Artificial Intelligence and Machine Learning in Food Science: Conceptual Foundations and Application Areas

Year 2025, Volume: 8 Issue: 1, 196 - 216, 31.07.2025

Raciye Meral

Abstract

This study provides a comprehensive overview of the current and potential applications of artificial intelligence (AI) and machine learning (ML) techniques in the field of food science and technology. It begins with an explanation of key concepts related to machine learning, including learning types (supervised, unsupervised, and reinforcement learning) and commonly used traditional algorithms such as decision trees, support vector machines, artificial neural networks, and k-nearest neighbors. The study then explores how various subfields of AI—particularly natural language processing (NLP), image processing, and Internet of Things (IoT)-based monitoring systems—are applied in food science. Representative applications are presented in areas such as product labeling, content analysis, sentiment analysis on social media, product recognition, spoilage detection, and food safety monitoring systems.

A literature-based review demonstrates that AI-powered methods are effectively used in critical tasks including quality control, shelf life prediction, sensory evaluation, consumer preference analysis, and early detection of microbial or chemical spoilage. Moreover, these technologies contribute to the development of data-driven decision support systems that can enhance operational efficiency across the food supply chain.

Despite certain limitations such as data heterogeneity, lack of interpretability, and model reliability, the integration of AI into food systems is assessed as a transformative process. It is expected to pave the way for smarter, more sustainable, traceable, and consumer-oriented food systems in the near future.

Keywords

Artificial intelligence, machine learning, deep learning, food science

GIDA BİLİMİNDE YAPAY ZEKÂ UYGULAMALARI: MAKİNE ÖĞRENMESİ TABANLI YAKLAŞIMLAR VE GÜNCEL EĞİLİMLER

Abstract

Bu çalışmada, yapay zekâ ve makine öğrenmesi tekniklerinin gıda bilimi ve teknolojisi alanındaki güncel ve potansiyel uygulamaları kapsamlı bir şekilde ele alınmıştır. Çalışmanın başlangıcında, makine öğrenmesine dair temel kavramlar, öğrenme türleri (denetimli, denetimsiz ve pekiştirmeli öğrenme) ve bu alanlarda yaygın olarak kullanılan geleneksel algoritmalar (karar ağaçları, destek vektör makineleri, yapay sinir ağları, k-en yakın komşu vb.) ayrıntılı şekilde açıklanmıştır. Devamında, doğal dil işleme (NLP), görüntü işleme ve nesnelerin interneti (IoT) tabanlı sistemler gibi yapay zekânın alt bileşenlerinin gıda bilimi alanında nasıl kullanıldığına dair örnek uygulamalara yer verilmiştir. Bu kapsamda; etiketleme, içerik analizi, sosyal medya duygu analizi, ürün tanımlama, bozulma tespiti ve gıda güvenliği izleme sistemleri gibi pek çok uygulama senaryosu incelenmiştir.

Literatür incelemesine dayalı olarak, yapay zekâ tabanlı yöntemlerin kalite kontrol süreçlerinde, raf ömrü tahmininde, duyusal analizlerde, tüketici eğilimlerinin belirlenmesinde ve mikrobiyal/kimyasal bozulmaların erken tespitinde etkili biçimde kullanıldığı ortaya konmuştur. Ayrıca, bu teknolojilerin veri temelli karar destek sistemlerinin gelişimine katkı sağladığı vurgulanmıştır. Sonuç olarak, veri çeşitliliği, açıklanabilirlik ve model güvenilirliği gibi bazı sınırlılıklar bulunsa da, yapay zekânın gıda sistemlerine entegrasyonu, bu alanın geleceğinde daha akıllı, sürdürülebilir, izlenebilir ve tüketici odaklı çözümlerin önünü açacak önemli bir dönüşüm süreci olarak değerlendirilmiştir.

