an association of multiple biological components would require a robust slicing software that can optimize the number of layers and layer size required for an accurately printed object from the equipped CAD model. An optimized slicing software specifically for food printing is yet to be developed. Only one review related to optimizing the 3D model building and slicing process is found in the literature for food 3DP (Guo et al. 2019). After slicing, printing commands in the form of G-code are then loaded into the printer to fabricate the desired model. The editor software packages such as Arduino IDE, Reptier-Host, and Marlin are present in many printers, which basically read the G-code (input) and convert the command into movements (output). Selection of a printer suitable to material inks is a tedious task, as the commercially available printing platforms only allow printing of a specific type of material with characteristic properties. Therefore, it is important to fathom the currently available technology explicitly in terms of applicable material and functioning mechanisms. The next section provides an insight into the different methods employed, their working mechanism, and recent advancements in terms of 3DFP.

7.3

Potential Relevance of Various AM Techniques in Printing Food

The 3DP technology, since its inception, has undergone ample advancements establishing it as the potential technology of the future. This has led to a mark where 3DP has gained prominence as a competent approach for digital modeling and fabrication of novice material. One illustration is the pertinence of 3DP in the food industry; the technology presented an excellent opportunity for digital food fabrication, revolutionizing the food production process. It has been utilized in unprecedented ways by augmenting the process variables as well as the material properties to address a diverse number of challenges in food processing. The major influential parameters established by the research community for obtaining a high-quality 3D printed product are material characteristics, specifics of printing technology, and subsequent posttreatment. Out of these parameters, selecting the right technology that is categorized in many classes conditional to the energy specification, spatial configuration, operating mechanism, etc. is of prime importance. Based on the American Society for Testing and Materials (ASTM) standards, AM processes can be categorized into seven broad headings such as VAT photopolymerization, material jetting, binder jetting, material extrusion, powder bed fusion, sheet lamination, and directed energy deposition. The individual process has its corresponding strong and weak points depending on the material employed and process parameters. Food application has specific requirements of material that should be considered;

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Fig. 7.2 A schematic classification of 3D printing technologies applied in food

therefore, only four 3DP processes have been effectively applied for developing edible 3D constructs. These include material extrusion, material jetting, binder jetting, and powder bed fusion through selective laser sintering and melting (CandyFab 2017). Figure 7.2 illustrates the schematic classification of 3DP technologies applied in food as per the nature of selected food materials and type of driven mechanism.

7.3.1

Material Jetting

7.3.1.1

Fabrication Technique Description

Material jetting (MJ) (method is commonly referred to as inkjet printing) printer originated as an office accessory and has now perfected as a mechanism in the industrial mass fabrication process to 3D print products. It was one of the first AM processes adapted for 3DP and has been used commercially since 1994 (Gibson et al. 2015). The material/ink used in this process is deposited over the surface of a substrate using a printhead. The added advantage with this deposition is it happens over a large, desired area at once and not at just a particular point. The employed printhead has hundred to thousand tiny nozzles, each one command assisted for precision material deposition. The schematic of an MJ printing process is illustrated in Fig. 7.3. MJ printing technique allows synchronized deposition of diverse material. Typically, in a printhead of MJ printing, nozzle sizes are in the range of 20–30 μm. Liquid droplets produced are in the range of 10–20 pL. Minute nozzles allow smaller droplet deposition which delivers a better resolution final product. The printhead can be operated in two methods: continuous (Co-MJ) application and drop-on-demand (DD-MJ) application.

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Fig. 7.3 A schematic diagram of material jetting (MJ) process

In a Co-MJ deposition head, the printing material is pressurized through an orifice using a high-pressure pump (Fig. 7.4), creating a continuous flow of droplets causing the Rayleigh instability phenomenon in liquid jets assisting the flow of material (Stow and Hadfield 1981; Shastry et al. 2004). Prior to the deposition, the material is mixed with additional components that provide conductivity to achieve the desired flowability. When the material in the form of droplets flows out of the nozzle, they pass through the deflector plates (Fig. 7.4). Individual droplets are steered in the desired direction by applying a potential to the electrostatic plates which avert the charge of conductive material droplets to either the printing platform or the reservoir for recycling. A material surface tension of 25–70 N/m is conferred for Co-MJ printing (Lloyd and Taub 1988). The drop formation speed is in the range of 80–100 kHz, which is faster than DD-MJ dispenser. Application of charge conducive agents to the material is requisite in Co-MJ process which makes it a little less preferable in 3DFP. The DD-MJ dispenser system uses a thermal or piezoelectric mechanism to generate material droplets on-demand as shown in Fig. 7.5. An electric pulse is passed through a semiconductor in the dispenser, causing inner temperature escalation and consequent bubble formation. Maintaining consecutive pulses will induce evaporation, nucleation, and enlargement of the bubble, which provides the required energy for a drop ejection. Each pulse survives a few split-seconds and increases the plate temperature to approximately 300
C. The ejected drop is positioned on the substrate with precise control. Inside the dispenser, the piezoelectric chamber accommodates a piezoelectric quartz crystal which controls the droplet ejection mechanism. The applied external voltage causes an abrupt quasi-adiabatic decrease in volume of the chamber via piezoelectric action, contributing to the pressure required for ejection. The volume of the droplets range from 1 pL to 1 nL with

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Fig. 7.4 A schematic diagram of binary deflection process in continuous material jetting (Co-MJ) dispensing system

equivalent diameters in the range of 10–100 μm. The first commercial printer to apply the MJ process for food fabrication was developed by De Grood, it employs a DD-MJ mechanism for liquid food deposition on different food substrates (De Grood and De Grood 2013).

7.3.1.2

Layer Consolidation Principle

For any material to be compatible for use in 3DP, it should have appropriate characteristics like, it should follow a particular flow regime, adhere to the substrate and previously deposited layer, and finally should solidify quickly after deposition without spreading (Lanaro et al. 2017). The flow dynamics of material in the MJ process can be characterized with the standard equations used for any incompressible Newtonian fluids. Material droplets when ejected through the nozzle are analogous to the fluid dislodged from a small opening, the flow of which can be defined by employing the Navier–Stokes and continuity equations. In literature, using these

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Fig. 7.5 A schematic of drop-on-demand (DD) material jetting dispensing system

equations, certain parameters are formulated that have a significant effect on the droplet jetting mechanism, those are: • Ohnesorge number (Oh): A dimensionless number described as the ratio between viscous forces to surface tension and inertial forces, a significant parameter that illustrates the competence of the material for the MJ process. Oh ¼

η 1

ðγρaÞ2

¼

pffiffiffiffiffiffiffi We Re

ð7:1Þ

where ρ, η, and γ are the density, dynamic viscosity, and surface tension of the fluid, respectively, a is a length—usually the diameter of a nozzle, Re is the Reynolds number, and We is the Weber characteristic number (Bhola and Chandra 1999).

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• Minimum droplet discharge velocity (vmin): Duineveld recommended the minimum velocity required by a material droplet to overcome the surface tension produced at uncovered nozzle tip is generated by inertia of liquid droplets (Duineveld et al. 2002). This typical minimum velocity of drop generation is given by Eq. (7.2).  vmin ¼ 2

γ ρa

12

ð7:2Þ

• Maximum droplet discharge velocity (R): Upon jetting, the material drop has an impact on the substrate causing to either settle or splash. To avoid splashing of the droplet after the impact, Stow and Hadfield (1981) proposed a parameter (R), above which splashing occurs. It is important to consider that splashing also differs with surface roughness R (Eq. 7.3). R¼

7.3.1.3

pffiffiffiffiffiffiffipffiffiffiffiffiffiffi We Re

ð7:3Þ

Impact of Process Variables on Feature Generation

The MJ process is endorsed to create a superior quality output with excellent accuracy, which would require the material to be easily ejected from tiny orifices. The final quality of the fabricated product in any AM technology is evaluated by its accuracy to the designed model and stability of the printed constructs. The viscosity of the material functions as a key determinant defining its flowability, regulating the deposition rate, reducing unnecessary spreading, and providing uniformity and smoothness of the printed object. In food applications, the MJ printing process was initially used for creating 2D structures as the edible products that inherently possess the necessary rheology for MJ were limited; typical materials used for MJ have viscosity in the range of 2–6.8 cP (Shastry et al. 2004). Therefore, the technology appeal was restricted to be used as a surface filling and image designing process. The decorations and fillings were done on edible substrates such as cookie, cake, and pizza. Several patents report the development of new edible formulations that are suitable for the MJ process, emphasizing the role of ink flowability in the final resolution of the printed image (Shastry et al. 2006; Edwards and Hills 2012; Cavin et al. 2016). The temperature of the ink is a key determinant in the MJ process, as it is having a direct effect on the viscosity of the ink. At low temperatures, spreading of ink is reduced as a result of lower surface energy, establishing uniformity and impressive finish to the print (Willcocks et al. 2011). The optimum temperature that would administer a high precision final image varies with the ink formulation. The compatibility of the edible substrate with ink after deposition influences the interaction performance that has a considerable impact on the resolution of the final image. Largely edible ink preparations include a liquid solvent like

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Table 7.1 Summary of selected works involving material jetting (MJ) technique in 3DFP Ink formulation Linseed oil core with carrageenan shell, Mint syrup core with a wax shell

Device TNO’s encapsulation printer

Sugar and starch powder mixtures along with different flavor binders (water or alcohol based) and an edible colorant

Numerous printers, one example is Fujifilm Dimatix’s Merlin

Liquid chocolate (Hershey’s, shell topping)

Electrostatic inkjet printer

Study observation Their invention generates highly monodispersed microcapsules with very well-defined shells and a capacity of 100 L/ h. The printhead has 500 nozzles and allows the use of a variety of materials, including waxes and fats, polymers, aqueous solutions, emulsions, and dispersions Newman and Newman (2015) The inks are designed to be printed on porous food surfaces. Printing can be done before or after baking, thus making inks suitable also for printing, deposition and decorative food and non-food applications Pallottino et al. (2016) The authors have used the basic technology of electrostatic inkjet 3D chocolate printers to create complex patterns with high precision in chocolate by controlling the printing conditions Takagishi et al. (2018)

water, alcohol, and an edible pigment (Ryan 2009). The compatibility can be enhanced by glazing the substrate exterior with a suitable binder or a layer prior to jetting the ink onto the substrate, this entails a proper comprehension of the dynamic interaction between ink and substrate surface (Willcocks et al. 2011). Surface roughness aids in the strong adhesion of the ink onto the substrate. In previous work, a layer of gums and other surface simulants like polyglycerol oleates and polysorbates were credited to alter the chocolate surface effectively to support the production of high-quality precise imageries (Mandery 2010). Besides the ink viscosity, software packages employed for model development and machine control are also key factors in the MJ process. Considering the multicomponent nature of the food material as a potential for blockages and probable negative effects caused by pressure and shear on droplet formation of the material, nozzle diameters reported in the literature are usually larger than 1 mm and seldom below 0.5 mm. As the technology progressed, many image processing programs were written or modified befitting for food printing. In literature, liquid chocolate with or without functional ingredients is the most frequently used ink in the food MJ process. Recently, an electrostatic MJ printer assembly was constructed to print chocolate on edible film with high-precision (Takagishi et al. 2018). Selected examples from the literature highlighting research on food printing accomplished successfully using the MJ process and their findings are summarized in Table 7.1.

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7.3.2

Material Extrusion

7.3.2.1

Fabrication Technique Description

The material extrusion (ME) technique has gathered a great demand in the market due to its ability to create intricate structures with a simple and effortless operating procedure. The technique involves material deposition through a single- or multiplenozzle system guided by a numerical command to produce 3D constructs by stacking layers of material (Fig. 7.6). This mechanism allows for the material to be forced out under a continuous pressure coupled with persistent nozzle movement, making it a competent method to build complex geometries with great accuracy. Along with easy process control, it offers great flexibility with the material formats involved for fabrication. Also, contrary to the inkjet printing process where the material is ejected through tiny nozzles inside the printhead, ME functions on the extrusion of the material under constant pressure allowing easy flow of colloidal material (with a total solid in the range of 5–50%) to produce 3D structures. The ME printers’ configurations that are frequently used for food fabrication include Cartesian, Polar, Delta, and SCARA (Selective Compliant Assembly Robot Arm) (Sun et al. 2018). Delta and Polar configurations are the most commonly used owing to Fig. 7.6 A schematic of 3D printing: material extrusion (ME) process

Extruder assembly

Piston

Material supply

Printing nozzle

Deposition of self -supporting layers

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their high material deposition rate, economic affordability, and small build. Examples of commercially accessible ME food printers and their products are described in Table 7.2. Depending on the format of food material, paste-like or powder, different dispensing systems are engaged in ME printing. For liquid/semisolid food materials, pneumatic and mechanical (screw or piston) dispensing systems are engaged. A schematic diagram of the various dispensing mechanisms adapted for 3DFP is depicted in Fig. 7.7. Screw-driven arrangement is a continuous extrusion system where the material is loaded through a hopper and an auger screw is used to force out the material forming a layered structure. Additionally, the auger mechanism aids blend the material as it is being deposited to ensure homogeneity and avoid phase separation. Intricate designs can be easily accomplished using a screw-based system as it facilitates more spatial administration while printing (Sun et al. 2018). The auger-based system is preferred to print material with high viscosity. In a positive displacement-based system, a syringe/barrel is filled with printing material, and a piston controlled by a stepper motor is employed to drive the extrusion process. The reported deposition rate ranges from 10 to 104 μL/h (Zhao et al. 2015). Better resolution can be obtained using this dispensing mechanism as there is a constant positive displacement of the material in the syringe. A pneumatic-based system uses air pressure to extrude materials kept in the enclosed cartridge. It is relatively faster than the other dispensing systems as it can instantly be pressurized and unpressurized on command. The low-viscosity fluids/semisolid material is fitting to be used for this ME dispensing system. Furthermore, these three systems are also used in combination with print novel materials created for specific purposes. Broad categories of food materials ranging from high-viscosity pastes to liquids have been reported in the literature that are articulated to obtain a good quality printed construct using mechanical extrusion system. A recent study related to the design and development of 3D printed food for people with swallowing difficulties reported the use of a dispensing system involving a piston top and a fine nozzle operated with pressure from an external pneumatic pump (Kouzani et al. 2017). An investigation on print fidelity of soy protein isolate mixed with different concentrations of gelatin and sodium alginate was reported to have good mechanical stability and that it could be used to print more exquisite products (Chen et al. 2019). Both rotation and positive displacement material dispensing systems have persistently been explored to expand the variety of food material suitable for 3DP due to their capacity to perform at high pressure, 100–600 kPa (Hamilton et al. 2018; Schutyser et al. 2018). An innovative mixture of pneumatic and screw-auger-based materialdispensing systems was established by Ghazanfari et al. (2017). Figure 7.8 illustrates the design of the auger valve, which uses pneumatic pressure to transport material to the printhead where an auger is used to deposit the ink (Ghazanfari et al. 2017). An analogous novel arrangement combining two dispensing systems was employed in the modularized printer—xPrint, assembled by MIT media lab for dispensing a variety of materials (Wang et al. 2016). These printers can be used in food printing by slight amendments to create functional foods with striking designs.

Powder bed fusion (PBF)

Printing process Material extrusion (ME)

Binding is based on rheological properties (non-phase change extrusion)

Binding through • Thermal-gelation • Chemical/ ionotropic/enzymatic cross-linking • Complex coacervate formation Melting of powder layer using a laser source to fuses desired regions of the powder together Hot air is utilized as a sintering source to fuse powder particles and form a solid layer

Soft material extrusion

Gel-forming

Selective hot air sintering and melting (SHASAM)

Selective laser sintering (SLS)

Description Binding of melted layers by solidification after deposition

Method Melting

Icing sugar, Nesquick powder

Sodium solution, calcium chloride, xanthan, and gelatin with starch and protein bases

Cheese, dough, sauce, meat purees, marzipan

Fabricated materials Chocolate

Table 7.2 List of 3D printing techniques employed in food

CandyFab

TNO’s Food Jetting printer

Commercially available printers Choc Creator, Focus, XYZ 3D food printer, Qiaoke, Porimy, Fouche chocolate printer Foodini, BeeHex Robot pizza printer, Barilla 3D pasta printer, Procusini, PancakeBot Nufood 3D Food Printer

More freedom to build complex food items

Support not required

Multiple combinations and degree of freedom for food Low cost, easy method

Advantages Supports large array of foods

Rough surface finish

Applicable to restricted food items with low melting point

Long fabrication time

Appearance of seam line between layers

Disadvantages Low precision level

CandyFab (2017), Godoi et al. (2016), Kietzmann et al. (2015), Sun et al. (2018), Malone and Lipson (2007), Diaz et al. (2015)

References Lipton et al. (2010), Natural Machines: (The Makers of Foodini, a 3D Food Printer) (2020), Porter et al. (2015), Sun et al. (2015), (Yang et al. 2017), Periard et al. (2007), (BEEHEX, PASTA OF THE FUTURE? IT’S PRINTED IN 3D BARILLA PREVIEWS ITS THE PROTOTYPE AT CIBUS 2016 | Barilla Group).

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Binder jetting (BJ)

Material jetting (MJ)

Agglomeration of powder particles

Continuous deposition

Drop-ondemand

A liquid binder sprays two consecutive evenly spread powder layers to a predefined shape

Deposition of a stream of droplets onto a substrate from a syringe-type printhead A high-pressure pump directs the liquid ink through an orifice, creating continuous ink flow printing

Sugar-based sweets

Sugars, corn flour, flavors, puree, or pastes

ChefJet,

–

ChefJet, FoodJet

Support structures are included automatically in layer fabrication

No wastage of material Easy method Versatile shape fabrication ability Multiple colors Able to create complex structures Precise

Good accuracy and resolution Short time Powder can be recycled High resolution and accuracy

Rough or grainy appearance Fragile end products Postprocessing required to remove moisture and improve strength

Limited application in decoration and surface fill on substrate

Complicated process

Expensive machines

Diaz et al. (2015), 3D Systems (n.d.), Porter et al. (2015), Wegrzyn et al. (2012)

Murphy and Atala (2014), Sun et al. (2015)

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Fig. 7.7 Types of dispensing systems used in ME 3DP

b Pressurized air inlet

a

Liquid

Heat radiation source

Extruder and nozzle

Tank

Part Substrate

Liquid

Material barrel

Servo motor

Part Substrate Auger Material Outlet

Fig. 7.8 A schematic of an (a) on-demand extrusion process and (b) auger valve. (Adopted from Ghazanfari et al. 2017)

7.3.2.2

Layer Consolidation Principle

For a successful extrusion-based process, it is imperative to comprehend the rheological characteristics like viscosity (η), yield stress (τ0), shear recoverability, storage modulus (G0 ), and loss modulus (G00 ). Viscosity determines the flowability of the material, which is an important parameter in extrusion-based printing. The relation between the viscosity of the material and its flowability characteristics can be described by the Hershel–Bulkley model (Herschel and Bulkley 1926) as per Eq. (7.4).

