• ISSN: 2148-2225 (online)

Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

A Literature Review on Machine Learning in The Food Industry

bib

Furkan Açıkgöz

Leyla Zeynep Verçin

Gamze Erdoğan


Abstract

Machine Learning (ML) has become widespread in the food industry and can be seen as a great opportunity to deal with the various challenges of the field both in the present and near future. In this paper, we analyzed 91 research studies that used at least two ML algorithms and compared them in terms of various performance metrics. China and USA are the leading countries with the most published studies. We discovered that Support Vector Machine (SVM) and Random Forest outperformed other ML algorithms, and accuracy is the most used performance metric.

Keywords: Classification, Food Industry, Machine Learning, Support Vector Machine

Jel Classification: C46

Suggested citation

Açıkgöz, F., Verçin, L. Z. & Erdoğan, G. (). A Literature Review on Machine Learning in The Food Industry. Alphanumeric Journal, 11(2), 207-222. https://doi.org/10.17093/alphanumeric.1214699

References

  • Bhagya Raj, G. V. S., & Dash, K. K. (2022). Comprehensive study on applications of artificial neural network in food process modeling. Critical Reviews in Food Science and Nutrition, 62(10), 2756–2783. https://doi.org/10.1080/10408398.2020.1858398
  • Boehmke, B., & Greenwell, B. (2020). Hands-On Machine Learning with R. CRC Press - Taylor & Francis Group.
  • Bonifazi, G., Capobianco, G., Gasbarrone, R., & Serranti, S. (2021). Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images. Food Control, 130, 108202. https://doi.org/10.1016/j.foodcont.2021.108202
  • Cas Proffitt. (2017). Top 10 Artificial Intelligence Companies Disrupting The Food Industry. Disruptor Daily. https://www.disruptordaily.com/top-10-artificial-intelligence-disrupting-food-industry/
  • Castro, W., Oblitas, J., De-La-Torre, M., Cotrina, C., Bazan, K., & Avila-George, H. (2019). Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces. IEEE Access, 7, 27389–27400. https://doi.org/10.1109/ACCESS.2019.2898223
  • Cho, B.-H., Koyama, K., Olivares Díaz, E., & Koseki, S. (2020). Determination of "Hass" Avocado Ripeness During Storage Based on Smartphone Image and Machine Learning Model. Food and Bioprocess Technology, 13(9), 1579–1587. https://doi.org/10.1007/s11947-020-02494-x
  • Cui, H., Huang, D., Fang, Y., Liu, L., & Huang, C. (2018). Webshell Detection Based on Random Forest–Gradient Boosting Decision Tree Algorithm. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 153–160. https://doi.org/10.1109/DSC.2018.00030
  • De-la-Torre, M., Zatarain, O., Avila-George, H., Muñoz, M., Oblitas, J., Lozada, R., Mejía, J., & Castro, W. (2019). Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits. Processes, 7(12), 928. https://doi.org/10.3390/pr7120928
  • Erban, A., Fehrle, I., Martinez-Seidel, F., Brigante, F., Más, A. L., Baroni, V., Wunderlin, D., & Kopka, J. (2019). Discovery of food identity markers by metabolomics and machine learning technology. Scientific Reports, 9(1), 9697. https://doi.org/10.1038/s41598-019-46113-y
  • Insights, S. (2022, January 31). 5 Top AI Startups impacting the Food Industry. StartUs Insights. https://www.startus-insights.com/innovators-guide/ai-startups-impacting-the-food-industry/
  • 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.2019.03.063
  • Kovalenko, O. (2021, July 23). Machine Learning and AI in the Food Industry. SPD Group. https://spd.group/machine-learning/machine-learning-and-ai-in-food-industry/
  • Kwasek, M. (2012). Threats to Food Security and Common Agricultural Policy. Economics of Agriculture, 59(4), 701–713.
  • Lantz, B. (2015). Machine learning with R: Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R (Second edition). Packt Publishing.
  • Lewis, N. D. (2017). Machine learning made easy with R: An intuitive step by step blueprint for beginners. AusCov.
  • Li, H., Lee, W. S., & Wang, K. (2014). Identifying blueberry fruit of different growth stages using natural outdoor color images. Computers and Electronics in Agriculture, 106, 91–101. https://doi.org/10.1016/j.compag.2014.05.015
  • Martinho, V. J. P. D., Cunha, C. A. D. S., Pato, M. L., Costa, P. J. L., Sánchez-Carreira, M. C., Georgantzís, N., Rodrigues, R. N., & Coronado, F. (2022). Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0. Applied Sciences, 12(22), 11828. https://doi.org/10.3390/app122211828
  • Miles, S., & Scaife, V. (2003). Optimistic bias and food. Nutrition Research Reviews, 16(01), 3. https://doi.org/10.1079/NRR200249
  • Min, W., Jiang, S., & Jain, R. (2020). Food Recommendation: Framework, Existing Solutions, and Challenges. IEEE Transactions on Multimedia, 22(10), 2659–2671. https://doi.org/10.1109/TMM.2019.2958761