Keywords

Yapay zekâ, Makine öğrenmesi, Derin öğrenme, Gıda bilimi

References

• Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine learning applications based on SVM classification a review. Qubahan Academic Journal, 1(2), 81-90.
• Adak, A., Pradhan, B., & Shukla, N. (2022). Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: Systematic review. Foods, 11(10), 1500.
• Aich, S., Al-Absi, A. A., Hui, K. L., Lee, J. T., & Sain, M. (2018). A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques. 2018 20th International conference on advanced communication technology (ICACT), 139-143.
• Alav, A., Kutlu, N., Kına, E., & Meral, R. (2024). A novel green tea extract-loaded nanofiber coating for kiwi fruit: Improved microbial stability and nutritional quality. Food Bioscience, 62, 105043. https://doi.org/10.1016/j.fbio.2024.105043
• Alfian, G., Syafrudin, M., Farooq, U., Ma’arif, M. R., Syaekhoni, M. A., Fitriyani, N. L., Lee, J., & Rhee, J. (2020). Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control, 110, 107016.
• Al-Tameemi, I. K. S., Feizi-Derakhshi, M.-R., Pashazadeh, S., & Asadpour, M. (2024). A comprehensive review of visual–textual sentiment analysis from social media networks. Journal of Computational Social Science, 1-72.
• Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114.
• Asnicar, F., Thomas, A. M., Passerini, A., Waldron, L., & Segata, N. (2024). Machine learning for microbiologists. Nature Reviews Microbiology, 22(4), 191-205.
• Avcı, İ., & Koca, M. (2024). Intelligent Transportation System Technologies, Challenges and Security. Applied Sciences, 14(11), 4646.
• AVCI, İ., KOCA, M., & ATASOY, M. (2021). Windows Tabanlı Uygulamalarda SQL Enjeksiyon Siber Saldırı Senaryosu ve Güvenlik Önlemleri. Avrupa Bilim ve Teknoloji Dergisi, 28, 213-219. https://doi.org/10.31590/ejosat.995697
• Ayata, F., Çavuş, H., İnan, M., Seyyarer, E., Biçek, E., & Kına, E. (2020). Dostroajan: Facial recognition based system input control agent. AJIT-e: Academic Journal of Information Technology, 11(40), 82-96.
• Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3, 100071.
• Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
• Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
• Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
• Ceylan, Z., Atıcı, C., Unal, K., Meral, R., Kutlu, N., Babaoğlu, A. S., & Dilek, N. M. (2023). A novel material for the microbiological, oxidative, and color stability of salmon and chicken meat samples: Nanofibers obtained from sesame oil. Food Research International, 170, 112952.
• Ceylan, Z., Kutlu, N., Meral, R., Ekin, M. M., & Kose, Y. E., (2021). Protective Effect of Grape Seed Oil-Loaded Nanofibers: Limitation of Microbial Growth and Lipid Oxidation in Kashar Cheese and Fish Meat Samples. Food Bioscience, 42(101076), 101076. https://doi.org/10.1016/j.fbio.2021.101076
• 1Ceylan, Z., Unal, K., Kutlu, N., Meral, R., Balcı, B. A., & Doğu Baykut, E. (2022). A novel gastronomy application technique for ready‐to‐eat salmon meat samples: Curcumin and black seed oil nanofibers with sous vide cooking. Journal of Food Processing and Preservation, e16538. Chen, Y., & Zhang, Z. (2022). Exploring public perceptions on alternative meat in China from social media data using transfer learning method. Food Quality and Preference, 98, 104530.
• Chollet, F. (2017). Deep Learning With Python Manning Publications Company. Manning Publications Company.
• Chu, C., Wang, H., Luo, X., Wen, P., Nan, L., Du, C., Fan, Y., Gao, D., Wang, D., & Yang, Z. (2023). Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods. Foods, 12(20), 3856.
• Cingöz, A., & Nacar, S. (2024). Prediction of Rheological Properties of Flour From Physicochemical Properties Using Multiple Regression Techniques and Artificial Neuronal Networks. Journal of Food Process Engineering, 47(10), e14751.