7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries

τy ¼ τ0 þ Kγ n τy ¼

yΔP 2l

213

ð7:4Þ ð7:5Þ

where n is the shear-thinning exponent, K is the consistency index, τ0 is yield stress, τy is the shear stress, and γ is the shear rate. Numerous studies have reported the crucial role of n and K in determining printability. The yield stress determines the capability of printing material to adhere to the previously printed layer which enables self-supporting 3D structure fabrication (Lille et al. 2018; Joshi et al. 2021). Considering the pressure gradient due to external pressure applied is ΔP along the barrel length (l), then the developed yield stress can be calculated using Eq. (7.5), where y is the radial position within the nozzle (i.e., y ¼ 0 at the center axis and y ¼ R at the nozzle wall). The storage and loss modulus are the measure of viscoelastic behavior of materials. These values determine the post-processing stability of the printed construct, i.e., resistance to deformation after deposition. Use of any material for the ME process should possess two essential characteristics: demonstrate good flowability (shear thinning behavior) during extrusion and withstand distortion after layer deposition. The former can be altered by enhancing rheological properties of the material through temperature and pressure, and the latter can be controlled by inclusion of additives into the printing material. The material inside the syringe during the extrusion through a nozzle is characterized by shear rates of order 100 s1 (Seppala et al. 2017). This causes a significant change in the orientation of components, derangement of loose bonds, etc. Food materials are usually a heterogeneous mix of many components which exhibit highly variable rheological properties, and these high shear rate and temperature fluctuations during printing make it strenuous to establish one common optimum condition for printability of all food. The printed layer combining phenomena for the construction of intricate models in food material printing is governed by rheological behavior (in case of gelling-assisted extrusion) and thermal performance (in case of fused deposition extrusion).

Gelling-Assisted Deposition Edible gels are viscoelastic substances, and numerous gelled products are manufactured commercially. For 3DP, the gelation is induced in the material prior to or during deposition to cause the adhesion in between the deposited layers. The consistency and form of gel formulations are tailored to desired requirements by altering their rheological characteristics. On the application of specified yield stress (gel yield point), the formulation exhibits a shear-thinning behavior, making it suitable for ME printing. Knowledge of the gelling process in the printing material is important to modify its rheological properties such as elasticity and shear recoverability for better 3DP applicability. The gelling process can be categorized into five sections: heat-induced gelling, covalent cross-linking, enzymatic cross-linking,

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composite coacervate formation, and inotropic cross-linking. Several food gels such as cheese, dough, meat paste, and jelly which have different gel formation mechanisms were reported compatible for use as ink in the ME extrusion process to create explicit patterns with good stability (Kern and Weiss 2018). A recent study attempted to correlate the rheological behavior of different food-grade hydrocolloid gels and their printing veracity results established two parameters: the phase angle (δ) and the relaxation exponent (m), which can be used to determine the final product quality (Gholamipour-Shirazi et al. 2019).

Fused Deposition Extrusion In a fused deposition extrusion (FDE) process, semisolid material is deposited in the form of layers and welded together due to the thermally induced bonding. The softened and melted material is deposited in a X-Y plane through a nozzle with a typical diameter of 400 μm which solidifies immediately after extrusion to form the desired feature generation. The manifestation of changes due to the heat in edible material happens either at the glass transition/crystallization or melting temperature of the material. This happens in materials that are rich in fat or amorphous sugar content. Therefore, in food applications, the FDE method has primarily been used for printing chocolates (Malone and Lipson 2007; INFINITUS (CHINA) COMPANY LTD. 2019; Mantihal et al. 2019). Temperature control is vital in this type of ME process as the essential changes that facilitate contact between layers only happen when the material attains its phase change temperature. If the temperature is lower than the required phase change temperature, breaking and cracking arise in the final product, and if the temperature is above the required threshold, distortion occurs due to inadequate adhesion among deposited layers.

7.3.3

Impact of Process Variables on Feature Generation

The application of the ME process for food fabrication is a relatively new challenge. However, substantial research investigating the critical variables that have an impact on the printing accuracy is established, these include material attributes, choice of printer, and printing variables settings (Yang et al. 2017; Feng et al. 2019). The importance of the rheological performance of printing material to obtain uniform printed objects has been described above (in layer consolidation principle). Some of the crucial printing variables that have an impact on the printability of the material have been established by the researchers; these include nozzle diameter, layer height, extrusion temperature, and print speed (Liu et al. 2017). The importance of these variables and their effect on 3D printability is detailed below. The nozzle diameter of the printer is established to have a direct effect on the surface finish and smoothness of a printed product. The best possible resolution and a smooth finish of printed constructs are obtained by using the minimum nozzle

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diameter that permits a uniform material extrusion. Liu et al. demonstrated that printing of complex egg white protein with a larger nozzle diameter (1.5 and 2.5 mm) yielded a poor-resolution product due to over extrusion of material (Liu et al. 2019). For the same material, a lower nozzle diameter (1.0 mm) produced a decent surface quality product and good shape retention post extrusion. The nozzle diameter also determines the amount of pressure intended for printing, for a given material with specific viscosity, the pressure required for extrusion increases with decreasing nozzle diameter. Yang et al. established that reduced nozzle diameter results in an increased elevated pressure region in a nozzle which would cause instant swelling after extrusion (Yang et al. 2019). They established that with the increase in nozzle diameter from 0.2 to 0.5 mm, the pressure on the material in the flow channel increased from 2.5  106 Pa to 4.95  107 Pa. The material height or the layer height is the material layer width between the nozzle aperture and the printing platform. This was considered as a key printing parameter to determine the feature generation until recently, when Yang et al. (2018) have performed comprehensive tests to confirm that it has the same impact as that of the nozzle diameter. The authors interpreted that in an optimal printing condition without over or under extrusion, the extruded layer diameter is the same as the nozzle diameter and the inadequacies in layer adhesion due to a delayed deposition can be avoided by restricting the nozzle height to as low as possible. Moreover, the ink temperature during extrusion has a strong influence on the capacity of the extruded layers to adhere after deposition. The validation is the direct association between temperature and viscosity of the food. Predominantly in gelling-assisted extrusion, the printing temperature is a crucial factor to maintain constant flow during the printing. The print speed and extrusion rate have a significant consequence on the rate of 3D construct fabrication with appropriate veracity. Derossi et al. (2020) examined the effect of high print speed, extrusion rate, and some nonprinting variables on the printing fidelity of the designed model using wheat dough. The results showed that in the case of the edible material extrusion process, at a high print speed, the corresponding screw speed must be adequately increased to ensure sufficient material deposition for accurate printing. The study found that increasing the extrusion rate to three times or by reducing the value of the “diameter of filament” command in slicing software to 1.0 mm against 1.75 mm permits acceleration in the screw rotation speed that would obtain printed product with exact dimensions at a print speed of 200 mm/s. In literature, most often for 3DP applications, a straightforward linear equation is employed to correlate the print speed and material extrusion rate as represented by Eq. (7.6) (Khalil and Sun 2007). The equation demonstrates that print speed, extrusion speed, and nozzle diameter are interrelated, needed in complete regulation to construct perfect 3D objects. A summary of selected works that explain the influence of process variables on feature generation using different edible materials is presented in Table 7.3.

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Table 7.3 Summary of selected works explaining the importance of process variables in material extrusion (ME) technique when used in 3DFP Raw material Milk chocolate

Printing method Fused deposition extrusion

Wheat dough

Room temperature extrusion

Layer height and infill percentage

Potato puree and milk

Room temperature extrusion

Printing temperature and composition of puree

Beef, salt, and guar gum

Gel forming extrusion

Percent infill and fat content

Sesame paste, chicken paste, and shrimp paste

Infrared lamp heating integrated extrusion

(1) 3D-printed without cooking, (2) 3D-printed with in situ IR heating, and (3) handmade with oven heating

Process variables Nozzle diameter, layer height, and extrusion speed

ps ¼

4 QD 2 π r N

Major findings Process variables such as extrusion rate, nozzle velocity, and nozzle height are critical for geometry accuracy of printed chocolate The layer height has an inverse effect on the layer diameter. Moreover, mechanical strength of the printed food is affected by the infill percentage The food formulations with higher consistency index (K ) and lower flow index (n) at a temperature of 30
C were the most stable Infill density (50%, 75%, 100%) contributed directly to moisture retention, hardness, and chewiness, and inversely to shrinkage and cohesiveness, with no effect on fat retention in the lean meat-lard composite layer 3D printed meat products cooked sousvide Applying heat to the meat and sesame pastes significantly affects the rigidity and printing resolution of the fabricated structure. Printing without heating leads to the extruded layers to droop and sag

References Hao et al. (2010)

Severini et al. (2018)

MartínezMonzó et al. (2019)

Dick et al. (2019)

Hertafeld et al. (2019)

ð7:6Þ

where ps is the print speed (mm/s), Qr is the material deposition rate (mm/s), and DN is the nozzle diameter (mm).

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7.3.4

Powder Bed Fusion

7.3.4.1

Fabrication Method Description

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As the name signifies, this technique applies a laser or a heat source to fuse powder material in a layer format layer by layer and finally into a required 3D model. If the heat source is a laser, then it is called selective laser sintering (SLS) (Fig. 7.9) and selective hot air sintering and melting (SHASAM) (Fig. 7.10) when the heat source is hot air. The method of operation of SLS and SHASAM is similar to only one variable being the heat source. Solidifying the powder particles with a laser beam using a computing-controlled command unlocks countless opportunities for the production of personalized objects with a great degree of freedom. In SLS, relying on slicing of the 3D model, a continuous-wave laser beam scans layer course and fuses selective powder material automatically. Once the cross-section is fused, the printing bed is lowered, and a new thin powder layer is deposited on the top using a roller. This procedure is iterated as long as the 3D design is finished. Subsequently, the unfused powder material is collected and used for the next object (Kolan et al. 2012). SLS has been an attractive and adaptable method for printing varied powder materials like metal, ceramic, and polymer (An et al. 2015; Wu et al. 2016). The added advantage of using this method is that the powder bed holds together the subsequent layers, providing a good degree of freedom to create complex shapes. The SLS procedure permits the fast, flexible, and cost-efficient 3D structure fabrication with good perfection, but is restricted to powder formulation, such as carbohydrates oligosaccharides or polysaccharides, lipids, and sugar. The

Fig. 7.9 A schematic of selective laser sintering process

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Fig. 7.10 A schematic of selective hot air sintering and melting process

multi-technology powder bed printer developed by TNO combines different powder-based AM techniques to produce food with a high degree of resolution. In one integrated system, a powder platform is provided where several liquids can be deposited to bind the particles, and the hydrated area is subsequently heated by IR beam to form a hardened layer (Diaz et al. 2015).

7.3.4.2

Layer Consolidation Principle

The layer consolidation phenomena in SLS are due to three main phenomena: solid form sintering, liquid-assisted sintering, and powder melting. A solid form sintering mechanism occurs when the laser temperature is between Tm (transition temperature) and Tm/2 of the powder particles. For liquid-assisted sintering, an additive is included in the powder formulation which will liquefy earlier than the matrix phase and gradually dissolve to join the powder particles and form the layer. This procedure is generally utilized for designing 3D structures for materials that require a high sintering temperature (Shuai et al. 2014). In the melting laser sintering process, complete melting of the powder particles achieving full density occurs in one step. Typically, metals and ceramic powders use this method of 3D fabrication. In all the methods when powder elements bind together at raised temperature, the overall surface area declines, subsequently reducing the surface energy, thus slowing down the rate of sintering (Schmid et al. 2013). For food applications, the solid form sintering method is utilized as higher layer temperature causes degradation of the temperature volatile components (Diaz et al. 2015).

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7.3.4.3

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Impact of Process Variables on Feature Generation

Several studies elucidated SLS as a very complicated process and proposed that several processing factors impact powder densification mechanism and printing veracity. The main process variables include laser energy density, spot diameter, scan speed, powder particle shape, size, and distribution. It is established that most of these variables are interdependent (Olakanmi et al. 2015). The powder densification mechanism is reliant on the laser energy density, which has a direct impact on strength of the manufactured object. To adjust the laser energy density, the laser power and scanning speed combination is changed. Lower scan speeds produce denser parts, while high scan rates produce porous and fragile structures (Gibson et al. 2015). Reduced layer thickness follows in sturdier products with good mechanical performance and lowered porosity (Amorim et al. 2014). Layer thickness has a significant effect on the average pore size, as thicker layers facilitate less fusion among particles causing less densification (Savalani et al. 2012). In an attempt to mitigate the issue of low printing resolution in printed constructs using hot air sintering and melting of sugar, it was established that changing the laser diameter from 5 to 1.6 mm enhanced the final object resolution (CandyFab 2017). It is pertinent to acknowledge that the powder properties bid a major impact on the microstructure and mechanical characteristics of the printed construct (Ziegelmeier et al. 2013). For SLS, the required powder material must be free-flowing and lumpfree. In an SLS-based printer, Aregawi et al. (2015) successfully printed many cookies using various flour mixtures such as semolina, soft wheat flour, and a flour and starch mix. Schmid et al. (2013) developed a method to produce edible objects using a binder made of a mixture of palm oil powder and maltodextrin. They concluded that the binder comprising at least two compounds that differ in their Tg or Tm demonstrated excellent performance with a high degree of resolution and precision.

7.3.5

Binder Jetting

7.3.5.1

Fabrication Method Description

Binder jetting (BJ) is a combination of MJ and powder bed fusion (PBF) techniques. The schematic diagram with all the parts employed in the BJ process is illustrated in Fig. 7.11. The basic setup comprises a build platform, a powder material supply bed, a leveling roller, and a liquid binder supply head. The powder material of which the model is to be made is fused by using a liquid binder. The designed 3D model is fabricated by sequentially depositing 2D layers of liquid binder on the selective areas of the print bed where the powder is spread using the roller. This deposition of binder fluid on the print bed is achieved using an MJ printhead to form the required geometry without melting the powder. Once the layer is hardened, the build platform is dropped by a one-layer width and powder from the feed bed is dispersed on the

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Fig. 7.11 A schematic of binder jetting process

print platform using a roller or a doctor blade. The subsequent layer is hardened by the binder fitting to the 3D model and the procedure is iterated until the 3D object is formed. This bound structure is denoted as the “green part.” Further post-process like curing or cooking is required to strengthen the physical bonding between the particles and across layers and provide enhanced mechanical stability. Unlike the PBF process where a laser or a heat source is used to combine powder particles to form a layer, in the BJ technique, a liquid binder accomplishes the same task. This makes the process appealing for the fabrication of complex 3D structures using heatsensitive edible powder formulations. The formed products are gushed with pressurized air to eliminate any unbound powder from the fabricated object. The most extensively exploited edible powder material in BJ fabrication of 3D products is sugar. Reason being powdered sugar is hygroscopic in nature ideally, and when immersed in a liquid binder, it sticks together with good adhesion and allows for easy spreading. Utilizing this technique, a commercial 3D printer called Chefjet™ is co-created by 3D Systems and The SugarLab which is capable of fabricating complicated multidimensional confections with sugar. The machine uses different edible flavors and colors as a binder to create these complex structures (3D Systems 2013). Another endeavor by 3D Systems and Brill, Inc to create an innovative professional-grade printing system for the fabrication of edible 3D figures using the BJ technology is in progress (3D Systems n.d.). In addition, various attempts are organized by the hobbyist to formulate edible structures using innovative food products that are tried and tested for BJ printing, the recipes of which are available on open-source platforms. Examples include using sake rice wine along with alcohol, and a mixture of glycerol and distilled water as a binder liquid formulations and a mixture of sugar and meringue powder, finely powdered salt, and maltodextrin as powder ink formulations. Coffee and cocoa powders have also

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shown promising results to be used as a powder to engineer 3D models using BJ printing (Vadodaria and Mills 2020).

7.3.5.2

Layer Consolidation Principle

In a BJ process when the binder liquid is ejected on the powder particles, they agglomerate to form a big porous secondary lump that is leveled using a roller to form a layer with precise width. The process of agglomeration is described as the development of large secondary particles as a result of an accumulation of primary particles over time (Palzer 2005). The powder binding mechanism depends upon the adhesion phenomenon between the particles and the liquid binder. Before binder deposition, the powder material is evenly spread by the roller, which is ensured by the altering flowability of the formulation. The flowability can be administered by controlling the powder particle size. It is suggested that a blend of particle size delivers good spread ability and post-processing strength of the printed construct rather than using just fine or coarse particles (Shirazi et al. 2015). This can be attributed to the formation of higher junction points, due to the substitution of smaller size particles in the pores created by the large particles in the printed objects. For edible powder formulations, a general tendency is to form lumps upon the incorporation of water because of the chemical reaction between the particle surfaces. External influences like temperature and pressure that can facilitate physical or chemical bonding of powder particles can be employed to alter the rate of agglomeration as well as the size of secondary particles (Dhanalakshmi et al. 2011). The binder on deposition migrates into a porous powder bed through capillary phenomenon and induces many changes to initiate agglomeration. First, hydrogen bonds among the polar components of the binder ink and powder are formed, enabling the sintering of particles to one another. Over time, the nucleation phase is initiated, dissolving the soluble components and increasing stickiness to strengthen these sinter bonds. Powder composition has a substantial impact on the final product quality, the presence of polar components like carbohydrates and proteins will enhance the hydrogen bonding between particles and a liquid binder. The moistness of the edible powder used in BJ must remain less than 6% based on the composition of the food material used.

7.3.5.3

Impact of Process Variables on Feature Generation

Process variables that will largely affect the end product properties in BJ are powder spreading rate, level of binder saturation, and layer thickness (Miyanaji et al. 2016). The powder spreading speed or powder feed rate is controlled by means of a spinning roller which travels forward and backward on the printing bed to deposit a powder layer. The deposited powder layer must be uniform without causing any distortion to the previous layer. An irregular and rough layer will contribute undesired porosity leading to the poor structural integrity of the finished construct.

