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2023 | OriginalPaper | Chapter

Artificial Intelligence and Data Science in Food Processing Industry

Authors : Mohit Malik, Vijay Kumar Gahlawat, Rahul S. Mor, Shekhar Agnihotri, Anupama Panghal, Kumar Rahul, Neela Emanuel

Published in: Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance

Publisher: Springer International Publishing

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Abstract

Digital transformations and industrial automation help improve productivity and operational efficiency at a mass scale without compromising quality and consistency in the food processing industry. This chapter discusses how artificial intelligence (AI) can effectively enhance food hygiene, safety and quality, efficient production and supply chain by efficient decision-making, food waste management, and smart sorting and packaging solution through economic resource utilization by reducing errors and saving capital investments. The convergence of data science and AI can further improve the delivery of items at food outlets, boosting on-demand production and predicting sales performance through algorithms. It can drastically improve shelf life, packaging, food safety with a more translucent supply chain, and quality control by identifying customer needs. Automation in the food industry will monitor each processing stage and thus improve its scale-up, inventory predictions, and supply chain stream. This helps in cost reduction, improved accuracy, and time-saving in a large-scale operation.

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Akey, S., & Sharma R. R. (2020). Real time monitoring and fire detection using internet of things and cloud based Drones. Journal of Soft Computing Paradigm 2(3), 168–174. https://doi.org/10.36548/jscp.2020.3.004

Basar, A., Smys, S., & Wang, H. (2021). 5G network simulation in smart citiesusing neural network algorithm. Journal of Artificial Intelligence and Capsule Networks, 3(1), 43–52. https://doi.org/10.36548/jaicn.2021.1.004CrossRef

Bera, S. (2021). An application of operational analytics: For predicting sales revenue of restaurant. In S. K. Das, S. P. Das, N. Dey, & A.-E. Hassanien (Eds.), Machine learning algorithms for industrial applications (Vol. 907, pp. 209–235). https://doi.org/10.1007/978-3-030-50641-4_13CrossRef

Bhattacharyya, S. S., Maitra, D., & Das, D. (2021). Study of adoption and absorption of emerging technologies for smart supplychain management: A dynamic capabilities perspective. International Journal of Applied Logistics (IJAL), 11(2), 14–54. https://doi.org/10.4018/ijal.2021070102CrossRef

Cao, T. D., & Truong, H. L. (2016). Analyzing and conceptualizing monitoring and analytics as a service forgrain warehouses. In Recent developments in intelligent information and database systems, studies in computational intelligence (vol. 642, pp. 161–171). https://doi.org/10.1007/978-3-319-31277-4_14

Castillo, O., & Meliif, P. (1995). Automated quality control in the food industry combining artificial intelligence techniques with fractal theory. WIT Transactions on Information and Communication Technologies. https://doi.org/10.2495/AI950121

Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2018). A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Applied Soft Computing Journal, 69, 33–59. https://doi.org/10.1016/j.asoc.2018.04.022CrossRef

Dadi, K., Varoquaux, G., Houenou, J., Bzdok, D., Thirion, B., & Engemann, D. (2021). Population modeling with machine learning can enhance measures of mental health. GigaScience, 10(10), giab071. https://doi.org/10.1093/gigascience/giab071

Dehghan-Dehnavi, S., Fotuhi-Firuzabad, M., Moeini-Aghtaie, M., Dehghanian, P., & Wang, F. (2020). Estimating participation abilities of industrial customers in demand response programs: A two-level decision-making tree analysis. In 2020 IEEE/IAS 56th industrialand commercial powersystems technical conference (I & CPS), 1–8. https://doi.org/10.1109/ICPS48389.2020.9176817

DiRosa, A. R., Leone, F., Cheli, F., & Chiofalo, V. (2017). Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review. Journal of Food Engineering, 210, 62–75. https://doi.org/10.1016/j.jfoodeng.2017.04.024CrossRef

DiSalvo, S. (2020). How data science plays a role in the food industry. Master’s in Data Science. https://www.mastersindatascience.org/resources/food/. Accessed on 03 Feb 2022).

Donepudi, P. K. (2014). Technology growth in shipping industry: An overview. American Journal of Tradeand Policy, 1(3), 137–142. https://doi.org/10.18034/ajtp.v1i3.503CrossRef

Fedorova, E., Darbasov, V., & Okhlopkov, M. (2020). The role of agricultural economists in study on problems related to regional food safety. E3S Web of Conferences, 176. https://doi.org/10.1051/e3sconf/202017605011

Filimonau, V., Todorova, E., Mzembe, A., Sauer, L., & Yankholmes, A. (2020). A comparativestudy of foodwastemanagement in full service restaurants of the United Kingdom and the Netherlands. Journal of Cleaner Production, 258. https://doi.org/10.1016/j.jclepro.2020.120775

Flemming, F., & Balthasar, D. (2019). An introduction to AI in sorting technologies. Recycling Today. https://www.recyclingtoday.com/article/an-introduction-to-ai-in-recycling-sorting-technologies/. Accessed on 03 Feb 2022.