• Cui, C., Caporaso, N., Chen, J., & Fearn, T. (2023). Farinograph characteristics of wheat flour predicted by near infrared spectroscopy with an ensemble modelling method. Journal of Food Engineering, 359, 111689.
• Çavdar, Z. Y., Avcı, İ., Koca, M., & Sertbaş, A. (2019). A survey of hybrid main memory architectures. Sakarya University Journal of Science, 23(1), 1-15.
• da Costa, N. L., Valentin, L. A., Castro, I. A., & Barbosa, R. M. (2021). Predictive modeling for wine authenticity using a machine learning approach. Artificial Intelligence in Agriculture, 5, 157-162.
• Ekin, M. M., Kutlu, N., Meral, R., Ceylan, Z., & Cavidoglu, İ. (2021). A novel nanotechnological strategy for obtaining fat-reduced cookies in bakery industry: Revealing of sensory, physical properties, and fatty acid profile of cookies prepared with oil-based nanoemulsions. Food Bioscience. https://doi.org/10.1016/j.fbio.2021.101184
• Esmaeily, R., Razavi, M. A., & Razavi, S. H. (2024). A step forward in food science, technology and industry using artificial intelligence. Trends in Food Science & Technology, 143, 104286.
• Geeksforgeeks. (2025a). Introduction to Recurrent Neural Networks.
• GeeksforGeeks. (2025). Multioutput Regression in Machine Learning. https://www.geeksforgeeks.org/multioutput-regression-in-machine-learning/
• Geeksforgeeks. (2025b). What is LSTM - Long Short Term Memory?
• Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.
• Hebbar, N. (2020). Freshness of food detection using IoT and machine learning. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 1-3.
• Hu, G., Ahmed, M., & L’Abbé, M. R. (2023). Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. The American Journal of Clinical Nutrition, 117(3), 553-563.
• IBM. (2025a). What are convolutional neural networks?
• IBM. (2025b). What is a recurrent neural network (RNN)?
• IBM. (2025c). What is classification in machine learning? https://www.ibm.com/think/topics/classification-machine-learning.
• Irnawati, I., Windarsih, A., Indrianingsih, A. W., Apriyana, W., Ratnawati, Y. A., & Rohman, A. (2023). Rapid detection of tuna fish oil adulteration using FTIR-ATR spectroscopy and chemometrics for halal authentication. Journal of Applied Pharmaceutical Science, 13(4), 231-239.
• Junior, S. B., Mastelini, S. M., Barbon, A. P. A. C., Barbin, D. F., Calvini, R., Lopes, J. F., & Ulrici, A. (2020). Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy. Information processing in agriculture, 7(2), 342-354.
• Kang, Y., Cai, Z., Tan, C.-W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.
• Karunathilaka, S. R., Yakes, B. J., Choi, S. H., Brückner, L. E. A., & Mossoba, M. M. (2020). Comparison of the performance of partial least squares and support vector regressions for predicting fatty acids and fatty acid classes in marine oil dietary supplements by using vibrational spectroscopic data. Journal of food protection, 83(5), 881-889.
• Kına, E. (2022). Detection of the Trends of Football Fans in Turkey by Machine Learning Algorithm Using Twitter Data. International Journal of Scientific and Engineering Research, 13(4).
• Kına, E. (2023). ALGORİTMİK OYUN KURAMI (E. Biçek, Ed.; 1. bs). İksad Yayınevi. https://iksadyayinevi.com/wp-content/uploads/2023/07/ALGORITMIK-OYUN-KURAMI.pdf
• Kına, E., & Biçek, E. (2023a). Duygu Analizinde Denetimli Makine Öğrenme Algoritmalarının Karşılaştırılmaları,(Kahramanmaraş Depremi Örneği). Batman Üniversitesi Yaşam Bilimleri Dergisi, 13(1), 21-31.
• Kına, E., & Biçek, E. (2023b). Metaverse–Yeni Dünyaya İlk Adım (E. Kına & E. Biçek, Ed.). İksad Yayınevi. https://iksadyayinevi.com/wp-content/uploads/2023/06/METAVERSE-–-YENI-DUNYAYA-ILK-ADIM.pdf
• KINA, E., & BİÇEK, E. (2023). Tweetlerin Duygu Analizi İçin Hibrit Bir Yaklaşım. Doğu Fen Bilimleri Dergisi, 6(1), 57-68. https://doi.org/10.57244/dfbd.1314901