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The spreading speed depends on powder particle morphology and flowability. Spherical particles exert a lower friction value when compared to faceted particles that adhere together generating more friction, therefore affecting the spreading speed. Decreased spread speed is advocated to reduce the inconsistencies; however, this would significantly raise the printing time. Due to the large Van der Waals forces, finer particles (100%. The other estimate of FAO stated that nearly 60% more food will be required in three decades to meet the food consumption for the mounting global population (FAO 2017). The food processing industry is one of the most water-intensive industries (Fig. 12.1d). Most water-intensive sectors are (a) dairy and poultry industries and (b) the fruit and vegetable processing units (Mekonnen and Gerbens-Leenes 2020; Alexandratos and Bruinsma 2012). The water footprint of an animal-based food product is greater than the water footprint of any plant-based product having similar nutritional value. For instance, it is estimated that animal food production is up to 20 times more water than for vegetables or fruits (Bhagwat 2019). To decrease water minimization in the food sector it has been suggested to utilize recycled water and replace water-consuming processes with efficient technologies (Nemati-Amirkolaii et al. 2019). It is noted that a typical Indian household contains an abundance of kitchenreleased waste as a wet waste having less calorific, higher moisture and organic matter. The water content of FW generally ranges from 70% to 85% (Tsang et al. 2019). A recent study assessed that an Indian household produces about 1 kg of wet waste per day, and this quantity is expected to increase up to 125 million tons by 2031. About 3/fourth of wet waste ends up in landfills and incineration factories. Because of the scarcity of available technique, a newer technology called biomethanation has been developed to address the problem. In this technology, organic matter is converted into biogas (methane and carbon dioxide) by microbes under anaerobic conditions. Briefly, wet waste is turned into a slurry which is fed into the anaerobic digestion pit. The methane gas produced under pressure is then

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transformed into electricity (AC) which is then stored in high energy batteries. Further, the leftover decomposed matter from the pit could be suitably utilized as biofertilizer in horticultural and agricultural areas (Sahithi Reddy et al. 2021). Integrated biorefinery holds the possibility of valorizing food wastes into numerous valuable bioactive molecules and energy. This has been proven unprecedented as a highly economical, sustainable and eco-friendly alternative (Chowdhary et al. 2018; Chowdhary and Kaushik 2019). Further, this systematic approach generates reasonable employment for rural communities having least environmental impact (Isah and Ozbay 2020). Inedible food components produced at any stage of food manufacturing process in the EU ~ 30 Mt and this number is projected to upsurge up to $4.1 trillion by 2024. Food waste has a high potential to be used as feedstock in biorefineries to produce value-added products and chemicals. However, this process has its own limitations. Some food wastes can deteriorate very quickly if left unused. For instance, fresh seafood can succumb to oxidation and microbial contamination impacting its possible use and transformation (Martínez-Alvarez et al. 2015; Chowdhary et al. 2018). The entire food business presently utilizes ~30% of the world’s total energy. Countries with higher GDP consume more portion in processing and transport operations while in low-GDP countries, cooking consumes the highest share. Refrigerated storage of food products accounts for up to 10% of the total food supply carbon footprint (Cleland 2010). Bulk preservation and the usage of passive evaporative-cooling technologies are few of the possible solutions. Likewise, economically viable stand-alone solar chillers are another option (Day 2011). Food packaging has a vital role to play in containing, protecting, and extending shelf life of food before it reaches the consumer. It is an instrument for conveniently transporting food along the supply chain to the end customer (Fig. 12.2). The highest utility of plastic is in packaging industries. Consumers must learn to redirect materials soiled with food residues to a recycling infrastructure (Kale et al. 2007). Fig. 12.2 Sustainable food packaging

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Co-Product Recovery in Food Processing

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Fig. 12.3 Mechanisms of bio-adsorption by food wastes

Synthesis of bioplastics from FW is a sustainable process and these subsequent bioplastics are biodegradable and compostable in the long run (Caldeira et al. 2020; Singh et al. 2021a). The fish feed has about 50% share in the aquaculture industry. The utilization of food wastes to synthesize fish feed is a realistic solution. A recent study found fish fed with food waste-based diets much safer for human consumption in comparison with those fed the commercial diets (Wong et al. 2016). In addition, food waste comprises components having high biosorption capacity, i.e., cellulose, starch, lipids, lignin, hemicellulose, proteins, and other hydrocarbons. These functional groups accelerate metal complexation leading to removal of heavy metals. Bio-adsorption is a resourceful, ecological and low-priced substitute technology over the conventional methods such as membrane filtration, ion exchange, and chemical precipitation for the removal of toxic metal ions (Fig. 12.3). The presence of multiple metal-binding functional groups has made agro-wastes and food wastes as the prospective biosorbents (Ahmad and Zaidi 2020).

12.2

Potential Strategies for Eliminating or Reducing Food Waste

The industrialization has provided an unprecedented upthrust to the world economy on a large scale. However, the tremendous number of pollutants generated by the industries is an area of paramount concern. Of all industrial sectors, the food industry contributes highly to the generation of pollutants referred to commonly as by-products due to the high water consumption and high release of effluents per production unit. Nevertheless, it is imperative to develop effective routes to valorize these by-products playing a significant role in contribution towards bioeconomy.

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Bioeconomy provides an integrated plan that suitably re-introduces the by-products back into the production process for obtaining newer products having high health benefits through sustainable methods to extract various nutritional components. The unified management of these aggregated food wastes will help in the reduction of the environmental deterioration caused due to traditional methods still being in practice as open burning, dumping, thus also meeting the landfilling directives. With this in picture, the section briefly discusses the food by-products generated in different sectors and its potential utilization.

12.2.1 Waste Generated from Preparation and Processing of Animal-Based Food Product The highest demanding sector in the food industry is dairy. Dairy products such as milk, milk powder, butter, and cheese generate solid and liquid wastes (Jaganmai and Jinka 2017; Ahmad et al. 2019). Nevertheless, speedy industrial growth also leads to toxic effluents detrimental to land, air, and water, thus impacting the environment and human health. A waste generation of 4–11 million is estimated yearly in the dairy sector. This, mainly in the form of dairy wastewater affects the dissolved oxygen content. Additionally, it contains lactose, nutrients, sulfates, fats, chlorides, along with soluble organic components. However, harnessing its potential to effectively utilize this waste as a raw material for producing other industrial products and energy utility holds potential gains as enlisted in Table 12.2.

12.2.2 Waste Generated from Meat and Fish Processing Industries Animal slaughtering leads to the production of animal by-products. They may be defined as entire bodies or animal parts, products of animal origin, or other products obtained from animals, which can be but are not intended for direct human consumption. Approximately 40% of the bovine live product and 30% of the porcine live weight is generated as waste. These wastes, particularly blood, plasma, hydrolyzates, and collagen hold significance as they inherit the integrated inability to serve as a natural preservative. These wastes being an inevitable part of the slaughterhouse, can serve many vital roles if utilized wisely, thus valorizing the waste by-product to serve economically, thus enhancing the food production utility (Przybylski et al. 2020). Over the last 50 years, global fish consumption has almost doubled. Many factors contribute to expanding worldwide fish consumption, including rising population, rising affluence, urbanization, an increasing number of fishing enterprises, and new

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Table 12.2 By-products from dairy wastes and their potential utility Food by-product Whey

Product Whey-derived products Biomass converted to bioethanol and biodiesel

Reference Ahmad et al. (2019) Chokshi et al. (2016)

Dairy sludge

Processing Fermented using lactose fermenting bacteria Acutodesmus dimorphus cultivation Geotrichum candidum cultivated on the combination of oil press water and whey Growth medium for rhizobium

Biofertilizers

Whey

Kluyveromyces fragilis fermentation

Biofuels

Wastewater

Chlorella pyrenoidosa cultivation

Biomass and biofuels

Whey

Propionic acid Citric acid Succinic acid

Wastewater

Cultivation with Propionibacterium shermanii Actinobacillus succinogenes Aspergillus niger Lactobacillus casei Fermentation Cultivation with Aspergillus niger Pseudomonas sp. Streptomyces sp. Lactose fermenting microorganisms cultivation Candida bombicola cultivation

Pandian et al. (2010) Senthilraja et al. (2011) Lu et al. (2015) Ahmad et al. (2019)

Biosurfactants

Whey (permeate, deproteinized) Whey

Cultivation with Xanthomonas campestris Streptomyces thermophilus Latex from Maclura prolifera

Polysaccharides (xanthan gum exopolysaccharides) Bioactive peptides

Wastewater

Microalgae Acutodesmus dimorphus Cultivation Anaerobic digestion and acidogenic fermentation Anaerobic digestion

Biofuel

Wastewater Whey

Yogurt production waste Wastewater

Whey

Dairy waste Fatty waste

Lactic acid Enzyme lipase

Single cell protein

Bioenergy Biomethane

Alonso et al. (2010) Spalvins et al. (2018)

Spalvins et al. (2018) Jaganmai and Jinka (2017) Spalvins et al. (2018) Corrons et al. (2012) Hamawand et al. (2016) Chandra et al. (2018) Chokshi et al. (2016)

and more modern ways of distributing processed frozen fish worldwide. As a result, a significant amount of nutrient-rich fish waste is disposed of on the ground and ocean every year. Against this background, high nutritional content in fish waste as fish skin, bones, fins, and scales holds potential application towards its transformation into value-added products (Table 12.3).

Meat processing waste

Slaughterhouse waste

Animal waste Fish waste

Porcine plasma Fat Mechanically recovered meat Rendering of entire carcasses of animals

Porcine prothrombin

Bovine thrombin Bovine plasma

Blood

Hydroxyapatite

Fish oil

Collagen

By-products Protein hydrolyzates

Water bonding Cross-linking Flavor enhancement Gelation Foaming Emulsifier

Lard, tallow

Protease inhibitors Triglycerides Protein hydrolyzates

Properties Anti-oxidative functions by scavenging free radicals, pro-inflammatory cytokines, and anti-microbial Biodegradable, emulsifier, stabilizer, foaming agent, biodegradable Health benefits due to presence of omega-3-fatty acids Structural similarity to mammal bones Protein source Hemoglobin Thrombin Fibrinogen, thrombin, globulin, porcine transglutaminase Prothrombin

Table 12.3 Waste generated from meat and fish processing industries

Promotes coagulation of blood, treatment of wounds Microbiology as a media to grow probiotic bacteria Restructure meat products Used as a precursor in thrombin purification and production Surimi (A form of a fish gel) Biodiesel Pharmaceuticals, diagnostic media, cosmetics, recombinant proteins, nutrition, food industries, biopolymers Cosmetics Chemicals

Bone replacement and prosthetic implants in maxillofacial, orthopedic, and dental applications Research, pet food, aquatic food

Mora et al. (2019), Bah et al. (2013)

Akram et al. (2014)

Applications Reference Pharmaceuticals, diagnostic media, cosmetics, recom- Araujo et al. (2021), binant proteins, nutrition, food industries, biopolymers Bhuimbar et al. (2019), Nam et al. (2020)

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12.2.3 Waste Generated from the Processing of Vegetable and Fruits Vegetables and fruits are the most utilized commodities, covering approximately 65% and 38% of the horticulture sector. This enormous production generates around 60% of waste by-products, according to the Food and Agriculture Organization of the United Nations (Sharma et al. 2016; Osorio et al. 2021). The by-products of the agri-food industry comprise seeds, pomace, shell, leaves, and peels; which are generally the food waste/loss originating in the food production chain; are a rich source of antioxidants, phenols, pigments, antiviral, antibacterial effects, etc., thus increasing its potential utility to be utilized in different sectors as discussed briefly in Table 12.4.

12.2.4 Waste Generated from the Processing of the Spent Mushroom Substrate Spent mushroom substrates (SMS) are generated as waste products post cultivation of mushrooms. It is being anticipated that harvesting of approx. 1 kg of mushrooms generated a lump sum of 5 kg of spent substrate. With ever-increasing demand to meet the requirement of optimal food production, the production of mushroom is gaining exponential growth worldwide (Singh et al. 2021b). This huge quantity of SMS produced is disposed of casually in open air begetting several environmental concerns. Piling-up of SMS leads to loss of basic plant nutrients, groundwater contamination along with production of greenhouse gases such as nitrous oxide and carbon dioxide, triggering global warming along with loss of basic plant nutrients (Barh et al. 2018; Lopes et al. 2015). On the contrary, SMS serves as a rich source of nutrients, enzymes, and minerals, which holds potential application in many sectors as enlisted in Table 12.5, thereby enhancing its potential utility.

12.2.5 Wastes from Bakery Industry and Sugarcane Industry Bakery wastes are one of the other categories of food waste generated. This generally comprises cookies, stale bread, and cereals. This waste possesses optimal characteristics to undergo solid and submerged state products, leading to the utilization of these wastes to develop biodegradable polymers, chemicals, and biofuels. Wang et al. (2009) also reported successful utilization of the bakery wastes to produce amylolytic enzymes using A. awamori. Haque et al. (2016) proposed the utilization of bakery waste to produce enzymes and biocolorant using Monascus purpureus. The waste bread has also been successfully converted to nutrient-rich hydrolyzates by Kwan et al. (2018) and as a growth medium to baker’s yeast by Benabda et al.

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Table 12.4 Waste generated from vegetable and fruits industry By-product Vegetables Onion peels, potato peels, cauliflowers (stems and florets), carrot pomace, tomato pomace Fruits Apple peel, grape pomace, mango by-product, orange peel Seeds Tomato, peach, apple, grapes, mango By-products of root and tuber industry of potato, yam, cassava Shells, cakes, pellets

Coffee husks Malt bagasse

De-oiled cakes generated on pre-processing of cereals and lentils

Pigments Tomato peel

Onion leaves

Coffee exocarp The by-product of mulberry industry Peel and waste pulp of red prickly pear

Properties Rich source of Cellulose Hemicellulose Pectins Galactose Glucose Arabinose

Applications Dietary fibers Wine industries

Reference Sagar et al. (2018)

Unsaturated fatty acids, carotenoids, phenolics, phospholipids Starch

Oil extraction, pasta and sauce preparation

da Silva and Jorge (2017)

Bread-making industry, fermented beverages (beer), processed foods Animal feed, flour for bread, cakes, and soups

Osorio et al. (2021)

Lignocellulose, protein values Rich organic content Fiber, carbohydrates, proteins, lignin Proteins, carbon

Compost, biofertilizer, biofuel Confectioneries, pastry, biorefinery, animal feed, alcohol fermentation Animal feed, enzyme production, bioethanol, mushroom cultivation, production of flours, tofu, sausages, and cereals

Lycopene, phytoene, phytofluene, and β-carotene Quercetin, cyanidin 3-Oglucoside Cyanidin 3-glucoside Cyanidin 3-glucoside Bethany and iso-betanin

Natural coloring, antioxidant

Natural coloring, additives Natural dyes. Provides protection against low-density lipoprotein oxidative modifications

Akanbi et al. (2019), Osorio et al. (2021) Sagar et al. (2018) Mello and Mali (2014) Ramachandran et al. (2007), Ancuța and Sonia (2020)

Rodman and Gerogiorgis (2016), Kantifedaki et al. (2018), Osorio et al. (2021)

Koubaa et al. (2016)

(continued)

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Table 12.4 (continued) By-product Enzymes Pineapple peel, core, stems, pulp residue

Properties

Applications

Reference

Bromelain

Improves food digestion, softens beef transformation Wastewater treatment Antioxidant Ester hydrolysis to produce detergents Fruit juice, starch syrup, chocolate cake, brewing industries

Sagar et al. (2018)

Industrial waste from palm oil, olive oil cake, mango seeds

Lipase

Banana waste, cabbage waste, coconut oil cake, cassava waste, date waste, potato peel, orange waste Banana waste Cabbage waste Mango peel Kinnow waste Banana peel, sapota peel, orange, pomegranate Strawberry peel, apple pomace, banana peel, orange peel, lemon peel Guava waste, grape seeds, rambutan skin, oat shell, grapeseed Orange peel, pineapple peel, mango seed, grape pomace, kaffir lime leaves

Amylase

Citrus fruit peels

Silveira et al. (2016)

Sagar et al. (2018)

Cellulase

Liberation of aroma rich and extraction of phenolics

Sharma et al. (2016), Sagar et al. (2018)

Invertase

Pectinase

Candies, jam, confectionery, and pharmaceutical products Fruit juices, wine

Antioxidant

Cosmetics

Sharma et al. (2016), Sagar et al. (2018) Sharma et al. (2016), Sagar et al. (2018) Yarovaya et al. (2021)

Antifungal, antibacterial, immunomodulatory

Utility in pharmaceutical industries as effective against various cell lines (colon cancer, prostate cancer, hepatocarcinoma), respiratory pathogens Inhibition of hepatitis B virus, chikungunya virus, human respiratory syncytial virus Feasibility against the treatment of Covid-19

Antiviral, reduce infected cells with the potential to produce compounds as Tangeretin Hesperidin Nobiletin

Peanparkdee and Iwamoto (2019), Meneguzzo et al. (2020)

Haque and Pant (2020), Hu et al. (2020), Lin et al. (2017), Osorio et al. (2021)

(2018). Gadkari et al. (2021) demonstrated the feasibility of waste bread to produce succinic acid. Govindaraju et al. (2021) recently reported the utilization of bakery waste for the development of compost. These approaches provide a sustainable solution to waste management, more readily than being dumped openly, leading to serious environmental concerns.

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Table 12.5 Utilization of spent mushroom substrates (SMS) Spent mushroom substrate Mushrooms Pleurotus ostreatus Calocybe indica Lentinula edodes Flammulina velutipes Hericium erinaceum Pleurotus sajor-caju Agaricus bisporus Components Saw dust Paddy straw Wheat straw Leafy wastes

By-products Enzymes • Cellulase • Laccase • Xylanase • Lignin peroxidase • α amylase • β glucosidase

Properties Redox substrate molecules with broad substrate specificity

Polysaccharides Rich source of Vitamins nutrients Trace elements Calcium Nitrogen Ash Phosphorous Rich source of carbon, enzymes, and nutrients

Rich source of nutrients

Adsorption Biosorption

Silica

Rich source of nutrients, silica, and minerals

Lime

Bioaugmentation with sulfatereducing bacteria

Applications Decolorization of dyes Degradation of phenols and polyphenolic components Biostimulation agent Biofuel production

Reference Phan and Sabaratnam (2012), Lopes et al. (2015), Hanafi et al. (2018), Singh et al. (2021b)

Animal feed due to in vivo dry matter digestibility Compost Biofertilizer

Wastewater Treatment Heavy metal uptake bioremediation Plant nutrient and disease management in tomato plants Acid mine drainage

Of the other industry by-products, sugarcane is also one of the other essential cash crops where a massive quantity of by-products is generated. Sugar mills consume excessive amounts of water in their operations. Approximately 20–30 tons of water is required to process a ton of sugar on an average and produces two categories of polluted water: (a) effluent from cane molasses distilleries and (b) processed water are the two main sources of pollutants. The various wastes generated by sugarcane production can be sustainably utilized to convert them into usable products by providing an efficient recycling route (Bhatnagar et al. 2016).