Goyache, F., Bahamonde, A., Alonso, J., Lopez, S., del Coz, J. J., Quevedo, J. R., Ranilla, J., Luaces, O., Alvarez, I., Royo, L. J., & Diez, J. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370–381. https://doi.org/10.1016/S0924-2244(02)00010-9

Hand, A. (2020). Artificial intelligence advances food safety. In: profood world. https://www.profoodworld.com/processing-equipment/inspection/article/21195038/artificial-intelligence-advances-food-safety#:~:text=Landing%20AI%20is%20helping%20food,evaluate%20issues%20around%20food%20safety.&text=Machine%20vision%20has%20long%20found,working%2024%2F7%20without%20fatigue. Accessed 28 Jan 2022

Jiménez-Carvelo, A. M., González-Casado, A., Bagur-González, M. G., & Cuadros-Rodríguez, L. (2019). Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review. Food Research International, 122, 25–39. https://doi.org/10.1016/j.foodres.2019.03.063CrossRef

Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033CrossRef

Keeble, M., Adams, J., Sacks, G., Vanderlee, L., White, C. M., Hammond, D., & Burgoine, T. (2020). Use of online food delivery services to order food prepared away-from-home and associated socio demographic characteristics: A cross-sectional, multi-country analysis. International Journal of Environmental Research and Public Health, 17(14), 1–17. https://doi.org/10.3390/ijerph17145190CrossRef

Kottilingam. (2020). Emotional wellbeing assessment for elderly using multi-language Robot interface. Journal of Information Technology and Digital World, 02(01), 1–10. https://doi.org/10.36548/jitdw.2020.1.001

Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021, 4535567. https://doi.org/10.1155/2021/4535567CrossRef

Kumar, V., Balasubramaniam, S., & Tharagesh, S. S. R. (2020). An autonomous food wastage control warehouse: Distributed ledger and machine learning based approach. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). https://doi.org/10.1109/ICCCNT49239.2020.9225525.

Lillford, P., & Hermansson, A. M. (2021). Global missions and the critical needs of food science and technology. Trends in Food Science and Technology, 111, 800–811. https://doi.org/10.1016/j.tifs.2020.04.009CrossRef

Liu, Y., Eckert, C., Yannou-Le Bris, G., & Petit, G. (2019). A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agri food value chain. Computers and Industrial Engineering, 127, 196–212. https://doi.org/10.1016/j.cie.2018.12.022CrossRef

Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 1. https://doi.org/10.1109/JIOT.2020.2998584

Mohamed, R. R., Yaacob, R., Mohamed, M. A., Dir, T. M., & Rahim, F. A. (2018). Food freshness using electronic nose and its classification method: A review. International Journal of Engineering and Technology, 7, 49–53. https://doi.org/10.14419/IJET.V7I3.28.20964CrossRef

Monteiro, J., & Barata, J. (2021). Artificial intelligence in extended agri-food supply chain: A short review based on bibliometric analysis. Procedia Computer Science, 192, 3020–3029. https://doi.org/10.1016/j.procs.2021.09.074CrossRef

Mor, R. S., Kumar, D., Singh, A., & Neethu, K. (2022). Robotics and automation for agri-food 4.0: Innovation and challenges. In Agri-food 4.0: Innovations, challenges and strategies.

Nagaraju, T., & Shubhamangala, B. R. (2020). Artificial intelligence powered smart refrigerator to arrest food wastage. In 3rd international conference on innovative computing and com-munication (ICICC-2020). https://doi.org/10.2139/ssrn.3565256.

Nychas, G.-J., Sims, E., Tsakanikas, P., & Mohareb, F. (2021). Data science in the food industry. Annual Review of Biomedical Data Science, 4(1), 341–367. https://doi.org/10.1146/annurev-biodatasci-020221-123602CrossRef

Olan, F., Liu, S., Suklan, J., Jayawickrama, U., & Arakpogun, E. (2021). The role of artificial intelligence networks in sustainable supply chain finance for food and drink industry. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1915510

Pandian, A. P. (2019). Artificial intelligence application in smart ware housing environment forautomated logistics. Journal of Artificial Intelligence and Capsule Networks, 2019(2), 63–72. https://doi.org/10.36548/jaicn.2019.2.002CrossRef

Qazi, M. W., de Sousa, I. G., Nunes, M. C., & Raymundo, A. (2022). Improving the nutritional, structural, and sensory properties of gluten-free bread with different species of microalgae. Food, 11(3), 397. https://doi.org/10.3390/foods11030397CrossRef

Rary, E., Anderson, S. M., Philbrick, B. D., Suresh, T., & Burton, J. (2020). Smart sanitation -biosensors as a public health tool in sanitation infrastructure. International Journal of Environmental Research and Public Health, 17(14), 1–14. https://doi.org/10.3390/ijerph17145146CrossRef

Sanaeifar, A., Bakhshipour, A., & de La Guardia, M. (2016). Prediction of banana quality indices from color features using support vector regression. Talanta, 148, 54–61. https://doi.org/10.1016/j.talanta.2015.10.073CrossRef

Schaller, E., Bosset, J. O., & Escher, F. (1998). “Electronic noses” and their application to food. LWT – Food Science and Technology, 31(4), 305–316. https://doi.org/10.1006/fstl.1998.0376CrossRef

Schmidt, R., & Piotter, H. (2020). The hygienic/sanitary design of food and beverage processing equipment. In Demirci A., Feng H., Krishnamurthy K. (Eds.), Food safety engineering (pp. 267–332). Food Engineering Series. https://doi.org/10.1007/978-3-030-42660-6_12.