• Kına, E., & Biçek, E. (2024). Machine Learning Approach for Emotion Identification and Classification in Bitcoin Sentiment Analysis TT - Bitcoin Duygu Analizinde Duygu Tanıma ve Sınıflandırma için Makine Öğrenmesi Yaklaşımı. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(3), 913-926. https://doi.org/10.53433/yyufbed.1532649
• Kına, E., Biçek, E., İnan, M., Gümüş, O., & Alkan, A. (2024). Üniversitelerde Dijital Araç Yönetimi: Van Yüzüncü Yıl Üniversitesi Örneğiyle Web Tabanlı Araç Takip ve İzleme Sistemi. Bartin University International Journal of Natural and Applied Sciences, 7(2).
• Kına, E., & İnan, M. (2023). Metaverse ve Güvenlik. Içinde İ. Avcı & M. Koca (Ed.), BİLGİ VE BİLİŞİM SİSTEMLERİ GÜVENLİĞİ (1. bs, ss. 187-243). ATLAS AKADEMİK BASIM YAYIN DAĞITIM .
• Kına, E., & Özdağ, R. (2021). Complexity Matrices in Twitter Sentiment Analysis of Thoughts on Mobile Games Using Machine Learning Algorithms. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2(2), 91-100.
• Kına, E., & Özdağ, R. (2024). Multilingual Sentiment Analysis for Mobile Gaming: A Comparative Study of Machine Learning and Hybrid Deep Learning Approaches. SETSCI-Conference Proceedings, 21, 1-5.
• Kina, E. (2025). TLEABLCNN: Brain and Alzheimer’s Disease Detection using Attention based Explainable Deep Learning and SMOTE using Imbalanced Brain MRI. IEEE Access, 13, 27670-27683. https://doi.org/10.1109/ACCESS.2025.3539550
• Koca, M. (2024a). Real-Time Security Risk Assessment From CCTV Using Hand Gesture Recognition. IEEE Access, 12, 84548-84555. https://doi.org/10.1109/ACCESS.2024.3412930
• Koca, M. (2024b). Real-Time Security Risk Assessment From CCTV Using Hand Gesture Recognition. IEEE Access, 12, 84548-84555. https://doi.org/10.1109/ACCESS.2024.3412930
• Koca, M., Avcı, İ., & Al-hayani, M. A. S. (2023). Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm. Sakarya University Journal of Computer and Information Sciences, 6(2), 80-90.
• Koca, M., & Avci, I. (2024). A Novel Hybrid Model Detection of Security Vulnerabilities in Industrial Control Systems and IoT Using GCN+LSTM. IEEE Access, 12, 143343-143351. https://doi.org/10.1109/ACCESS.2024.3466391
• Kumar, S., & Bhatnagar, V. (2022). A review of regression models in machine learning. Journal of Intelligent Systems and Computing, 3(1), 40-47.
• Kutlu, N., Alav, A., & Meral, R. (2024). A novel Nanocoating with zein, gallic acid, and flaxseed oil: Enhancing stability of chicken meatballs. Food Bioscience, 60, 104253.
• Kutlu, N., & Meral, Raciye, Ekin, M.M., Erim Kose, Y., Ceylan, Z. (2021). A new application for the valorisation of pomegranate seed oil : nanoencapsulation of pomegranate seed oil into electrospun nanomats for food preservation. International Journal of Food Science and Technology, 1-9. https://doi.org/10.1111/ijfs.15472
• Lareyre, F., Nasr, B., Chaudhuri, A., Di Lorenzo, G., Carlier, M., & Raffort, J. (2023). Comprehensive review of natural language processing (NLP) in vascular surgery. EJVES Vascular Forum, 60, 57-63.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
• Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia Cirp, 99, 650-655.
• Meral, R., Alav, A., Karakas, C., Dertli, E., Yilmaz, M. T., & Ceylan, Z. (2019). Effect of electrospun nisin and curcumin loaded nanomats on the microbial quality, hardness and sensory characteristics of rainbow trout fillet. LWT, 113. https://doi.org/10.1016/j.lwt.2019.108292
• Meral, R., Ceylan, Z., Kutlu, N., Kılıçer, A., Çağlar, A., & Tomar, O. (2022). Antimicrobial nanocoating for food industry. Içinde H. Chaudrey (Ed.), Handbook of Microbial Nanotechnology (ss. 225-284). Academic Press. https://www.elsevier.com/books/handbook-of-microbial-nanotechnology/hussain/978-0-12-823426-6