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Sugar leaves which comprise a major chunk of sugarcane by-product are mostly burnt as their rough texture limits its utility as an animal feed. This leads to adverse effects on humans and the environment. Sustainable utilization of this waste lies in converting the product to charcoal which is a promising adsorbent. This economical solution towards renewable energy development can serve as a better contribution to the field of fossil fuels (Porol et al. 2021). Bagasse is a highly abundant and cheaper by-product of sugar industry. This residual biomass is fibrous that remains after cane stalks are crushed for sugar extraction. On the other hand, the precipitate in the form of sludge slurry after filtration is termed as filter cake or press mud cake (Bhatnagar et al. 2016). Bagasse is a rich by-product with ideal cellulose, hemicellulose, lignin, fat, wax, and other essential minerals. This makes it a suitable substrate for the production of bioethanol, food additives as vanillin and xylitol, and single cell protein. Bagasse fly ash could be used as filler material in the development of paper and in landfilling. It is also applied for removing pollutants from water and concrete materials (Bhatnagar et al. 2016; Zareei et al. 2018; Martinez-Hernandez et al. 2018). Press mud, another by-product generated, is used in foaming agents, cement aid, and compost development. The rich organic and nutrient content of press mud and its inherited ability to serve as an inert material ingredient provide a suitable replacement to use of other costly reported materials such as coco peat, talcum powder, etc. (Bhatnagar et al. 2016; Kumar et al. 2017). Furthermore, recent articles have found it as suitable substrate for biocontrol agents serving as carrier in biopesticide and biofertilizer development (Singh et al. 2021b). Additionally, the rich sugar content (5–15%) makes it a suitable substrate for biogas production (Bhatnagar et al. 2016). Amongst the liquid waste generated is the molasses, left over in the crystallization process of sugar from sugarcane. This by-product contains high amounts of fermentative sugars and hence is used in bioalcohol production (Valderrama et al. 2020). It is plausible to conclude that sugar industry wastes should be viewed as economic resources that can be converted into valuable products to proceed towards a long-term waste disposal solution.

12.3

Different Technologies for Co-product Recovery and Valorization of Food Waste

Food wastes are a potential source of various industrial and health imparting bioactive metabolites such as phytochemicals, antioxidants, coloring pigments, and nutrients. Roots, barks, seeds, midribs, peels, bracts, and leaves are some of the most common organic by-products of food production. The recovery of by-products from food wastes always remained underutilized due to the lack of sensitive extraction techniques. On the basis of nature of both the wastes and the bioactive metabolites to be extracted, different extraction techniques are employed. There is great probability of degradation of extracted bioactive compounds due to

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harsh environment and processing conditions (Drosou et al. 2017; Rehman et al. 2019; Shishir et al. 2018). Bioactive metabolites can be extracted using a number of different techniques considering the type and properties of food, fruit and vegetable wastes, chemical nature, functional properties, and use of end product. Extraction conditions play a significant role as these are responsible for releasing bioactive compounds from the plant matrix to the medium. An overview of extraction techniques is presented in this section.

12.3.1 Solid-Liquid Extraction Solid-liquid extraction involves solubilization of bioactive compounds of a solid matrices into the liquid aquatic organic solvent. The solvents are selected so that lesser interference is caused by the matrix (Luthria 2008). The quantity and quality of extracted bioactive compounds depends on the optimization of experimental parameters. The significant parameters to be considered include pH, temperature, time, particle size, solid-to-liquid ratio, solvent polarity amongst others. Herbal and other food processing units apply this method when the vegetable matrix needed extraction before further processing. Nevertheless, this method has a massive drawback in terms of using expensive, partly toxic, inflammable, explosive, and hazardous organic solvents and the long times needed (Proestos and Komaitis 2008). Futuristic research might lead to the use of cheap solvent water with some other mild extraction methods.

12.3.2 Soxhlet Extraction This method involves repeated washing of powdered plant matrices with hot solvent that facilitate greater solubilization of bioactive compounds and have used in processing of food matrices. Soxhlet extraction is a comparatively low-cost extraction technique. It saves time, energy and affects the financial input to the extraction of bioactive compounds of interest. On small-scale extraction it is used as a batch process but in medium- to large-scale extraction, it can be employed as a continuous method. There is limited use of this technique in food and food waste processing. Soxhlet extraction proved better than other traditional methods of extraction except the extraction of temperature sensitive compounds (De Castro and Priego-Capote 2010). It is advantageous to many other advanced techniques such as automated, high-pressure, ultrasound-assisted and microwave-assisted Soxhlet extraction. Soxhlet extraction shortens the time of extraction when using auxiliary forms of energy and automation of energy.

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12.3.3 Enzyme-Assisted Extraction An enzyme alone, or in combination of other enzymes optimize extraction of bioactive ingredients from disrupted cells. It is an auspicious alternative strategy for solvent-based traditional extraction methods. The efficiency of the method depends upon the selectivity and specificity of enzymes under prevailing normal atmospheric conditions in aqueous medium (Gardossi et al. 2010). Hydrolytic enzymes such as pectinases, cellulases, hemicellulases, etc. hydrolyze cell wall constituents lead to increase in its permeability to bioactive compounds including polysaccharides, oils, natural pigments, flavors, antioxidant, and medicinal active compounds (Puri et al. 2012). The enzymes used can be obtained from bacteria, fungi, animals and plants tissues. Optimization of conditions like temperature, pH, pressure, time, and concentrations of enzyme and substrates should be appropriately established to increase the yield. To reduce the consumption of solvents, time of extraction along with high yield and quality of bioactive compounds specific enzymes can be used for pretreatment of waste food or whatever the substrate is utilized. The main limitation to enzyme-assisted extraction is its high cost of processing raw materials at industrial scale (Baiano 2014). Conventional methods entail pessimistic thermal effects on yield and quality of extraction. This approach implies large expenditure of solvent and energy. A few among them are supercritical fluid, subcritical water, ultrasound-assisted, microwave-assisted and pulsed electric field for the extracting phenolics, anthocyanins, flavonoids, tannins, carotenoids, and vanillic acid (Pattnaik et al. 2021).

12.3.4 Ultrasound-Assisted Extraction (UAE) Ultrasonic waves are sound waves with 20 kHz. In liquid media UAE is a nice choice for extraction of bioactive metabolites from food, agro-wastes, fruit and vegetable wastes via acoustic cavitations, vibration and their mix effect. For quality extraction of bioactive metabolites, frequency range 20 to 100 kHz is generally employed (Cravotto et al. 2008). The efficiency of UAE depends on the physical forces generated due to acoustic cavitation, which leads to the destruction of cell walls and facilitates extraction (Vardanega et al. 2014). In addition, acoustic pressures generate zones of low and high pressure in the liquid medium. When exposed to acoustic fields, cavities are formed by microbubbles. Acoustic field depends on the frequency of acoustic cycles. The microbubbles expand and contract due to negative and positive pressures, respectively. The expansion and contraction leads to the exchange of gases. As a result of exchange of gases, the size of bubbles increase considerably due to accumulation of mass or through fusion of microbubbles. After achieving a critical size the bubbles collapse (Alzorqi and Manickam 2015). Due to cavitation a large quantity of solvent enters into the cell matrix and releases the phenolic compounds into the solvent by cell wall dislocation. Cavitation gets

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affected by temperature and occasionally an increase in temperature increases the rate of solvent diffusion by depleting the interactions between solvent and matrix (Kaderides et al. 2015). When a range of temperature 30 to 70  C employed maximum phenol extraction was observed at 70  C indicating optimum extraction at higher temperatures (Ahmed et al. 2020). Comparative studies indicated that UAE is more efficient as compared to the conventional extraction methods like soxhlet extraction (Drosou et al. 2015; Safdar et al. 2017).

12.3.5 Microwave-Assisted Extraction (MAE) MAE is another extraction technique that can be used in combination with conventional one but is superior to them because it utilizes less solvent, high extraction efficiency and needs shorter duration (Delazar et al. 2012). The electromagnetic field generated by microwaves varies in the range between 300 MHz and 300 GHz. Polar molecules absorb this energy and then transform into heat due to dielectric heating. In MAE the solvents with greater dielectric constants are commonly used for extracting bioactive compounds from plant matrices. Such solvents absorb microwave waves maximally and convert into kinetic energy. Highly energetic molecules enter into the plant material by diffusion and solute molecules carried into the solvent (Jaitak et al. 2009). The mechanism of MAE involves three steps. To begin with, localized heating near the boiling point due to absorption of microwaves by water glands inside the plant materials expands water and disrupts cell walls. Heating leads to breakdown of hydrogen bonds and associated interaction between the solute and active site of plant matrices which finally causes the cell wall to disrupt. Second, ruptured cells encourage the mass inflow of solvent in the plant matrix and solute into the solvent. In the third and last step extracted solutes spread in the nearby solvent (Alupului et al. 2012). Microwave-assisted extraction can be performed in closed and open apparatuses. Closed MAE apparatus includes sealed vessels with invariable microwave heating. Closed systems are faster and more efficient in extraction as there prevailed conditions of high temperature and pressure but need extra safety measures. On the other hand, open systems need less safety concerns comparatively and a beautiful option for extraction of thermolabile bioactive metabolites (Chan et al. 2011). Sometimes the plant materials or the food waste are directly heated by microwaves which release the bioactives into cold solvent (Liu et al. 2018). In comparison, traditional Soxhlet extraction methods need a large amount of solvent and time. Zhang et al. (2005) carried out comparative study on different extraction methods like percolation, UAE, MAE, and maceration to extract alkaloids from Macleaya cordata. MAE was observed to yield the highest amount of alkaloids within the shortest extraction time. MAE has another advantage to use the fivefold to tenfold reduced amount of solvent if compared to the classical methods of extraction. Using MAE needs special attention in designing closed reaction vessels as there are chances of solute degradation and explosion in the closed vessel MAE equipment.

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12.3.6 Pulsed Electric Field Extraction (PEF-E) This is an evolving extraction technique for extraction of bioactive metabolites from plant samples. This method does not involve heating that destroys cell structure. In this method electric pulses of moderate electric field strength are used for very short duration (Azmir et al. 2013). Application of these electric fields generate transmembrane potential on the cell surface. When the trans-membrane potential crosses, a critical limit electroporation occurs. As a result, membrane permeability increases and there is an efflux of compounds from the cell interior. PEF-E is beneficial to increase the yield of bioactive at lower energy costs and threats to the environment (Siddeeg et al. 2019). PEF-E is useful to extract thermolabile bioactive compounds from the sample matrix.

12.3.7 Supercritical Fluid Extraction (SFE) Supercritical fluids (SCF) are maintained above their critical temperature and pressure. Under such conditions they show properties between pure liquid and gas and are known as compressible liquid or dense gas. SCF show liquid like densities, diffusivity greater than liquids, good solvating power, reduction in surface tension, low viscosity, and gas like properties, hence exhibiting high penetration to the solid matrices (Pitchaiah et al. 2019). Water at 374  C and 22.1 MPa and CO2 at 31.3  C and 7.38 MPa exist as supercritical fluid. SCFs can easily diffuse into the solid matrix like a gas and dissolve solute efficiently like a liquid. These properties are responsible for higher yield in shorter extraction duration (Soquetta et al. 2018). SFE is a method of extraction of bioactives from the sample matrix using supercritical fluid as extraction solvent. Carbon dioxide is the most useful supercritical fluid, sometimes modified using non-toxic, non-explosive and non-polar co-solvents such as ethanol and methanol, which can easily extract slightly polar compounds. Its easy removal from the final product made it a preferred solvent for extraction of bioactive compounds from plants and food by-products (Wang and Weller 2006). SFE stepwise procedure for extraction involves placing raw material in the extraction chamber provided regulated temperature and pressure conditions. Then it is pressurized with the fluid by a pump regulating the temperature conditions. The bioactive compounds dissolved in fluid are carried to the separation units and collected at the lower part of the structure. The fluid is then recycled or released (Da Silva et al. 2016). Due to slight polarity shown by supercritical CO2, the bioactive metabolites from the solid matrix exhibits reduced solubility. To overcome this barrier, co-solvents or modifier like water and ethanol are used in addition to supercritical CO2 (Da Porto et al. 2014). Supercritical anti-solvent (SAS) process has been utilized for precipitation of bioactive compounds. In this process the sample containing bioactive compounds is first dissolved in an organic solvent. Then the continuous flow of CO2 in the extraction system is maintained under regulated temperature and pressure

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conditions. The solute-solvent mixture is then sprayed into supercritical CO2; here organic solvent is separated from the mixture. Under supercritical conditions there is high solubility of organic solvent in the supercritical CO2; an instant mutual diffusion occurs at the interface of solute and supercritical CO2; this leads to the saturation and phase separation of solute in supercritical CO2, which results in nucleation and precipitation of the desired compound (Zhong et al. 2008). A number of bioactive metabolites have been extracted using SCFs such as flavonoids from onion peels (Munir et al. 2018), pectin from Jackfruit wastes (Li et al. 2019), and phenolics from grape waste (Elmi Kashtiban and Esmaiili 2019). Baysal et al. (2000) extracted lycopene and beta-carotene from tomato pomace, crushed skins of fruits and seeds using supercritical CO2 and ethanol. SFE is an appropriate method to extract caffeine up to 97% from green tea leaves with no effect on useful catechins and flavonols (Perva-Uzunalić et al. 2004). Oil from rice bran successfully extracted using 100 g supercritical CO2 at 10,000 psi pressure and 80  C temperature resulted in highest yield (Perretti et al. 2003). At industrial scale if this method is used in isolation, it yields suboptimal results while on using in combination with certain pretreatments and scale-up methods optimum extraction can be achieved. The integration of SFE with prior cleavage and separation, microorganisms-mediated partial breakdown of feedstock, pretreatment of plant matrices with certain chemical and enzymes to release the bioactive compounds, etc. can uplift the efficiency.

12.3.7.1

Subcritical Water Extraction (SCWE)

SCWE is an alternative extraction technique to the conventional ones, this promising technique is environment friendly as well as less toxic. The process involves heating of water at 100–320  C at a pressure of about 20 to 150 bar. Under such conditions water remains in liquid phase but its dielectric constant changes from 80 to 27 which is at the level of ethanol and methanol under normal conditions. This decrease in dielectric constant of water increases the solubility of nonpolar solutes in water (Gbashi et al. 2017). This unique property of water is utilized for extraction of a variety of bioactive compounds. Munir et al. (2018) compared the extraction of phenolic compounds from onion peels using SCWE for half an hour and ethanol for 3 h. They found higher amounts of phenolics and flavonoids in SCWE as compared to ethanol. This was due to breaking of no-covalent interactions like hydrogen bonds, van der Waals forces, and low viscosity of water between solute and matrix. A pretreatment is given to improve the rate of extraction, minimize long exposure of heat-sensitive bioactive compounds. Commonly the plant samples are pretreated with ultrasonication, microwaves and gas hydrolysis with N2 or CO2. Microwaves and ultrasonication diffuse the bioactive compounds into solvent while N2 replaces oxygen in water, this forms a shielding effect on the reaction milieu that enhances the extraction of bioactives (Zhu et al. 2008). Among all the pretreatments, microwaves proved to be the best for extraction of bioactive compounds from the spent ground coffee. Some limitations for this method are its high cost of processing per unit sample and high reactivity of water under specified conditions.

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12.3.8 Cold Plasma Assisted Extraction Two major constraints in solvent extraction techniques are that a large quantity of solvent is needed with low yield. The use of solvents like methanol affects the quality of processed products and is also harmful to the environment. Conventional solvent extraction generally carried out at high temperature for longer durations that consume more energy (Brglez Mojzer et al. 2016; Mokhtarpour et al. 2014). Plasma is a partially ionized gas containing activated particles, i.e., ions, free electrons, radicals, and photons, and it is often referred to as the fourth state of matter. Plasmas are classified into two types: high-temperature plasmas and low-temperature plasmas. High-temperature plasmas also called fusion plasma contain equilibrium at temperatures higher than 107 K (Rutscher 2008). Low-temperature plasma may be of thermal and non-thermal plasma. Thermal plasma components remain at equilibrium at higher temperatures than non-thermal plasma. Non-thermal plasma or cold plasma is operated at temperatures lower than 400  C. It produces reactive gas species, UV radiation, energetic ions, and charged particles, all of these can cause significant physicochemical reactions in treated samples (Hoffmann et al. 2013). Dielectric barrier discharge (DBD) and plasma jet generated cold plasma is commonly used in food processing (Misra et al. 2016). The influencing factors in this technology are treatment period, applied voltage, working gas, and relative humidity (Lotfy et al. 2020). The cold plasma properties like cell wall rupture and modification of surface ease the diffusion of internal molecules and enhance the extractability of secondary metabolites especially the phenolic compounds and essential oils from the waste food debris. Kodama et al. (2014) extracted comparatively higher amounts of essential oils from orange peels using cold plasma. It is observed that cold plasma treatment affects the food products’ total phenolic contents inconsistently. In orange and white grape juice, low total phenolic contents are reported when treated with cold plasma (Almeida et al. 2015; Pankaj et al. 2017). The same treatment enhances total phenolic content in cashew apple juice (Rodriguez et al. 2017). This indicates that the mechanism of interaction at molecular level between cold plasma reactive species and phenolic content is still unknown.

12.4

Conclusion: Current Challenges and Future Opportunities

In today’s world, “waste” is the biggest environmental problem and the dearth of its management has serious consequences on animal life and health. As society has moved from paucity to wealth, food waste has become more of an ethical and social issue. The magnitude of this nuisance is so immense ecologically, that it is pivotal to pay consideration towards optimal food waste management recycling procedures. It is not only unwise but also cruel to dispose of food (either raw or prepared). For a

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cleaner environment, sustainable methods should be applied for food waste management. The highlighted key points which can be concluded from this chapter are as follows: (a) Water usage has to be minimized and use of recycled water should be adopted; (b) residential kitchen waste could be channelized to generate electricity via biomethanation; (c) Fish feed is the best substitute for food waste. Fish processing industries give several peptides (thrombin, globulin, and prothrombin) for biomedical applications; (d) Dairy by-products have been appropriately used in biorefineries to produce bioethanol and biodiesel and value-added metabolites (i.e., citric acid, succinic acid, lactic acid, etc.); (e) Food waste is also a powerhouse for producing industrially relevant enzymes such as lipase, cellulose, pectinase amongst others. Natural dyes and pigments (lycopenes) can be derived from food waste.