Sharma, S., & Patil, S. V. (2015). Key indicators of rice production and consumption, correlation between them and supply-demand prediction. International Journal of Productivity and Performance Management, 64(8), 1113–1137. https://doi.org/10.1108/IJPPM-06-2014-0088CrossRef

Sharma, T. K., & Abraham, A. (2020). Artificial bee colony with enhanced food locations for solving mechanical engineering design problems. Journal of Ambient Intelligence and Humanized Computing, 11(1), 267–290. https://doi.org/10.1007/s12652-019-01265-7CrossRef

Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R. S., Malik, M. (2021) Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics, 5(4), 66. https://doi.org/10.3390/logistics5040066

Sivaganesan, D. (2021). Performance estimation of sustainable smart farming with blockchain technology. IRO Journal on Sustainable Wireless Systems, 3(2), 97–106. https://doi.org/10.36548/jsws.2021.2.004CrossRef

Soltani-Fesaghandis, G., & Pooya, A. (2018). Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry. International Food and Agribusiness Management Review, 21(7), 847–864. https://doi.org/10.22434/IFAMR2017.0033CrossRef

Sophy, J. (2018). Restaurant owners learn 60% of consumers will judge them on food quality. Technology Trends. https://smallbiztrends.com/2018/02/restaurant-experience-statistics.html. Accessed on 04 Feb 2022.

SPD Group. (2021). Machine learning and AI in food industry: Solutions and potential. https://spd.group/machine-learning/machine-learning-and-ai-in-food-industry/. Accessed on 03 Feb 2022.

Sun, Q., Zhang, M., & Mujumdar, A. S. (2019). Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition, 59(14), 2258–2275. https://doi.org/10.1080/10408398.2018.1446900

Tan, J., & Xu, J. (2020). Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 4, 104–115. https://doi.org/10.1016/j.aiia.2020.06.003CrossRef

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009CrossRef

Tripathi, S., Shukla, S., Attrey, S., Agrawal, A., & Bhadoria, V. (2020). Smart industrial packaging and sorting system. In Kapur P. K., Singh O., Khatri S. K., Verma A. K. (Eds.), Strategic system assurance and business analytics. Asset analytics (performance and safety management). https://doi.org/10.1007/978-981-15-3647-2_18.

Trab, S., Bajic, E., Zouinkhi, A., Abdelkrim, M. N., & Chekir, H. (2018). RFID IoT-enabled warehouse for safety management using product class-based storage and potential fields methods. International Journal of Embedded Systems, 10(1), 71–88. https://doi.org/10.1504/IJES.2018.089436

Tyagi, N., Khan, R., Chauhan, N., Singhal, A., & Ojha, J. (2021). E-rickshaws management for small scale farmers using big data-apache spark. IOP Conference Series: Materials Science and Engineering, 1022(1). https://doi.org/10.1088/1757-899X/1022/1/012023

Vadlamudi, S. (2018). Agri-food system and artificial intelligence: Reconsidering imperishability. Asian Journal of Applied Science and Engineering, 7, 33–42.

Wang, X., Puri, V. M., & Demirci, A. (2020). Equipment cleaning, sanitation, and maintenance. Food Engineering Series, 333-353. https://doi.org/10.1007/978-3-030-42660-6_13

Wardah, S., Djatna, T., & Yani, M. (2020). New product development in coconut-based agro-industry: Current research progress and challenges. IOP Conference Series: Earth and Environmental Science, 472(1). https://doi.org/10.1088/1755-1315/472/1/012053

Wilts, H., Garcia, B. R., Garlito, R. G., Gómez, L. S., & Prieto, E. G. (2021). Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy. Resources, 10(4). https://doi.org/10.3390/resources10040028

Yost, E., & Cheng, Y. (2021). Customers’ risk perception and dine-out motivation during a pandemic: Insight for the restaurant industry. International Journal of Hospitality Management, 95, 102889. https://doi.org/10.1016/j.ijhm.2021.102889CrossRef

Yu, X., Lin, Y., & Wu, H. (2020). Targeted next-generation sequencing identifies separate causes of hearing loss in one deaf family and variable clinical manifestations for the p.R161C mutation in SOX10. Neural Plasticity, 2020, https://doi.org/10.1155/2020/8860837.

Title: Artificial Intelligence and Data Science in Food Processing Industry
Authors: Mohit Malik
Vijay Kumar Gahlawat
Rahul S. Mor
Shekhar Agnihotri
Anupama Panghal
Kumar Rahul
Neela Emanuel
Publisher: Springer International Publishing
Book: Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance
Print ISBN: 978-3-031-19710-9

Electronic ISBN: 978-3-031-19711-6

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-19711-6_11

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