• Meral, R., Ekin, M. M., Ceylan, Z., Alav, A., & Kına, E. (2025). A Novel Solution to Enhance the Oxidative and Physical Properties 2 of Cookies Using Maltodextrin-Based Nano-Sized Oils as a Fat 3 Substitute. ACS Omega, 10(22), 23111-23120.
• Meral, R., Ekin, M. M., Kutlu, N., & Kose, Y. E. (2022). The nanoemulsions: A new design and fat-reducing strategy for the bakery industry. Their effects on some quality attributes of fat-reduced cakes. Journal of Food Processing and Preservation, 46(12), e17160. https://doi.org/10.1111/jfpp.17160 Meral, R., & Kamberoğlu, G. S. (2012). Tahıllardan etanol üretimi. Journal of the Institute of Science and Technology, 2(3), 61-68.
• Meral, R., Kına, E., & Ceylan, Z. (2024). Low-Calorie Cookies Enhanced with Fish Oil-Based Nano-ingredients for Health-Conscious Consumers. ACS Omega, 9(37), 39159-39169. https://doi.org/10.1021/acsomega.4c06050
• Meral, R., Kose, Y. E., Ceylan, Z., & Cavidoglu, İ. (2022). The potential use of agro-industrial by-products as sources of bioactive compounds: a nanotechnological approach. Studies in Natural Products Chemistry, 73, 435-466.
• Mishra, M., Sarkar, T., Choudhury, T., Bansal, N., Smaoui, S., Rebezov, M., Shariati, M. A., & Lorenzo, J. M. (2022). Allergen30: detecting food items with possible allergens using deep learning-based computer vision. Food Analytical Methods, 15(11), 3045-3078.
• Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., & Ashida, K. (2022). Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews, 80(12), 2288-2300.
• Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Fundamentals of artificial neural networks and deep learning. Içinde Multivariate statistical machine learning methods for genomic prediction (ss. 379-425). Springer.
• Mostafa, M. M. (2019). Clustering halal food consumers: A Twitter sentiment analysis. International Journal of Market Research, 61(3), 320-337.
• Muhammad, L. J., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021a). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), 11.
• Muhammad, L. J., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021b). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), 11.
• Nafea, A. A., Alameri, S. A., Majeed, R. R., Khalaf, M. A., & AL-Ani, M. M. (2024). A short review on supervised machine learning and deep learning techniques in computer vision. Babylonian Journal of Machine Learning, 2024, 48-55.
• Naravane, T., & Tagkopoulos, I. (2023). Machine learning models to predict micronutrient profile in food after processing. Current Research in Food Science, 6, 100500.
• Nerkar, P. M., Shinde, S. S., Liyakat, K. K. S., Desai, S., & Kazi, S. S. L. (2023). Monitoring fresh fruit and food using IoT and machine learning to improve food safety and quality. Tuijin Jishu/Journal of Propulsion Technology, 44(3), 2927-2931.
• Okumuş, E., Bakkalbaşı, E., Javidipour, I., Meral, R., & Ceylan, Z. (2021). A novel coating material: Ellagitannins-loaded maltodextrin and lecithin-based nanomaterials. Food Bioscience. https://doi.org/10.1016/j.fbio.2021.101158
• Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. 2017 15th international conference on ICT and knowledge engineering (ICT&KE), 1-6.
• Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.
• Ozturk, S., Bowler, A., Rady, A., & Watson, N. J. (2023). Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process. Journal of Food Engineering, 341, 111339.
• Rahman, S. M. A., Nassef, A. M., Al-Dhaifallah, M., Abdelkareem, M. A., & Rezk, H. (2020). The effect of a new coating on the drying performance of fruit and vegetables products: experimental investigation and artificial neural network modeling. Foods, 9(3), 308.
• Raj, A. S., Badgujar, C. M., Lollato, R., Prasad, P. V. V., & Siliveru, K. (2024). Predicting rheological properties of wheat dough from flour properties using NIR coupled with artificial neural network. Journal of the ASABE, 67(4), 1023-1035.