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Chapter 13

Upcycling Technologies in the Food Industry Rubeka Idrishi, Divya Aggarwal, and Vasudha Sharma

Abstract Food waste is a global issue upon which many countries are concerned. Moreover, among the consumers of the upper strata of the pyramid, the dietary habits and lifestyle changes have undoubtedly imparted the increasing concern of food waste, and to the lower part people who cannot afford nutritious food the prolonged starvation causes deaths worldwide. Waste valorization is a key to endow them with essential food commodities. It is an ethical issue that plays a key role in developing a sustainable economy. Focusing on the sustainability aspect of the food cycle will thus contribute to SDG2030 which aims to “end hunger, achieve food security and improved nutrition and promote sustainable agriculture”, responsible consumption and production, climate action, life below water and life on land and sustainable food production, consumption patterns, and efficient agricultural practices by ensuring the accessibility of safe food to the people are directly related to them. In this chapter, various upcycling technologies have been discussed concerning its present status, technologies used, challenges, and future prospects because many studies reveal that there is a huge gap in understanding the vicious cycle in upcycling food commodities. Keywords Upcycling · Food industry · Hunger · Recycle · SDG · Food waste

R. Idrishi (*) Indian Institute of Technology Guwahati, Guwahati, Assam, India e-mail: [email protected] D. Aggarwal CSIR-Central Food Technological Research Institute, Mysuru, Karnataka, India V. Sharma Department of Food Technology, Jamia Hamdard (Deemed to be University), New Delhi, India © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_13

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Introduction

Food wastage and food loss hamper the sustainability of the food systems globally and to encounter the same, various multifaceted technical and innovative solutions are being proposed, researched and policies are being laid by all the stakeholders in and outside the food chain (Ojha et al. 2020). Throughout the food value chain from farm to fork almost all the stages of production, postharvest, processing, distribution, and consumer purchases include a certain degree of food loss and/or food waste. This waste can be categorized industry-wise into agriculture, horticulture, brewery, dairy, feed-fodder, and miscellaneous (Ravi et al. 2020). Henceforth, upcycling is a great way to tackle food shortage and food waste issues at all stages of the supply chain. It is an exciting sustainable trend in the food sector that involves upgrading and recycling the substantial amounts of underutilized fractions into innovative and essential ones. In food industries, the utilization of by-products has been considered an object of interest to minimize unexpected wastage and adverse environmental impacts. Closing the food loop by recycling nutrients in food waste is an important way of limiting the use of mineral nutrients, as well as improving national and global food security. To close this loop of food waste all the stakeholders such as researchers, authorities and government should look into the hierarchy of the food waste to minimize the generated waste within their sector itself, and to assure that the waste which is flowing into other sectors is being sent into optimal conditions to be treated, processed, or reused (McConville et al. 2015). Therefore, owing to the rising food and nutrition security issue the need for upcycling technologies in the food industry is escalating which are now backed up by policies like the United States Environmental Protection Agency (USEPA) by the United States Department of Agriculture (USDA), and Environmental Pollution Act (EPA) India. These policies are collaborating to reduce and recover food waste (McConville et al. 2015). The life cycle assessment (LCA) studies w.r.t different streams of waste from food industry at different stages is completely lacking as only household waste LCA has been studied to some extent (Gao et al. 2018). Provided all these research backgrounds, a global cohesive approach is needed to bring all the stakeholders of the food chain and the allied sectors to a single platform. This chapter aims at drawing attention towards various upcycling approaches and the research done for the same, and hence adds its bit towards maintaining a closed loop in terms of the life cycle of food.

13.2

Various Waste Streams in Food Industries

Recycling food waste streams is one of the major concerns of the food industry and globally Japan and South Korea have been the major players in upcycling the same. There are various concerns with regard to regulations and legislations for different

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Table 13.1 Various food industries and their waste streams S. No. 1.

Industry Meat industry

2.

Bakery industry Fish and marine industry Fruits, vegetables, and tubers Milk industry Cereal industry Nuts and oilseed industry Coffee industry Spirit producing industry

3.

4.

5. 6. 7.

8. 9.

Major waste streams Meat and bone meal, feather meal, and blood meal, skin, nails, feathers, bones, hides, horns, visceral mass Ground biscuits, bakery meal, and potato crisps Fish meal, shells of molluscs, and viscera Peel, seeds, pomace, bagasse

Whey Husk, spent grains, bran, germ, and milling meal Pressed cake, meal, and residues

Coffee mucilage Spent grains, waste distilled fractions

Reference Henchion et al. (2017), Rao et al. (2021)

Shurson (2020) Araujo et al. (2020), GarciaSifuentes et al. (2009), Kangas et al. (2013), Ucak et al. (2021) Dhumal and Sarkar (2018), Fierascu et al. (2019) Pame et al. (2020), Rao et al. (2021), Skryplonek et al. (2019) Alexandri et al. (2020) Kangas et al. (2013), Rao et al. (2021) Alexandri et al. (2019), Heeger et al. (2017) Rao et al. (2021), Roth et al. (2019)

waste streams and the risk of transmission of pathogens, bacteria, viruses, parasites, and prions (Shurson 2020). Therefore, it is necessary to know about various waste streams of the food industry in order to understand all the necessary attributes to model food waste management systems and their assessment (Garcia-Garcia et al. 2019). Following are the major waste streams of food industries (Table 13.1).

13.3

Upcycling Technologies of Food Industry Waste Streams

13.3.1 Biotechnology and Fermentation It is one of the promising upcycling technologies intended for the recovery of valuable food products. As food is a matrix for several biomolecules including carbohydrates, proteins, fats, and oils, it is hydrolysed to simpler molecules that is, sugars, amino acids, glycerol, and fatty acids, respectively. The molecules in food waste are also cleaved similarly via the route of either acids or biological/enzymatic routes. Nevertheless, enzymatic processes are preferred as they specifically target the

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biomolecules to desired moieties, otherwise using chemical (acid) hydrolysis, may lead to the production of inhibitors or unpleasant by-products. The technology involves biocatalyst-based processes which are considered to be highly selective and environmentally safe in food processing. Enzymes, classified as microbial or non-microbial, are used as catalysts to enhance the activity of chemical reactions. The development of rational biocatalysts using enzymes for chemical modification of food processing waste is also an effective means which overcome the limitations of the traditional catalyst systems (Andler and Goddard 2018). These catalysts aid in the cleaning of food waste streams and developing novel products out of them. Some of the potential enzymes are discussed here: Proteases belonging to the group of hydrolases are generally employed to solubilize proteins in food waste streams, thereby resulting in the recovery of nutritious solid and liquid concentrates (Karam and Nicell 1997). A study reported the action of alkaline proteinase from Bacillus subtilis on waste chicken feathers from poultry slaughterhouses. Upon enzyme hydrolysis, it was revealed that the end product contains a very high protein content that can be used as a feed constituent (El-Nagar et al. 2006). Amylases are one of the crucial industrial enzymes that catalyze the hydrolysis of starch into smaller fragments, glucose, and maltose. For example, potato is a high carbohydrate food and is considered a staple vegetable to be consumed around the world. Using potato peels as a carbon substrate, it was identified that amylase could be isolated from the Bacillus subtilis K-18, one of the bacterium species that could be a potent strain for biofuel production (Mushtaq et al. 2017). Lipases are immense biocatalysts that can catalyze certain reactions including hydrolysis, esterification, and transesterification under mild conditions. In a study conducted, lipases from Thermomyces lanuginosus and Candida antarctica B were used for hydrolysis and esterification, respectively, to obtain biodiesel from waste cooking oil. It could alleviate the energy demand of plants as well as the costs of conventional downstream processes (Vescovi et al. 2016). Pectinases are a group of enzymes that act on the degradation of pectin, a polysaccharide commonly found in the cell wall of plants. The sources of pectin include apples, bananas, peaches, apricots, etc. The pectin degrading enzyme, pectin esterase is produced from Clostridium thermosulfurogenes. A food processing waste, apple pomace is used as a substrate for producing butanol and it has been assessed that up to 80% of the sugars were consumed, and the residue remained after the separation of butanol possess the potential to be an excellent animal feed (Blaschek 1992).

13.3.2 Supercritical Fluid Extraction For many years, it has been believed that food wastes are considerable sources of potential bioactive compounds. However, a vast majority is yet to be exploited.

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Table 13.2 Sources, their bioactive compounds and the operational parameters for CO2 supercritical fluid extraction Bioactive compounds Lycopene, β-carotene Phytosterols

Parameters (Temperature and pressure) 80
C and 30 Mpa

2.

Source Tomato peels and seeds Peach seeds

3.

Citrus peel

Volatile oils

35
C and 10 Mpa

4.

Fatty acids

56
C & 26 Mpa

5.

Passion fruit seeds Banana peels

Essential oil

40
C and 30 Mpa

6.

Grape pomace

Phenolic compounds

50–60
C and 30 Mpa

S. No. 1.

40
C and 20 Mpa

Reference Sabio et al. (2003) Ekinci and Gürü (2014) Omar et al. (2013) Liu et al. (2009) Comim et al. (2010) Oliveira et al. (2013)

Compared to the conventional solvent extraction technique for isolating these compounds, supercritical fluid extraction proves to be an environmentally effective alternative that yields products free from toxic residues (Hauthal 2001). The application of this technique lies in the efficient utilization of industrial wastes and transforming them into valuable products having no or little economic value (Viganó et al. 2015). Carbon dioxide is one of the most frequently used supercritical fluids that contribute to the greener extraction processes for phenolic compounds. It is a low-cost, non-toxic, non-mutagenic, non-flammable, thermodynamically stable, and high purity solvent. Additionally, because of its moderate critical temperature (31.3
C) and pressure (7.38 MPa), it can also be used in the extraction of thermally labile compounds (Sabio et al. 2003), (Torres-Valenzuela et al. 2020). Some of the examples of food processing wastes that utilize CO2 as a solvent in supercritical fluid extraction and produce significant bioactive compounds are highlighted in Table 13.2.

13.3.3 Separation Techniques for Upcycling The global demand urges for the treatment of food industrial wastes. The sustainable approach to this is to effectively recycle the waste streams and recover valuable products from them. This section emphasizes the practical applications of different separation methods, whether it be physical or chemical. The separation technologies are based on the principle of changing the phase of the matter in a single or multiple-step operation. Adding steps to the process may enhance the quality of the end product while also increasing the capital and operation costs. Suppose if the technique involves m steps, each having similar recovery

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efficiency of ɛ, then overall recovery efficiency may be calculated as follows (El-Mashad and Zhang 2007): Overall recovery ¼ ɛm When the m steps possess distinct recovery efficiencies, the overall recovery efficiency can be calculated as follows: Overall recovery ¼ ɛ1  ɛ2  . . .  ɛm

13.3.3.1

Physical Processes

Screening Generally, it is employed as a primary separation method to separate solid materials from waste streams which then undergo several unit operations such as drying with rotary vacuum filters and can be converted to animal feed sources. Like in cereal industries, raw materials are subjected to various cleaning operations to separate impurities. Screening is one of the vital steps to remove foreign particles such as stones, chaffs, crop seeds, etc. according to the differences in their physical characteristics, e.g. shape, size, density. To increase the flowability of solid materials and ameliorate the screening rates, methods like mechanical agitation and screen inclination may be implemented (Li et al. 2002). Flotation One of the primary separation methods utilizes three phases, solid phase, liquid phase, and gaseous phase separately. In principle, it is a surfactant-based separation process wherein on adding certain surfactants, a scum appears on the surface after the gas bubbles transport through the solution and solid materials are removed (Kyzas and Matis 2019). Liquid biphasic flotation is a novel technique that has immense applications in the recovery of potential substances from food waste. For example, betacyanin extraction from the peel and flesh of red-purple pitaya (Yi et al. 2019), and protein extraction from expired milk products (Yap et al. 2019) that approaches a green and sustainable environment. Sedimentation It involves the use of gravity to separate solid substances from industrial waste streams. The differences in the specific gravities make these substances settle/deposit as sediments which are then removed using certain clarifiers. In some cases, this physical treatment can also be done in combination with chemical processes. Like in treating wastewater, the use of divalent ions (calcium and magnesium) serves to enhance the sedimentation rates by destabilizing the unwanted particles (O’Melia 1998). A newly fabricated flotation-cum-sedimentation system has been used for the separation of skin and seeds from tomato pomace which was then used for extracting lycopene, a vital phytochemical compound (Kaur et al. 2005).

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Centrifugation It is an efficient separation technique that utilizes centrifugal force to separate particles based on their density differences. It is widely used as a pre-treatment process in the dairy industry for separating cream from skim milk. The food waste generated from kitchens ends up contributing to municipal solid wastes that get treated via fermentation processes followed by centrifugation, resulting in the production of short-chain fatty acids. These can further be used in manufacturing high-quality poly-L-lactate (PLLA) biodegradable plastics (Sakai et al. 2003) as well as in generating biofuel that can be used for cooking purposes (Karmee 2016). It is also observed that centrifugation followed by pH shift (acidic and alkaline) results in the isolation of high-protein fractions from sardine fishmeal waste (Garcia-Sifuentes et al. 2009). Crystallization It is a process in which molecules orient themselves into a structure known as crystal, forming a solid phase from an aqueous solution. It may occur due to certain physical (changes in temperature, pH) and chemical changes. It has significant applications in minimizing food waste, ranging from recovery of protein from whey and phosphorous from wastewaters using lactose (Božanić et al. 2014) and struvite (Le Corre et al. 2009) crystals, respectively. A unique crystallization process referred to as drowning-out crystallization is specifically appropriate for the separation of heatsensitive compounds such as a sustainable recovery of polyphenols from olive mill wastewater (Dammak et al. 2016).

13.3.3.2

Chemical Processes

Precipitation A chemical technique used to separate components from the food waste streams depends upon their solubility characteristics. Due to these factors, the soluble compound turns to an insoluble solid known as precipitate and forms a suspension by getting dispersed in the solution. Precipitation is a significant operation in the recovery of polysaccharides as well as proteins. Usually, proteins are taken to an insoluble state by the action of heat, or by altering the composition of the solution (pH, ions, electrolytes) followed by their extraction using solid-liquid separation techniques. A traditional separation technique, iso-electric precipitation has been generally used for producing casein precipitates and soy protein isolates from milk and soy, respectively. These methods can be performed at a large scale using simple equipment, and at low costs too (Zaror 1992), (Singh and Singh 1996). It also has numerous applications in the treatment of food waste. For instance, bromelain is a protein-digesting enzyme extracted from the pineapple stem which is also reported in the core, peel, and crown of the fruit, major wastes generated from the pineapple processing industry. It was identified that the acetone precipitation yielded a higher recovery of bromelain activity from pineapple wastes (Chaurasiya and Hebbar 2013).

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Coagulation Chemical coagulation is customarily applied in the treatment and purification of industrial wastewater. This method utilizes certain chemical coagulants that destabilize the colloidal particles and aggregates to form micro-floc materials, referred to as flocculation. These flocculant materials are then removed in subsequent filtration, flotation, and sedimentation stages, etc. The technique involves mainly inorganic metal salts, including ferric chloride, aluminium, ferric sulphates, etc. Generally, these metals decrease the pH of waste streams from the alkaline levels to nearly neutral values that exert a strong positive effect in reducing turbidity, suspended impurities as well as chemical oxygen demand (COD) (Renault et al. 2009) as being elucidated in a study wherein a cationic carbohydrate polymer, chitosan was used as a chemical coagulant and the results revealed that the concentration of suspended solids and turbidity were reduced to 97% and 83%, respectively, along with a 45% reduction in COD in seafood processing streams. The process involved the recovery of organic compounds, notably a large concentration of flavour-related free amino acids, including arginine, alanine, glycine, glutamic acid, and serine (No and Meyers 1989). It has been suggested that coagulation followed by ultrafiltration, is a potential method for recovering the polyphenols and proteins from flaxseed hulls (Loginov et al. 2013).

13.3.3.3

Membrane Processes

Reverse Osmosis A general separation technique that applies pressure against the osmotic pressure to force the movement of solvent from a region of high solute concentration to a less concentrated solution through the semipermeable membrane. It is considered as the leading and optimized membrane-based solution in purifying water by desalination process that refers to the removal of salts and minerals from the sea or brackish water (Qasim et al. 2019). It proves to be an alternative process in managing the food industrial wastes, for example in treating the solid wastes generated from the orange juice industry (Mayor et al. 2011), and in reclaiming the wastewater from the dairy industry (Suárez et al. 2015). Micro- and Ultrafiltration In recent years, the use of membrane-based techniques is gaining more attention in food processing industries. These pressure-driven membrane processes are based on certain pore sizes of the membranes to separate the dissolved substances. Indeed, they offer various advantages over conventional separation methods, including non-use of chemical agents, including non-use of chemical agents, operated under mild conditions of temperature and pressure, thereby preserving the functional attributes of food products. Additionally, they are highly selective towards specific compounds of interest, and use simple equipment with a lesser number of processing steps and hence, low energy consumption (Castro-muñoz et al. 2018). The ultrafiltration technique has been employed for the separation and recovery of phenolic

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compounds from almond skin extracts (Prodanov et al. 2008) and grape seeds (Nawaz et al. 2006) depending on their molecular weight. In a study conducted, microfiltration and ultrafiltration techniques were known to recover biomolecules (protein and fat) and permeate (containing NaCl and acetic acid) which makes postproduction marinating brines re-use in fish marination and not discharged as futile effluent (Nędzarek et al. 2017).

13.4

Upcycling of Waste from Food Industries into Other Value-Added Products

13.4.1 Dairy For many years, this industry has gained global attention due to the consumption of a wide range of products including butter, cheese, curd, yoghurt, milk powders, etc. Milk is composed of approximately 87% water, and additional water is required for the cleaning and sanitation of dairy processing plants, thereby producing substantial amounts of liquid wastes. Whey, being the predominant waste generated from the processing of cheese, notably 1 kg of cheese produced is expected to give rise to nine kilograms of whey (Martínez-Ruano et al. 2019). Due to its high biological oxygen demand (BOD) and COD concentrations, it contributes to polluting the environment. Nevertheless, it is considered a significant source for valorization into functional products, including whey protein, whey permeate, bioethanol, biopolymers, hydrogen, methane, probiotics (Yadav et al. 2015) and D-lactic acid (Alexandri et al. 2019; Sakai et al. 2003), and whey protein isolates (Sani et al. 2021). Casein is the second component which leaches out in the dairy waste streams; owing to its biodegradable nature it is suitable for forming edible films which have favourable mechanical and optical properties (Sani et al. 2021). In addition to this, it elicits certain applications in developing value-added food products such as dietary protein, bio protein (for food and feed applications), whey protein isolates for infant formula, beverages, food, and dietary supplements for medical purposes (Rao et al. 2021), functional fermented beverages (AbdulAlim et al. 2018), low-fat meat products (Pame et al. 2020), and ice popsicles using an underutilized crop “jamun”, a natural bioactive source with potential therapeutic benefits (antidiabetic) and enhanced organoleptic properties (Jan et al. 2021). The advanced technological methodologies for the isolation of whey protein and its derivatives include novel separation methods such as vibratory shear enhanced processing, chromatographic technology, high power ultrasound, ion exclusion, and molecular recognition-based isolation techniques (Rao et al. 2021).