  • Moubarac, J.-C., Parra, D. C., Cannon, G., & Monteiro, C. A. (2014). Food Classification Systems Based on Food Processing: Significance and Implications for Policies and Actions: A Systematic Literature Review and Assessment. Current Obesity Reports, 3(2), 256–272. https://doi.org/10.1007/s13679-014-0092-0
  • Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11(7), 1463–1482. https://doi.org/10.1007/s13042-019-01050-0
  • Nyce, C. (2007). Predictive Analytics White Paper (Insurance Institute of America, 9-10.). American Institute for CPCU - Insurance Institute of America. https://www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf
  • Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56, 117–132. https://doi.org/10.1016/j.jmsy.2020.05.013
  • Rady, A., & Adedeji, A. A. (2020). Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats. Food Analytical Methods, 13(4), 970–981. https://doi.org/10.1007/s12161-020-01719-1
  • Ramasubramanian, K., & Singh, A. (2019). Machine Learning Using R: With Time Series and Industry-Based Use Cases in R. Apress. https://doi.org/10.1007/978-1-4842-4215-5
  • Ribeiro, J., Neves, J., Sanchez, J., Delgado, M., Machado, J., & Novais, P. (2009). Wine Vinification prediction using Data Mining tools. Computing and Computational Intelligence. WSEAS, 78–85.
  • Sadiku, M. N. O., Musa, S. M., Ashaolu, T. J., & Roy G. Perry College of Engineering, Prairie View AandM University, Prairie View, Texas, United States. (2019). Food Industry: An Introduction. International Journal of Trend in Scientific Research and Development, Volume-3(Issue-4), 128–130. https://doi.org/10.31142/ijtsrd23638
  • Saha, D., & Manickavasagan, A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28–44. https://doi.org/10.1016/j.crfs.2021.01.002
  • Sakinah Shaeeali, N., Mohamed, A., & Mutalib, S. (2020). Customer reviews analytics on food delivery services in social media: A review. IAES International Journal of Artificial Intelligence (IJ-AI), 9(4), 691. https://doi.org/10.11591/ijai.v9.i4.pp691-699
  • Say2eat. (2017). Chatbots are Here to Stay. https://chatbotnewsdaily.com/chatbots-are-here-to-stay-46195401b734
  • Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing. Advanced Engineering Informatics, 45, 101101. https://doi.org/10.1016/j.aei.2020.101101
  • Sood, S., & Singh, H. (2021). Computer Vision and Machine Learning based approaches for Food Security: A Review. Multimedia Tools and Applications, 80(18), 27973–27999. https://doi.org/10.1007/s11042-021-11036-2
  • Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080
  • Tongcham, P., Supa, P., Pornwongthong, P., & Prasitmeeboon, P. (2020). Mushroom spawn quality classification with machine learning. Computers and Electronics in Agriculture, 179, 105865. https://doi.org/10.1016/j.compag.2020.105865
  • United Nations. (2022). Food Loss and Waste Reduction. United Nations; United Nations. https://www.un.org/en/observances/end-food-waste-day
  • Vidyarthi, S. K., Tiwari, R., & Singh, S. K. (2020). Stack ensembled model to measure size and mass of almond kernels. Journal of Food Process Engineering, 43(4). https://doi.org/10.1111/jfpe.13374
  • WEF. (2019). Innovation with a Purpose: Improving Traceability in Food Value Chains through Technology Innovations (System Initiative on Shaping the Future of Food). World Economic Forum. http://www3.weforum.org/docs/WEF_Traceability_in_food_value_chains_Digital.pdf
  • WEF. (2020). Future of Jobs Report 2020. World Economic Forum. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf
  • WizataTeam. (2021). How the Food Industry Can Benefit From AI. https://www.wizata.com/knowledge-base/how-the-food-industry-can-benefit-from-ai
  • Zhou, L., Zhang, C., Liu, F., Qiu, Z., & He, Y. (2019). Application of Deep Learning in Food: A Review. Comprehensive Reviews in Food Science and Food Safety, 18(6), 1793–1811. https://doi.org/10.1111/1541-4337.12492

Volume 11, Issue 2, 2023

2023.11.02.MIS.04

alphanumeric journal

Volume 11, Issue 2, 2023

Pages 207-222

Received: Dec. 5, 2022

Accepted: Sept. 14, 2023

Published: Dec. 31, 2023

Full Text [747.3 KB]

2023 Açıkgöz, F., Verçin, LZ., Erdoğan, G.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Creative Commons Attribution licence

scan QR code to access this article from your mobile device

Contact Us

Faculty of Transportation and Logistics, Istanbul University
Beyazit Campus 34452 Fatih/Istanbul/TURKEY

Bahadır Fatih Yıldırım, Ph.D.
editor@alphanumericjournal.com
+ 90 (212) 440 00 00 - 13219

alphanumeric journal

alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.