• Rikters, M., & Kāle, M. (2023). The Future of Meat: Sentiment Analysis of Food Tweets. Proceedings of the 11th International Workshop on Natural Language Processing for Social Media, 38-46.
• Rousu, J., Flander, L., Suutarinen, M., Autio, K., Kontkanen, P., & Rantanen, A. (2003). Novel computational tools in bakery process data analysis: a comparative study. Journal of Food Engineering, 57(1), 45-56.
• Satorabi, M., Salehi, F., & Rasouli, M. (2021). The influence of xanthan and balangu seed gums coats on the kinetics of infrared drying of apricot slices: GA-ANN and ANFIS modeling. International Journal of Fruit Science, 21(1), 468-480.
• Scatigno, C., & Festa, G. (2022). FTIR coupled with machine learning to unveil spectroscopic benchmarks in the Italian EVOO. International Journal of Food Science & Technology, 57(7), 4156-4162.
• Sen Gupta, D., Sharanagat, V. S., Chakraborty, G., Kumar, J., Parihar, A. K., Yadav, T., Swaraj, Upadhyay, S., Desai, S., & Katiyar, P. K. (2025). Urdbean (Vigna mungo L. Hepper) cultivar characterization based on multiple seed and flour properties and their multi-variate analysis using artificial neural network. Food Production, Processing and Nutrition, 7(1), 26.
• Sharma, N. A., Ali, A. B. M. S., & Kabir, M. A. (2024). A review of sentiment analysis: tasks, applications, and deep learning techniques. International Journal of Data Science and Analytics, 1-38.
• Shaw, B., Suman, A. K., & Chakraborty, B. (2020). Wine quality analysis using machine learning. Emerging technology in modelling and graphics: proceedings of IEM graph 2018, 239-247.
• Shen, Z., Shehzad, A., Chen, S., Sun, H., & Liu, J. (2020). Machine learning based approach on food recognition and nutrition estimation. Procedia Computer Science, 174, 448-453.
• Shetty, S. H., Shetty, S., Singh, C., & Rao, A. (2022). Supervised machine learning: algorithms and applications. Fundamentals and methods of machine and deep learning: algorithms, tools and applications, 1-16.
• Smithers, G. W. (2016). Food Science–Yesterday, Today and Tomorrow. Reference Module in Food Science, 1-11.
• Snowflake.com. (2025). Types of Regression Models in Machine Learning. https://www.snowflake.com/guides/types-regression-models-ml/
• 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.
• Tabassum, N., Aftab, R. A., Yousuf, O., Ahmad, S., & Zaidi, S. (2023). Application of nanoemulsion based edible coating on fresh-cut papaya. Journal of Food Engineering, 355, 111579.
• Tutul, M. J. I., Alam, M., & Wadud, M. A. H. (2023). Smart food monitoring system based on iot and machine learning. 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), 1-6.
• Windarsih, A., Jatmiko, T. H., Anggraeni, A. S., & Rahmawati, L. (2024). Machine learning-assisted FT-IR spectroscopy for identification of pork oil adulteration in tuna fish oil. Vibrational Spectroscopy, 134, 103715.
• Xu, Y., Hassan, M. M., Kutsanedzie, F. Y. H., Li, H. H., & Chen, Q. S. (2018). Evaluation of extra-virgin olive oil adulteration using FTIR spectroscopy combined with multivariate algorithms. Quality Assurance and Safety of Crops & Foods, 10(4), 411-421.
• Yang, C., & Huang, C. (2023). Natural language processing (NLP) in aviation safety: Systematic review of research and outlook into the future. Aerospace, 10(7), 600.
• Zatsu, V., Shine, A. E., Tharakan, J. M., Peter, D., Ranganathan, T. V., Alotaibi, S. S., Mugabi, R., Muhsinah, A. Bin, Waseem, M., & Nayik, G. A. (2024). Revolutionizing the food industry: The transformative power of artificial intelligence-a review. Food Chemistry: X, 101867.
• Zhang, S., Qi, X., Gao, M., Dai, C., Yin, G., Ma, D., Feng, W., Guo, T., & He, L. (2024). Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms. Food Chemistry, 448, 139103.
• Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233-249.