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13.4.2 Meat, Poultry, and Its Derivatives The meat industry trashes enough slaughterhouse by-products, including meat meal, meat and bone meal, feather meal, and blood meal, skin, nails, feathers, bones, hides, horns, visceral mass, etc., that contribute to around 60% to 70% of the slaughtered carcass, of which 40% and 20% form edible and inedible waste, respectively (Bhaskar et al. 2007). Carcass rendering in the meat industry is one of the risky affairs because of the perceived risk of incomplete destruction of all pathogens, viruses, and prions (Shurson 2020). To minimize these wastes, one of the significant approaches is anaerobic digestion that has been proven to be a promising and green alternative for the recovery of nutrients (N, P, Vitamins, Protein) as well as energy from the industry-derived organic wastes with high protein and fat content. This strategy aids in the anaerobic bioconversion of these wastes into the generation of biofuel and biofertilizers (Onwosi et al. 2020). Bioactive peptides, mineral binding peptides, and plasma proteins derived from blood and collagen obtained as meat industry waste have various health promoting properties (Rao et al. 2021). Additionally, the poultry processing industry generates feather by-products that are known to be a considerable source of structural protein (keratin) that can be used as a raw material for producing cheap keratinase enzymes followed by their further valorization into sustainable value-added products by hydrolysing it into feather meal (Tesfaye et al. 2017). Also, the development of protein hydrolysates from the pre-treated sheep visceral mass explains their role as functional ingredients in raising the protein quality of foods (Bhaskar et al. 2007) mainly as a flavouring agent (Rao et al. 2021). The blood meals obtained from these industries can be spray dried and can be used for feed and microbiological media (Shurson 2020). Meat processing waste is also a source for various technological applications in the food industry such as immunoglobulins, fibrinogen, and serum albumin which can be used as emulsifying and gelation agents, plasma proteins as foaming and protein enrichment, white blood cells (WBC) as an antimicrobial agent, enzymes such as fibrinogen and thrombin as binding agents in meat product processing, gelatin obtained from collagen as a gelling agent, stabilizer, clarifier, biodegradable edible films (Sani et al. 2021), and coating materials.

13.4.3 Seafood With increasing urbanization, the worldwide consumption of fish and its associated products is increasing rapidly and around 70% of the fish consumed globally is processed in the industries before being sold to consumers (Araujo et al. 2020). After processing, the industry generates a huge quantity of waste that poses a serious ecological threat to the environment. These seafood by-products are considered to be novel sources for the recovery of valuable biomolecules such as collagen and gelatin which can be utilized in developing functional food ingredients (Pal and Suresh

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2016). Astaxanthin, a keto carotenoid recovered from ornamental fish, seafood industry wastewater is widely used as a colouring agent in fish diets and serves as a precursor of Vitamin A exerting high antioxidant effects. It has been highlighted that the application of solid waste (fish scales) as a natural adsorbent has resulted in the isolation of this pigment from the seafood industry wastewater (Stepnowski et al. 2004). Also, seafood waste has the potential to be used as a cheap medium for the growth of several proteolytic and chitinolytic microbes with the simultaneous recovery of value-added products (Satyanarayana et al. 2012). Fish viscera is also a by-product of the fish industry which is used in the conversion of poultry and fish feed (Shurson 2020), and shells from molluscs are converted into handicrafts and decorative pieces. Also, the fish processing wastes are a reservoir of value-added bioactive compounds such as protease enzymes obtained from fish visceral waste which may exhibit potential applications in de-staining capabilities against bloodstained cloth and dehairing goat skins (Sabtecha et al. 2014).

13.4.4 Cereals and Pulses Cereals and their products are consumed by a vast majority of the population and hence are considered staple foods in their daily diet. Since starchy endosperm (a rich source of carbohydrates and energy) is mainly used in the cereal processing industries, the outer layers of cereal grain kernel (germ, husk, and bran) remain as by-products (Roth et al. 2019). Research studies emphasize that these by-products comprise a huge nutrition potential and can be valorized into functional products. It has been elucidated that the fortification of brans of the three different local kinds of cereal (maize, rice, and sorghum) significantly ameliorated the nutrient profile (lipids, proteins, fibres) of breads, suggesting bran to be a vital ingredient in the formulation of functional foods, and can be used as an alternative in preventing various chronic non-communicable diseases (Pauline et al. 2020). Rice straw also contributes to an agricultural waste rich in cellulose that can be upcycled to sustainable bioplastic, a potential eco-material for different applications (Bilo et al. 2018). Protein fractions obtained from waste streams of pulses provide a very good source of forming biodegradable packaging material. Sani et al. (2021) upon reviewing various articles found out that proteins of soybean and peas have good mechanical properties but poor barrier properties. Upcycling of Distiller Grains The cereal grain-based fuel-ethanol plants generate distiller’s dried grains with solubles (DDGS) as one of the principal co-products of the dry-grind distillation process. It is believed that 100 kg of grain delivers approximately 40 L of ethanol, 32 kg of CO2 as well as 32 kg of DDGS (Chatzifragkou et al. 2016). And, the rapid increase in ethanol production has led to their production in excess amounts. Although, since earlier times, DDGS has been marketed as a feed for livestock but it is essential to embrace its potential for value-added uses. Certain nutrients,

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including linoleic acid, dietary fibre (beta-glucan), and antioxidants (e.g. Vitamin E) are concentrated in the DDGS and hence, it is suggested to recover them (Gibreel et al. 2011). In a research study, wet solids were proven to be more suitable as a raw material for protein extraction, unlike DDGS extracts where it was probably due to the protein aggregation during the drying process. The upcycling of distiller grains direct towards future implications in such a way that they can be explored for the development of biodegradable coatings, films, and biodegradable plastics, which can be utilized for food and agricultural purposes (Chatzifragkou et al. 2016).

13.4.5 Fruits and Vegetables Fruits and vegetables are regarded as perishable commodities that often undergo processing for their shelf-life extension while the fruit and vegetable-based industries generate a huge quantity of horticultural waste, (including skin, seeds, pomace, etc.) accounting for 25–30% of the total product. The sustainable solution for managing such waste lies in its utilization and developing valuable products such as biosorbents, carbon dots, edible films, probiotics, nanoparticles, etc. (Kumar et al. 2020). The pomace from apple, tomato, and other fruits can be converted into animal feed as a whole and the derivatives such as dietary fibre, antioxidants, and pigments find its use in food application by using suitable extraction technologies by keeping their toxicological components into account such as toxin amygdalin in apple seed, pesticides on apple skin, tomatine (a toxic glycoalkaloid in tomato), solanine in potato peels (Rao et al. 2021). Owing to the higher concentration of bioactive compounds and nutraceuticals (such as carotenoids, dietary fibre, fatty acids, phenolic compounds, proteins, etc.) present in the fruit and vegetable residues, they find immense applications in formulating functional foods and food additives (JiménezMoreno et al. 2020). The peel, seeds, and membranes of fruits, vegetables, and tubers can be used for extracting high value products such as fibre, soluble sugars, organic acids, lipids, vitamins, minerals, and flavonoids (Rao et al. 2021). For example, the incorporation of grape pomace powder in developing valueadded functional cookies elucidates a sustainable approach of utilizing food industry waste streams by the application of 3D printing technology (Jagadiswaran et al. 2021). Another potential way is the incorporation of pea pod powder (by-product emanating from the pea processing industry) in formulating functional products like instant pea soup powder (Hanan et al. 2020) and mayonnaise (Rudra et al. 2020), thus representing pea pod shells as a promising candidate for supplementation of foods with explicit nutritional benefits. Also, the recovery of bioactive compounds from the valorization of industrial wastewater represents an exciting opportunity for developing value-added products as well as minimizing adverse environmental impacts (Chen et al. 2019).

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13.4.6 Nuts and Oilseeds Major waste streams from these industries are of oilseed cakes and meals which are a source of nutritionally rich and low cost like spent grains from which the cake fraction being already nutritionally rich finds its applications in the bakery, infant food, animal feed and supplements but they are not directly used in food-based applications because of the antinutritive compounds like polyphenols and phytic acids which gets concentrated after oil expel but it can be reduced by using aqueous ethanol extraction. These antinutritional properties are found more in rapeseed and soybean cake and meals (Rao et al. 2021). The cake and meals of oilseeds and nuts can also be used for the production of bioactive compounds like amino acids, flavours, vitamins, pigments, enzymes, phenolic acids, lignans, and flavonoids. These cakes also act as an excellent substrate for solid and submerged fermentation and mushroom cultivation.

13.5

Polyphenols from Waste Streams in Food Industries

The food industry is undoubtedly one of the largest sectors that generate enough amount of waste causing harm to the environment. In a review study, it was highlighted that in developed countries, 42% of food waste is produced by household chores, 39% losses occur in food processing industries, 14% in the food service sector (catering, and restaurants), while the rest 5% occurs during retail and distribution (Mirabella et al. 2013) (Fig. 13.1). Over the last years, several studies have indicated that these wastes are known to be a valuable source of nutrients, especially polyphenols and bioactive compounds that are of significant use for human health. However, these are discarded along with trashing the waste, and hence, it is necessary to recover them. Polyphenols are generally classified into certain categories such as phenolic acids, flavonoids (flavones, flavonols, flavanones, flavanols, isoflavones, proanthocyanidins), and their derivatives, stilbenes, and lignans. These are reported to exert pharmacological effects by acting as antioxidants, Fig. 13.1 Percentage of food waste generated in developed countries

Food waste 5% Household chores

14% 42%

Food processing industries Food service sector

39%

Retail and distribution

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antimutagens, antimicrobial, and anticancer agents and minimize the risk of developing associated disorders (Gharras 2009). The extraction of these compounds is of major interest. Traditionally, the extraction was based on solvent extraction methods, such as solid-liquid extraction, liquidliquid extraction, etc. but as encompassing the time, today’s era direct towards an approach of using novel technologies, including ultrasound, microwave, enzymeassisted, and membrane separation (Cai et al. 2021). Availability of these techniques supports the optimal recovery of the phenolic compounds and provides an opportunity for using them in developing certain functional food products. As observed in the case of citrus (e.g. orange, lemon, clementine) peel wastes, extraction of value-added polyphenols proves to be a sustainable method in reducing citrus processing waste with immense interest in formulating therapeutic foods to prevent chronic diseases (Gómez-mejía et al. 2019). Moreover, it has also been recognized that the total polyphenol content is higher in peels of citrus fruits, including lemons, oranges, and grapefruits than those of peeled fruits (Gorinstein et al. 2001). The upcycling of polyphenol-rich almond skins, a by-product of the confectionery industry in the development of functional biscuits proves to be another example (Pasqualone et al. 2020). Several examples of food wastes that yield potential bioactive compounds are listed in Table 13.3. In a research study, several secondary metabolites have been elucidated from two sub-streams of agricultural waste, grape pomace, and olive leaves. The former was known to contain phenolic compounds such as resveratrol, anthocyanins,

Table 13.3 Polyphenols obtained from the food waste streams and their sources S. No. 1.

Source Coffee

Waste Pulp, husk

Phenolic compounds Chlorogenic acid, protocatechuic acid, gallic acid, and rutin

2.

Olive

Leaves

Secoiridoids, oleuropein, apigenin, kaempferol, luteolin, caffeic acid, tyrosol, hydroxytyrosol

3.

Wheat

Bran

4.

Tomato

Skin, seeds

Ferulic acid, lutein, cryptoxanthin, syringic, p-hydroxybenzoic, vanillic, coumaric acid Caffeic, chlorogenic, p-coumaric, ferulic, rosmarinic acid, quercetin, rutin

5.

Grape

Skin, seeds

6.

Mango

Kernel

7.

Onion

Fleshy scales

Gallic acid, p-hydroxybenzoic acid, gallic acid, syringic acid, caffeic acid, ferulic acid, p-coumaric acid, catechins, proanthocyanidins, quercetin, myricetin, rutin resveratrol, kaempferol Gallic, ellagic, caffeic, coumaric, protocatechuic, ferulic acid, mangiferin, homomangiferin, isomangiferin, anthocyanins, kaempferol, quercetin Quercetin, kaempferol, myricetin, isorhamnetin

Reference Heeger et al. (2017) Talhaoui et al. (2014) Zhou et al. (2004) Ćetković et al. (2012) Mattos et al. (2017) Mwaurah et al. (2020) Pal and Jadeja (2019)

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proanthocyanidins, catechins, and quercetins while the latter contained secoiridoids and oleuropein. These are all compounds that exert protective effects against the oxidation of low-density lipoprotein (LDL) in blood circulation along with improvements in lipid metabolism, hence reducing the risk of obesity (Cravotto et al. 2018). Also, extracts rich in polyphenol content possess the potential to fortify food or nutritional supplements to enhance the antioxidant and antimicrobial efficacy of daily diets (Mourtzinos and Goula 2019). For example, the industrial processing of coffee yields by-product formation such as coffee pulp and husk that reports 29% and 12% of the dry weight of the original cherry coffee, respectively, and are found to contain a considerable number of polyphenols, including chlorogenic acid (most abundant) followed by the presence of protocatechuic acid, gallic acid, and rutin. It has been recognized that such underutilized by-products can be valorized by formulating a refreshing and nutritious Cascara beverage loaded with a high level of antioxidants (Heeger et al. 2017).

13.6

Recovery and Upcycling of Macronutrients from Food Industry Side Streams

The ultimate fate of macronutrients (Nitrogen (N), Phosphorus (P), Potassium (K), Calcium (Ca), Magnesium (Mg), and Sodium (Na)) from the food industry are not completely to the gut of the consumer rather few parts of it leach out in the side streams as well. Thus, for recovering valuable nutrients from various side streams of the food industry, for instance, feed and food processing water (Matassa 2016) the treatment process is very important (Buckwell and Nadeu 2016) in which the liquid fraction of digestate produced from the food industry is usually processed as shown in Fig. 13.2. A review done by Chojnacka et al. (2020) highlighted that bones and bone mass streams from slaughterhouses are a valuable source of phosphorus recovery and

Fig. 13.2 Flow chart of recovery of macronutrients from food industry side streams. VSEP vibratory shear enhanced processing, WTP wastewater treatment plant

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phosphate fertilizers can be made by pyrolysis of slaughter waste. Carcass rendering from the meat industry can be a major source for N and P (Shurson 2020). Fish waste side streams of the food industry when combined with few bulking agents provide a valuable fertilizer enriched with N, P, and Ca whereas the waste keratin materials (hairs, feathers, etc) provide a good source of N when digested with strong acids. K can be recovered from waste streams of the feed and fodder industry.

13.6.1 Proteins Out of the total global waste which is above 450 million kg/year, 10% is protein. Side streams of industries like cereal, brewery, oilseeds, dairy, fish, and slaughterhouse are good sources of protein that can be processed into high-quality foodstuffs or ingredients, before finally ending up into raw materials for the compost, fertilizers, or biogas. Protein extracted from side streams of the food industry provides a sustainable alternative approach to meet the protein needs globally (SchweiggertWeisz et al. 2020) but the process of separation is challenging in terms of texture, taste, and increased bioavailability. Here are few examples of protein recovery from side streams of major industries: For recovery of proteins from slaughterhouse side streams like bone materials, the important processing parameters are hydrolysis time, the liquid to solid ratio, and the enzyme to substrate ratio. The liquid: solid and the enzyme: substrate ratio has a significant role on protein recovery which is approximately 90% under optimal conditions. From the fish industry side stream, the process is as followed: Fish head bones Enzyme assisted processing

Solids, bones

Soluble protein and oil

From oilseed side streams, the protein recovery process is as follows: Oilseed meal Enzymatic treatment, carbohydrate degrading enzyme Centrifugation

fibrous residual mass

Supernatant Membrane filtration

Permeate: phytochemicals, minerals, sugars, peptides

Protein concentrate

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The proteins recovered from wheat and rice bran streams are considered to be of high quality (Schieber 2017). They can be used for rearing the insects as a promising source of conversion of protein to protein (Ojha et al. 2020) and there are insects (e.g. black soldier fly larvae) which in turn helps in the nutritional upcycling of agrifood industry side streams and waste biomass which are reported to yield high-value protein, chitin, lipids, and frass (the combination of undigested leftovers of the substrates and organic refuse excreted by insects) (Ravi et al. 2020). The side stream protein sources and processes for other industries and new products simply take into account the following factors (Pihlanto 2019): 1. Adaptation of their industrial process with their production, transformation to products. 2. Better knowledge of their health impact and their nutritional value. 3. Social acceptance of sidestream protein sources consumption.

Production of Single-Cell Protein, Edible Mushroom, or a Vegan Protein Source Agricultural and food industrial side streams can be used as a sole source of carbon for the submerged propagation of mushrooms. Ahlborn et al. (2019) performed the same with apple pomace in flasks which resulted in the biomass of mushroomsubstrate combination having a composition of 21% protein, lipids (4%), ash (2%), and carbohydrates (74%) with dominating fatty and amino acids like linoleic acid, glutamic acid/glutamine, and vitamin D. The production of single-cell protein, microbial protein, and fungal cells (Matassa 2016) allows the use of food waste side streams as a substrate which helps in secretion of enzymes such as cellulases, amylases, pectinases, inulases, proteases, and lipases, into the surrounding medium and hydrolyse plant polysaccharides (e.g. cellulose, starch, pectin, inulin), proteins, and lipids (Meyer et al. 2020). Extracted enzymes like phytase, amylase, β-glucanase, and xylanase are added to cereal-based diets to increase the utilization of dietary phosphorus, starch, beta-glucans, and arabinoxylans (Ghorai et al. 2011). Souza Filho et al. (2018) developed a vegan-mycoprotein concentrate from a pea-industry by-product using edible filamentous fungi with potential application in human nutrition in terms of enhancement of protein content in the final product.

13.6.2 Carbohydrates The cereal bran in general is a good source of bioactive components, for example, non-starch carbohydrates, polysaccharides and oligosaccharides, phenolic compounds, lipid-soluble vitamins and folic acid, and phytosterols. The valorization of carbohydrate-based food waste streams results in the production of lactic and succinic acids and ethanol, recovery of ferulic acid for subsequent conversion to vanillin, protein extraction for the production of amino acids, and arabinoxylan

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extraction which can be used for probiotic encapsulation as a potential application. Wheat bran contains high-quality proteins, which may be used for the fortification of foods and the production of amino acids and biologically active peptides. Valuable compounds present in rice bran include proteins, lipids, dietary fibre, minerals, and antioxidants such as vitamin E and oryzanol (Schieber 2017). The olive oil pomace also contains mainly carbohydrates, polyphenols, minerals, and residues of lipids and thus can be recovered from the same taken into account that the recovery of these nutrients from wastewater generated from olive oil production facility is not easy because of acidic nature and black coloured stream (Schieber 2017).

13.7

Smart Technologies for Upcycling Side Streams

1. Using insects as biofactories for nutritional upcycling of food wastes: The upcycling of nutrients with insects guarantees a consistent macronutrient profile that can be incorporated efficiently in feed formulations (Ravi et al. 2020). This technology of upcycling is gaining popularity with the rise of interest in the alternative protein sector. One such case study is of OrganicFe Co. (Indonesia) which utilizes black fly larvae waste stream obtained from vegetable and fruit supply chain to produce organic fertilizer and maggot flour which is again sold to crops supply chain, poultry, and fish chain which helps in maintaining a circular economy (Nattassha et al. 2020). 2. Using agricultural and food side streams as biomass for the cultivation of other valuable products: Valorization of the substrates obtained from the side streams offers an additional advantage to the yield in the form of enhanced nutrition (Ahlborn et al. 2019) when compared with usual cultivation methods. This can be used for microalgae-based processes (Acién Fernández et al. 2018), cultivation of single-cell protein, edible fungi, lab-grown meat (Matassa 2016), and its vegan alternatives such as mock meat, fish, and dairy products. 3. Fuelling biogas reactors: Organic wastes and residues from side streams from agri-food processing industries (such as animal by-products from abattoirs, brewers spent grains, etc.) act as a principal substrate combining with the digestate of biogas plants that comprises of other raw materials like animal manure, slurries and crop residues (Drosg et al. 2015). 4. Upcycling by-products and side streams to develop sustainable packaging: The presence of bioactive compounds in agro-industrial by-products elicits their application in developing renewable and biodegradable biopolymers with significant antimicrobial properties (Dilucia et al. 2020). Moreover, several waste streams act as substrates for their bioconversion into bioplastics such as polyhydroxyalkanoates (PHA) (Yadav et al. 2019). This application renders the physicochemical properties of food biowaste which is also used for converting the waste into smart materials for food packaging and sensors (Halonen et al. 2020). Valorization of streams of acid whey, coffee mucilage, and rice husk can be separately used as alternative feedstock for D-lactic acid production which in

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turn can be used for the synthesis of polylactic acid (PLA—a biodegradable polymer) which is used for the development of biodegradable packaging materials for food, cosmetic, and pharmaceutical industry (Alexandri et al. 2019). 5. Thermal processing of waste streams into animal feed: Conversion of waste streams into animal feed can be done by adequate thermal processing of waste streams in order to avoid disease outbreaks due to pathogen transmission. Shurson (2020) in their study highlights that Japan, South Korea, and Taiwan have developed a well-established substantial infrastructure with a conversion rate of 35–43% waste streams into pig feed. This technology is very much efficient in converting food waste streams into swine and poultry feed. 6. Valorization of Carbohydrates, Proteins, and Lipids present in food and agricultural waste streams using immobilized enzyme system: Along with proteins and lipids, various carbohydrate derivatives (monosaccharides, disaccharides and polysaccharides) can also be valorized by using immobilized enzymes. The selection of immobilization methods, source of enzymes, and implementation of enzyme systems is carried out with regard to cost efficiency. The suggested means are using purified enzymes, inexpensive carriers along with immobilized enzyme systems that utilize whole-cell or crude extracts, genetic modification of enzymes that enables site direction, and trying multi-enzyme systems (Andler and Goddard 2018). The designed biocatalysts by using immobilization methods have the potential to improve sustainability of the food industry through the creation of value-added products and have a positive impact on reducing food waste generated from food processing waste streams. 7. Conversion of waste oil into biofuels by using enzymes: Transformation of waste frying oil into biofuels can be done via transesterification which produces soap when a chemical catalyst is used in this method and to avoid the same lipase enzyme transesterification is done which prevents soap formation and reduces by-product formation. This method promotes green processing conditions which is active in solvent-free conditions (Andler and Goddard 2018; Vescovi et al. 2016).

13.8

Conclusion

Valorizing heavy and bulky wastes from the food and agricultural industries is a complex, energy-consuming process. Thus, the upcycling of food waste biomass maintains a circular economy and is an important step towards food sustainability through which macronutrients, micronutrients, and substrates can be obtained. To eradicate the food wastes and rescuing them from going into vain, several initiatives across the globe have come forward to upcycle those foods along with the objective of formulating innovative and health-oriented products which can be from any stream of the food chain, be it from the organic leftovers of fresh fruits and vegetables, scraps of cereal grains, by-products of the dairy industry. The remnants of foods including peels of fruits and vegetables have been found to transform into

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edible coatings, referring to the natural biopolymers. Therefore, food waste stream upcycling technologies are escalating not only to reduce waste but also for recyclable approaches such as sustainable processing and packaging along with preservation of various kinds of perishable foods having a limited shelf life. The future implication of utilizing such upcycling technologies lies in directing towards a route of achieving sustainable attributes, minimizing food wastage along formulating value-added products, and more emphasis should be given to the treatment of food waste streams along with a mixture of other substrates and by using different methods.

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Chapter 14

Sustainable Value Stream Mapping in the Food Industry Himanshi Garg and Soumya Ranjan Purohit

Abstract Food wastage and loss are the biggest issues of the industry due to poor inventory practices and management approaches. To achieve sustainability, a comprehensive approach is required in product designing, manufacturing processes and systems, and the entire supply chain. Green and lean manufacturing are the two holistic approaches to deal with waste, pollution, and sustainability issues in the food industry. Further green manufacturing is efficient in the management of raw materials, energy usage, process, health, safety, and waste concerns all of which are necessary for achieving socio-economic and environmental sustainability goals. Thus, this chapter presents the integration of lean and green manufacturing, Internet of Things (IoT) integration with lean management, case studies focusing on Sustainable Value Stream Mapping (SVSM) establishments in the food industry with smart lean practices. Further, the chapter describes challenges in the application of smart lean principles and future perspectives of implementing this system. Keywords Sustainability · Lean manufacturing · Value stream mapping · Life cycle assessment · Zero defects in food manufacturing

14.1

Introduction

Industries are critical to the global economy. Many different types of resources are used and discarded during the manufacturing process, resulting in waste generation, carbon emissions, environmental pollution, and ecological deterioration. Several

H. Garg Amity Institute of Food Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India S. R. Purohit (*) Amity Institute of Food Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India Food Engineering and Technology, Tezpur University, Tezpur, Assam, India e-mail: [email protected] © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies, https://doi.org/10.1007/978-981-19-1746-2_14

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steps have been taken to increase manufacturing efficiency and resource utilization to accelerate the transition to sustainable manufacturing and a circular economy. Increasing material utilization necessitates that businesses consider various methods of reducing, recycling, and reusing their raw and waste materials, as well as extending the life cycle of their products. However, with the growing interest of industries and consumers in sustainability, global industries are targeting to double the output with 50% fewer resources and only 20% of current carbon emissions (Hedlund et al. 2020). Also, it is the social responsibility of industries to use eco-friendly methods to reduce the burden on the environment and waste generation. In this regard, one of the widespread philosophies used in the manufacturing industries is lean manufacturing, which corresponds to a system that addresses a range of practical management applications. The main goal of lean production is eliminating waste, reducing cost, and increasing efficiency. Subsequently, Value Stream Mapping (VSM) is considered as a lean method for efficient processes by identifying energy, cycle times, downtimes, delays, waste, and material flows. VSM is also a potential to evaluate value creation throughout the entire value chain from raw material collection to finished product. However, due to societal and environmental constraints and emerging new possibilities from the circular economy, VSM is not an eco-friendlier solution. Lean manufacturing practices are increasingly being evaluated and used as a catalyst to develop better green and sustainable manufacturing strategies (Mahlmann Kipper 2018). Therefore, finding the potential of Value Stream Mapping has found great attention in identifying environmental and societal impacts/waste (Faulkner and Badurdeen 2014). LCA is the most objective tool in environmental practices because it can assess a system’s potential environmental impacts (Bhatt et al. 2019). Manufacturing companies must understand the environmental impacts of their products at each stage of production. A life cycle perspective can help manufacturers identify potential improvements throughout the industrial system and at all stages of the product life cycle. The lean improvement process can be focused on specific environmental improvement actions by combining LCA and VSM. It also provides immediate benefits in terms of tracking the environmental effects of lean improvement initiatives. As a result, this chapter discusses LCA and VSM integration, smart and lean manufacturing in the food industry, and case studies focusing on sustainable VSM.

14.2

Lean Manufacturing

Lean means “downward slope” in the graph, while management means “planning and controlling processes systematically.” This term has been initially developed for the automobile industry. It can be regarded as a system approach that necessitates the collaboration of all value chain actors with the common goal of increasing customer satisfaction (Halloran et al. 2014). It is a set of principles for reducing waste, time, defects, and other unimportant aspects of food manufacturing. Furthermore, despite its origins in the manufacturing

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Overproduction Production more than the required

Unnecessary Inventory Working capital and space used for raw materials, components, work in progress and finished products.

Defects Materials, labor and time used in non – compliant products production

7 Types of Waste

Waiting Delay in meeting customer demands

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Transport Transport of materials across the locations in non- value adding

Movement Unnecessary movement of person and machinery

Overprocessing Oversized equipment, unnecessary technology utilisation

Fig. 14.1 Seven types of waste identified in industry

industry, lean can be applied to any process-driven environment, regardless of industry. The ultimate goal of lean is to identify and then eliminate or change any part of a process that does not add value. Lean enables faster responses to changing customer demands, resulting in more robust production, higher quality, and lower costs. However, due to the perishability of a wide range of food products, the complexity of the agri-food supply chain, and dynamic consumer preferences, its penetration into the agricultural sector has been slow (De Steur et al. 2016; Dora et al. 2016). It entails identifying the seven lean wastes presented in Fig. 14.1. Five principles should be followed to reduce these waste forms: • specify the worth, where the worth must be specified in terms of quality, time, and price from the customer’s perspective; • locate the value flow along the value chain by identifying value streams; • make the value flow in an unbroken stream wherever and whenever possible; • allow customers to pull value from the end of the value chain rather than stocking to avoid unnecessary waste; • strive for perfection through constant improvement.

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14.2.1 Value Stream Mapping Value Stream Mapping (VSM) is defined as “a tool that helps in seeing and understanding the materials and information flow of a product as it makes its way through the value stream” (Liu et al. 2020). Poor quality, reduced productivity, and increased costs are resultant of unawareness regarding waste types. A few examples of waste identified in the food industry are presented in Table 14.1. Conducting a VSM enables identification of challenges, where time is wasted on non-value-added activities. Therefore, VSM can be a potential tool in industrial applications, which can help in the visualization of the interdependence between processes, improving the effectiveness of value chain analysis by enhancing consumer value at each stage, boosting food production and service (Ziara et al. 2018), minimizing wastes in convenience food manufacture and improve the efficiency of a food contract manufacturer.

Table 14.1 Hotspots requiring VSM application Targeted industry Canned peach processing

Targeted step Peeling Pasteurizing

Bread manufacturing

Cooling and slicing Inventory Motion Storage

Food service/ hospitals

–

Problem identified Energy-consuming steps Increased chemical load in wastes and production cost

Breakage during slicing due to improper cooling hence generating waste Improper line balancing Manual work Poor and overtopping, overbaking, nutrient loss, variation in size/shape Overproduction Excess raw or finished product Product defect Short shelf life Menu and nutritional considerations; food procurement; food production; foodservice and patient expectations

Solution Using steam in place of lye peeling Condensed steam and insulation of pipes will reduce energy loss The neutralization step could be omitted Use first in first out (FIFO) technique for the raw and finished product Line balancing to reduce unnecessary motion Manual slicing was replaced with semi-automated slicing Proper inventory A menu detailing with ingredients Patient satisfaction

Reference Folinas et al. (2015)

Goriwondo et al. (2011), Sathiyabama and Dasan (2013)

Ahmed et al. (2015)

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Fig. 14.2 Steps involved in VSM establishment

14.2.2 How Does a Value Stream Map Look like? The idea of VSM is to be able to visualize an operation completely, showing how value is added to a product at each and every step against a timeline. Though there are certain similarities between VSM map and a process map, however, distinctive features in VSM are as follows: • Presence of a timeline • The data boxes, which are used to collect data needed from each process VSM is a method in which the present and future state of a product/process are first mapped. Once both the current and future states have been mapped, the third step in the VSM method is to create an action plan that will lead to the future state as presented in Fig. 14.2. For a long time, organizations are prioritizing processes and value chain development (Hedlund et al. 2020; Porter 2011). To be more competitive there is a need to improve efficiency. Thus, VSM can assist enterprises in identifying and reducing waste while also improving value creation. This could be accomplished by viewing the entire process through the eyes of a system. Waste reduction can take many forms besides traditional seven wastes. However, VSM has not yet systematically incorporated the principles of circular economy, environmental impact, and energy consumption.

14.3

Sustainable Value Stream Mapping

Norton and Fearne (2009) and Simons and Mason (2002) have proposed a method termed as Sustainable Value Stream Mapping (SVSM) to ensure sustainability by taking carbon footprint into account with due importance to time for value-addition.

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Conversely, SVSM approach has been applied to understand carbon dioxide emissions through the supply chains for cherries, apples, and lettuce. When attempting to improve the sustainability of a supply chain, it is critical to include the process steps and to measure other performance indicators in addition to CO2 emission (Norton and Fearne 2009), which gives a complete idea of carbon footprint.

14.4

Applications of VSM and SVSM in Industry

Implementation of lean principle with compromised weightage to impact of environmental performance may not be effective in ensuring sustainable processing and supply chain. Researchers have examined the impact of lead time compression on CO2 emissions using a simulation model (Norton and Fearne 2009). They have reported that a lean supply chain may lead to higher CO2 emissions. For example, in a refrigerated/frozen product supply chain, the lean principle suggests frequent deliveries of smaller quantities to reduce storage and refrigeration burden on the manufacturer side. Holding smaller stocks ensure less electricity consumption and associated greenhouse gas emission. However, the frequent deliveries do contribute to CO2 emissions and may aid to overall increase in CO2 emissions. Thus, there should be an optimal order size within any individual supply chain that balances inventory level and delivery frequency to ensure the lowest CO2 emissions. However, CO2 emission is just one aspect of the environmental performance of a supply chain.

14.5

Integration of LCA and SVSM

Industrialization has altered society and its interactions with the environment by increasing natural resource usage and the rate at which new products and processes are developed. LCA is an environmental assessment tool that investigates potential environmental impacts of products and services through the whole life cycle from “cradle to grave.” The environmental impacts of a product are assessed at every stage, from raw material extraction to materials processing, manufacturing, and distribution, and finally disposal or recycling. An LCA (Life Cycle Assessment) study consists of four main phases: • Target and scope of assessment: It articulates the background of the assessment and ensures technical information essential for the assessment. • Life Cycle Inventory (LCI): It involves the entire inventory like water, energy, material, etc. in an inventory flow form and also includes various discharges and releases to the environment. • LCIA: Life Cycle Impact Assessment (LCIA) identifies and evaluates the degree of potential environmental impacts and whether they are under ISO standards.

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• Interpretation Phase: Identifies, quantifies, and evaluates the results obtained from LCIA. In a study, Vinodh et al. (2016) recorded the environmental impacts using a sustainable value stream map having parameters like acidification, carbon footprint, eutrophication, and total energy consumed. Based on that a future state process maps were modeled for the sustainable performance of the industry. Further detail on this can be accessed through bibliographic information at the end of the chapter.

14.6

Lean Manufacturing in Food and Beverage

The word lean means a slope indicating a downward trend in a graph. Whereas management means planning and systematically controlling different processes. It deals with reducing waste, time, defects, and other negative quotients related to various manufacturing processes and has been functioning in the food industry for decades. It is an intricate and detailed process involving factors like scientific management, industrial engineering, machine automation, and supply chain management. To fully grasp this concept, it is crucial to understand the four key components of lean management which are as follows: • Pull: It means the raw material supply must be generated only when there is demand from the industry, hence helping in reducing wastage and shortage of limited resources. • One-Piece flow: It focuses on one product production at a time to improve quality, reduce hurdles and wastage of energy. • Pulse: It focuses on increasing efficiency and productivity with better time management of production units. • Zero Defect: This key focuses on the elimination of errors from the root. Or in other words, it is the reverse approach of quality control and assurance. The food industry deals with various challenges in meeting consumer demands like readily perishing items that are not easy to transfer and store. However, the most dominant areas of lean management implementation in the food industry include: • The Warehouse: The place where raw materials are collected. With the help of LM process and special storing procedures lesser waste of raw material, longer preservation, and better results could be achieved. • The Production Line is where humans and machines perform various activities like cutting, mixing, peeling, cooking, brewing, and packaging. The automation due to Lean Management has helped in reduced time and increased output. However, better precision throughout production (in terms of defects and record-keeping) has been achieved with AI and software integration in the food industry.

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• Quality Assurance: With the implementation of LM at operation units the product quality is no longer dependent upon merely tossing out the defective batches of food and beverages. It depends on finding the root cause. • The Logistics: LM has aided the supply chain and food industry in the development of packaging for long-term and cost-cutting purposes. • The Costs: Implementing lean management in the food and beverage industry means producing more products in less time, storing them better, and improving logistics. As a result, manufacturing costs decreased. Hence, in a nutshell with the implementation of LM in the food industry based on its broad areas of application, practical implementation, and tremendous advantages have the most prominent benefits which are as follows: • Less Waste: Raw material and finished product waste is reduced as a result of improved warehouses, limited production, and more advanced manufacturing techniques. • Preservation of Limited Resources: Resources such as freshwater, fruits and vegetables, electricity, and labor are scarce. Lean management aids in the preservation of these resources and the creation of more utility from them. • Demand-Based Production: Selective production in response to viable demand has enabled manufacturers to save money and reduce stock losses. • Enhanced Productivity: Lean management has aided food scientists and engineers in producing better products in less time. As a result, overall productivity has increased. • Efficient Logistics: Because of lean management, raw materials last longer, and products are produced in response to demand. It has also helped to reduce supply chain lags. • Higher Quality: With lean management, the root causes of defects are addressed. As a result, the possibility of defects has been reduced to a bare minimum. • Lesser Prices: At every stage, lean management has revolutionized the food and beverage manufacturing industry. When production costs fall, consumer costs fall as well. In the last decade, lean management has become an essential component of the food and beverage manufacturing process. This system’s innovations and solutions have benefited all stakeholders from farm to fork or throughout the supply chain.

14.7

Smart Lean Manufacturing in Industry

No doubt, to meet consumer demand and stay competitive in the market, manufacturing companies look for new alternatives for the betterment. In this regard, lean manufacturing has been in the role for the last 20 years which is a simple and less technical approach. But with the increasing demand for customized food products, strict competition, 360 stakeholders have led to the integration of IT,

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Table 14.2 Lean manufacturing and smart lean manufacturing Lean manufacturing Decentralized control Focus on transparency Weak and simple Addresses problem from the root cause and authenticate employees to take action

Smart lean manufacturing Centralized database Disconnect reality and abstract information Rigid, complex, and robust Encourages workarounds rather than addressing the root cause

Reference Åhlström et al. (2016)

sensor-equipped, self-configured digitalization of the food industries using cheaper and powerful networking sources such as wireless technology, cloud computing, big data, and artificial intelligence. All this led to the introduction of Industry 4.0 which targets the automation and digitalization of manufacturing companies (Buer et al. 2020; Shahin et al. 2020). LM and smart LM can be presented in Table 14.2: If the contrasting statements presented are ignored, then the objective of lean manufacturing and Industry 4.0 is the same to improve the industry performance. Lean efficiency and establishment are being hampered by the complexity of manufacturing systems, changing global market trends, customer behavior, and short product life cycles. In this regard, manufacturers are increasingly focusing on the use of industry 4.0-based digitized techniques to improve operations while ensuring complete customer satisfaction (Netland 2015; Ramadan and Salah 2019). The lack of real-time monitoring of the systems is challenging in this regard, as production systems are dynamic and difficult to capture matters (Metz et al. 2012). Furthermore, in traditional lean environments, workers do not always follow leanbased established instructions due to a lack of real-time mechanisms that improve lean instruction practice. As a result, there is a scarcity of established systems with real-time monitoring that ensures lean manufacturing tools, particularly in the context of Industry 4.0 (Ramadan and Salah 2019). As a result, researchers concluded that Industry 4.0 and lean manufacturing complement each other conceptually, and then described how Industry 4.0 can support specific lean tools and practices to achieve lean targets (Mayr et al. 2018). To emphasize the interaction between lean manufacturing and Industry 4.0, Satoglu et al. (2018) attempted a methodology that guides Industry 4.0 within the context of lean manufacturing. Industry 4.0 ensures capturing real-time data from smart entities engaged in manufacturing processes for control and analysis for making quick and efficient decisions. Hence, a smart lean-based Industry 4.0 or lean-based smart factories framework called Dynamic Value Stream Mapping was introduced (Ramadan 2016). It was a real-time RFID-VSM system that interacted with processes, people, materials, and any other constraint relevant to the manufacturing situation. It enables managers to make the right decision at the right time depending on the real situation, where laborers will make changes to processing capacity, labor requirements, flow, and cell layout, to advance and plan for the future state. In the further section, the concept of LM and Industry 4.0 will be discussed in

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Table 14.3 Integrated Industry 4.0 and LM approaches Integrated system Mobile devices + automated production

IoT and machine Human to machine interaction GPS

RFID and smart sensors 3D printing

Big data analytics and blockchain Cloud computing

Application Completing checklist Getting, meeting, and tracking orders Real-time observing and optimizing production areas and environment Enables machine function without humans like sensors, networks, APIs, data Intelligent manufacturing with effective information transfer and feedback Package, delivery tracking, immediate response, inspections, resource control Monitor, diagnose, and control data Additive manufacturing of snacks and different food items with personalized nutrition QR scans

A large number of users can access data at a time like monitoring Modeling process design

Results Enhanced productivity and increased revenues for the companies

Reference Demirkol and Al-Futaih (2020), Morkos et al. (2012)

Improve operational efficiency and performance in manufacturing Better operations and faster production with zero defects Efficient and timely delivery

Cheruvu et al. (2019)

Improve and enhance manufacturing Fast and inexpensive manufacturing, reduced waste Compact and predictable methods Better traceability Reduced production cost High performance, productivity, reliable, and secure

Ma et al. (2019)

Demirkol and Al-Futaih (2020) Zou (2016) Muthurajan et al. (2021) Yiannas (2018)

Wang et al. (2017)

more detail. Integration of Industry 4.0 with lean management has found various applications in the food industry as presented in Table 14.3. High-quality sensor products, particularly viscosity sensors, hardness, surface finish, configuration, and color, are in high demand in industrial automation. And this is subject to stringent quality controls (Schütze et al. 2018). Smart technologies, when combined with factory systems and supported by accelerated intelligent sensor technology, have a profound impact on the performance of the industrial system, ultimately leading to high quality, flexibility, and productivity in manufacturing systems. A new RFID and wireless technology-enabled real-time VSM has been implemented in a food chain inventory and logistics, resulting in time savings, error reduction, waste reduction, and increased consumer trust. It also enables managers to make more accurate and timely decisions (Chen et al. 2021). Another study proposed a new framework called “VSM 4.0,” which combines traditional VSM with an innovative data collection and handling system (Meudt et al. 2017). In the next section, various real-time studies and examples are discussed where VSM has been established or taken care of in a lean and sustainable way.

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14.7.1 IoT and Lean Concepts in Food Industry IoT has a huge impact on the food industry and a combination of IoT and lean concepts can help in bringing positive changes soon. Following are the applications of the IoT in the food sector in Fig. 14.3. • Equipment Management IoT devices could alert or solve the problem of equipment maintenance before it becomes a major issue, saving time and money on routine maintenance. For example, in the hospitality industry, if a device is about to defer, the IoT’s signaling and reform features would notify the owner. This is critical because a disruption in the workflow results in a loss of market reputation as well as customer satisfaction. During these days, the income also decreases or changes. • Intelligent Refrigerators In the food sector, the refrigerator plays a crucial role from storing leftovers to fresh and perishable foods like meat, fruits, etc. Further, different food requires a different storage temperature. While fragrance and flavors should also not be lost with time. Thus, it is critical to certain refrigerator conditions. Here, IoT integration will not only improve food conditions but also preserve nutrients, thus, preventing wastage and time. • Reduce the energy consumption Food industries use hefty machines having huge power consumption. Thus, IoT will help in saving time and energy. For example, if a worker left an oven on after removing the food from it, then sensors installed could help in getting it off on its own or may alert to switch it off, thus help in maintaining cost.

Fig. 14.3 IoT and LM in food industry

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• Stock management The application-based IoT helps in tracking and managing stock in warehouses or restaurants. These applications are wireless systems that could be operated from mobiles or tablets and could alert the owner about the requirement or status of any item in stock so that advanced orders could be placed resulting in inefficient operation and reduced waste. • Oven designing Using a sensor, the temperature of the oven could be controlled, thus preventing overcooking, food burning, and oven damage. These sensors may notify the handler about the food condition in the oven like the temperature is optimum or not. In case of no response from the other end, the oven will switch off automatically. • Reduce logistics charges With the help of technologies, the real-time monitoring of the supply chain is achieved, hence resulting in reduced transportation and logistics costs. • Data analytics report To know about the direction of improvement for any industry, data needs to be tracked and market trends need to be considered. This indicates that customer feedback and demand records need to be maintained. Thus, here, the Internet of Things plays a critical role as it helps in tracking the data and records of the food chain. • Food safety regulations To meet the quality regulations setup for the food industry IoT enabled recorders are required to ensure premium food quality. IoT is the game-changer of every field. Even in recent times, the globe has sustained the food requirements of the population. This has become possible only because of the advent of IoT in the food sector. Though, privacy and complexity are the major drawbacks of IoT. These fields, however, can be covered with proper technical research and applications.

14.8

Various Lean Concepts Based Case Studies

14.8.1 Just in Time Nowadays, the fast-food service industry is expanding, becoming more competitive and diverse. The fast-food industry is dealing with two major issues. The first is that sales are slowing and operating costs are rising. Furthermore, customers are in high demand, and the services they receive are becoming increasingly selective. As a result, restaurant managers must understand how to maintain market profitability while providing more efficient and high-quality services to the target market. As fast-food restaurants are unique operational systems designed to provide customers with the fastest and most responsive services, they must focus on these subsystems (input, output, and process) and their interactions to improve efficiency,

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quality, and responsiveness. Just-in-time (JIT) inventory is the current operation trend. It is a method of providing supplies to customers “just in time,” as the name implies. It is also an inventory strategy that businesses use to improve efficiency and reduce waste by receiving goods only when they are needed in the production process, thus lowering inventory costs. JIT approach is used by numerous food and beverage enterprises due to the short life span of finished goods. However, the problem here is reliable real-time inventory management to prevent stock-out problems. Here, IoT-based inventory systems could be installed where the managers could be alerted before stock out using RFID tags, hence resolving stock out issues. This could be understood from the example presented in the next section. Another implementation of the JIT approach is adopted by the PICNIC system which delivers groceries fresh, hence reducing food loss problems found in groceries shops. Perishable goods (meat, apples, avocados) become part of a dynamic supply chain and are never stored for extended periods. This shortens the time between harvest and consumption, ensuring maximum freshness for all products ordered by the customers. Running a just-in-time supply chain, however, is a difficult task. It necessitates in-the-moment coordination. From production to receiving orders from suppliers, turnaround, and final delivery, every step must be coordinated. PICNIC prepares a forecasting model using machine learning and AI networking, which provides real-time visibility into supply and demand data and allows the company to purchase only what customers want. This precision means that, unlike traditional supermarkets, it does not keep an oversupply of goods, reducing waste throughout the supply chain. Furthermore, when there is an oversupply during the harvest season, the system is so adaptable and responsive that it collaborates with suppliers to distribute the excess by offering deals to the customers. Additionally, the system has well-equipped facilities to ensure freshness, well-designed loading and unloading resulting in smooth flow throughout the supply chain. In this way, the online store maximizes operation efficiency with precision and reduces food waste. McDonald’s is one of the most well-known franchise fast-food restaurants in the world. In its business operations, McDonald’s Fast-Food Restaurant has used the just-in-time system. The just-in-time system has assisted McDonald’s restaurant in lowering inventory costs and reducing waste. When using just-in-time systems, McDonald’s can produce burgers in 90 seconds. McDonald’s products are available pre-cooked. Customers can order the product and it will be manufactured. JIT is a new technology that has been presented as a challenge to McDonald’s. McDonald’s success mantra, however, is high flexibility or adaptability. McDonald’s uses a make-to-order approach for production and order fulfillment because customer satisfaction is important to the company. And a customer in any restaurant expects quick service that is also of high quality and flavor. As a result, McDonald’s considers the length of time it takes to deliver the order from the time the order is placed; this is referred to as lead time. The greater the lead, the less satisfied the customer, resulting in the customer not returning. Conversely, McDonald’s is a big restaurant that has a billion customers and employees. Therefore, the production speed is needed to fulfill the customer’s need and they are the main choice of customers because of the service they provide

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to customers which actually attracts consumers to buy their product. Also, being a brand, it maintains the quality of the burger using high techniques in processing, resulting in better productivity. Just in time has helped McDonald’s to become more sustainable in the fast-food industry.

14.8.2 Kaizen The word itself is Japanese and a combination of two words. “Kai” refers to change, and “zen” refers to good and better. As a result, the word “kaizen” can be translated as “good change” or “change for the better.” Kaizen methodology is a method of continuously improving work, work environment, and employees. The method’s fundamental core revolves around employee collaboration and communication, as well as the work they do together. Its primary goal is to reduce potential risks and problems, increase productivity, instill a positive attitude in the workplace, and innovate. Building a Kaizen culture in an organization is a difficult task. Companies that invest time and effort into studying and promoting it to their workforce, however, reap a plethora of benefits that can only be obtained by assiduously incorporating its tenets into daily work. And it is about more than just improving quality and lowering waste. Kaizen, when properly implemented, can have a positive impact on every level of your organization. Here are its most important benefits: • Reduces waste like less downtime, less unnecessary movement, lead time, etc. • Improves operation efficiency with higher equipment and resource utilization resulting in higher productivity. • Predictable production will result in higher delivery rates with higher customer satisfaction. • Happier, more productive employees as all staff are openly contributing to continuous improvement. • Ensures an open system for improvements. • Successful both in the short and long term.

14.8.3 Nestlé and Kaizen One of the most important industries focusing on implementing and applying the Kaizen philosophy is the food industry. Nestlé is one of the world’s largest corporations, with products in a wide range of industries. Nestlé’s main goal in lean production is to reduce waste. Because their products can have an impact on the health of those who use (or consume) them, it focuses on reducing waste. This waste consists of both food and plastic. Nestlé promises that by 2025, all of its packaging will be recyclable and reusable. To accomplish this, the company invested $2 billion.

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It also donates a large portion of the food produced in its factories to animal shelters and people in need.

14.8.4 5S Methodology The 5S strategy objective is to maintain excellent working conditions with continuous improvement in a sequence throughout the processing, storage, and organization. It helps the organization in meeting international standards with little effort and cost. Even though it is a simple system, however, its implementation is not easy, the reason being it should be accepted by employees in terms of attitude, commitment, and involvement along with top managers. The 5S and the explanation of the acronyms are presented below in Table 14.4. When companies achieve the first 3S, they face the most difficult aspect of 5S, which is trying to keep workplaces in excellent condition over time. To accomplish this, businesses must standardize the rules defined in the preceding steps, which should be done in collaboration with employees because they are the most knowledgeable about their workplaces, equipment, and the most common problems/ anomalies (Lopes et al. 2015). This step should ensure that all rules are followed so that organization, storage, and regular cleaning become habits, thereby preventing the recurrence of previous bad habits (Patel and Thakkar 2014). In this regard, regular inspections need to be performed and employees must be trained about all aspects of the methodology to ensure they meet their responsibilities.

Table 14.4 5S Principles Japanese and Indian meaning Seiri (arranging properly) Seiton (order lineness) Seiso (cleanliness)

Seiketsu (standardize) Shitsuke (discipline or sustain)

Role Distinguishing required from non-required and eliminating the non-required Keeping the documents in places that are easy and quick to trace as and when required Performed in parallel with organization and order Ensures the workplace and machines are cleaned and are operating well To ensure the long-term cleanliness of the workspace some sets of rules are established Developing a constant habit of maintaining the establishing procedures

LM application Higher possible utilization of workspace Product diversification Reduced cost Strict delivery schedules Improved safety High quality

References Kumar et al. (2007) Lopes et al. (2015) Lopes et al. (2015)

Patel and Thakkar (2014) Patel and Thakkar (2014)

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With the successful 5S implementation following advantages are achieved by the industry: • • • • • • • •

efficient operation; organized, clean, productive, and safe; improvement of working conditions and employees value; a better view of problems; an embodiment of daily activities by employees; increased productivity, flexibility, quality, safety, and motivation of employees; cost savings, unproductive time, space, and movements; and cost savings related to failures and breaks.

In the next section, we will discuss the SMED implementation in another company with its effect on production.

14.8.5 SMED Single-minute exchange of die (SMED) aims at systematically reducing changeover times, ideally to single-digit minutes. Changeover time is the period between two good products coming out of a machine where the second product is from a different production order—activities performed during this time are usually non-value adding. Changeover can be divided into three main periods: (a) run-down or clean-up, removal of material remaining from previous production and cleaning; (b) setup, physically converting machines to enable producing new products; and (c) run-up or start-up when steady-state manufacturing is re-established, meeting required productivity and quality rates, which typically includes adjustments and quality checking. Rapid changeover is critical for reducing lot sizes and thus improving flow and manufacturing flexibility. These are important aspects in business because they measure efficiency and competitiveness and are an effective way to reduce costs, which are the main benefits of SMED (Lopes et al. 2015). Furthermore, the company lacks standards or documents that explain how changeovers should occur, operator training, variation in operations sequence and working methods, no coordination, insufficient tools, and equipment calibration and adjustments. As change over time has been divided further into three main periods, thus, SMED has been extended to address these periods (Ferradás and Salonitis 2013). Further, SMED focuses on improvements in the organization as well as manufacturing equipment design (Cakmakci 2008). SMED has been successfully implemented in various industries; however, in the food and beverage industry it has been limitedly explored. The three main stages of the SMED methodology are presented in Table 14.5.

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Table 14.5 SMED in food industry Stage Separate

Convert

Streamline

Description Crucial step Classify activities as external or internal based on the possibility of performance in-house setup Video recordings and routing diagrams can be used Classify the change over time as well Analyze the classified internal activities for any error Attempt the internal activities and convert them to external using equipment design improvements like, standardizing tools and using intermediary jigs All change-over aspects must be streamlined and simplified Systematically improve all operations by reducing adjustments and eliminating. Implement parallel operation and use tools efficiently

Reference Ferradás and Salonitis (2013)

Lopes et al. (2015)

Ferradás and Salonitis (2013)

All the three steps must be evaluated to ensure most time-improving and costefficient measures are employed. The SMED implementation in the bottle manufacturing industry presented the reduced change over time loss like earlier it used to perform 30 steps which on SMED implementation came down to 20 resulting in 23% improvement. From the above case study, it could be observed that with the implementation of SMED in the food industry not only productivity will improve but also production flexibility, employee engagement, motivation, and continuous improvement will be observed which are critical for successful lean implementation.

14.9

Future Road Map

The food loss and waste along the supply chain are observed either in the form of discard or nutrient loss. Discarded food is comprised of inappropriate processing, overproduction, and defects as per lean principles. In concurrence with the abovesaid defects, the non-conformance to standards is also explicitly highlighted in the food industry like size, weight, shape breakages, and shelf life of the product (De Steur et al. 2016). Additionally, if equipment and operation are not standardized it may result in loss and wastage during processing (Papargyropoulou et al. 2014), thus pointing to the need for process controls not only in processing but also throughout the supply chain to achieve holistic waste reduction (Mena et al. 2014). Excess food stock be it raw or prepared due to poor demand forecasting is a problem of both the world. Lean manufacturing, the just-in-time principle facilitates production based on demand, hence preventing overproduction and overstocking (De Steur et al. 2016). Critical awareness regarding consumer behavior wants and choices beforehand could help food chain actors in predicting target markets. Moreover, instead of discarding surplus food management should be followed like donating food, thus

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contributing to a noble cause of food insecurity (Garrone et al. 2014). Similarly, there should be an emphasis on food waste causes and reusing approaches among consumers, employees of the food industry, thus requiring considerations for the consumption level as part of the supply chain. Processing techniques may have a profound effect on the nutrient content of food. For example, overbaking and pasteurization could result in loss of essential heatlabile micronutrients such as thiamine, vitamin A and C. Besides heat, oxygen and light could also result in nutrient loss, rancidity problems, etc. Even cutting, peeling, and milling followed by washing result in losses (Atungulu and Pan 2014). All the above-mentioned points imply that VSM is effective in identifying both nutrient loss and waste. However, innovative strategies and methodologies are needed to be developed that integrate both types of losses along the supply chain, as the current evidence shows that both kinds of losses could be attributed to similar causes. Further, single food products and food companies are considered which could be problematic for generalizing the concept. Further, today’s need is for qualitative and quantitative approaches. It is important to include supply chain actors in SVSM approaches. Furthermore, performance indicators need to be established with full quantification as observed in the case of the coffee sector case study. So that, lean implementation effects could be observed. There have been few empirical studies conducted from farm to fork. Although previous studies have mentioned the need to address this issue holistically, they have failed to move from posturing to application. There is a need for a multi-stakeholder approach and further highlight the mitigation potential of SVSM. Another concern is establishing possible links of nutritional value with food loss.

14.10

Conclusions

Lean implementation in the agri-food industry is still growing; the potential of SVSM, VSM, Kaizen, JIT, 5S, and SMED has been demonstrated in this chapter. Regardless of challenges, the integration of IoT and lean management has been shown to improve the visibility of the entire value stream and consequently creates an opening for information sharing required for an integrated food system. Lean and green practices integrally improve production efficiency by reducing production costs and waste which could favor the vulnerable and hungry population. Further, future scientific research could be extended to the application knowledge of SVSM, lean principles with IoT into an unexplored and complementary approach, with the potential to sustainably enhance production with minimized food wastage, better operation efficiency, full employee involvement, and betterment.

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