Gıda Endüstrisinde Yapay Zeka Uygulamaları

Yıl 2025, Sayı: 9, 28 - 36, 31.03.2025

Elif Ceren Çakıroğlu , Güzin İplikçioğlu Aral

Öz

Dünya nüfusundaki hızlı artış, gıda kaynaklarına yönelik talebin de paralel şekilde yükselmesine neden olmaktadır. Bu durum, gıda endüstrisini hem yenilikçi değişikliklere ve hem de artan talepleri karşılamak amacıyla çözümler geliştirmeye yönlendirmiştir. Küreselleşme, teknolojik ilerlemeler ve tüketici beklentilerindeki değişiklikler, gıda endüstrisini önemli ölçüde dönüştürmüştür. Bu süreçte yapay zeka (YZ) ve teknolojileri, gıda endüstrisindeki dönüşümlere öncülük etmektedir.
Gıda endüstrisinde yapay zeka; gıda güvenliği, kalite değerlendirmesi, kontrol sistemleri, üretim, işleme, ambalajlama ve dağıtım süreçlerinin verimliliğini arttırarak daha güvenilir bir yapı sağlamaktadır. Yapay sinir ağları, bulanık mantık, genetik algoritma gibi yapay zeka uygulamaları sektörde yaygın olarak son yıllarda kullanılmaktadır. Bu uygulamalar, gıda endüstrisinde yenilikçi çözümler getirerek verimliliğin artmasına katkı sağlamıştır.
Gıda güvenliği, halk sağlığını koruma ve gıda kaynaklı hastalıkların önlemek açısından büyük önem taşımaktadır. Gıda güvenliği yönetim sistemleri, tehlikeleri önlemek ve riskleri minimuma indirmek için çeşitli stratejiler sunmaktadır. Yapay zeka, bu stratejilerin etkinliğini arttırmak için, veriye dayalı proaktif bir yaklaşımla riskleri tespit edebilen öncü göstergeler geliştirerek gıda güvenliği yönetiminde etkili rol oynamaktadır. Bunun yanında, YZ ve veri analitiği; gıda tedarik zincirinde risk yönetimi, kalite güvencesi ve tüketici tercihlerinin izlenmesi, ürün geliştirme ve pazarlama gibi alanlarda önemli avantajlar sağlamaktadır. Çiftlikten sofraya gıdaların izlenmesi, ekipman bakımı gibi birçok alanda yapay zeka uygulamaları yaygın olarak kullanılmaktadır. Ayrıca, atık su arıtımlarında da YZ teknolojileri kullanılarak çevresel riskleri azaltmak ve maliyetleri düşürmek için kullanılmaktadır.
Yapay zeka, gıda endüstrisinde üretim süreçlerinden tüketici tercihlerini anlamaya kadar geniş bir yelpazede yer bularak sektörü daha verimli, sürdürülebilir ve yenilikçi bir geleceğe taşıyacağı düşünülmektedir.

Anahtar Kelimeler

Yapay zeka, gıda, gıda güvenliği.

Kaynakça

• Ahmad, A., Liew, A. X. W., Venturini, F., Kalogeras, A., Candiani, A., Di Benedetto, G., Ajibola, S., Cartujo, P., Romero, P., Lykoudi, A., De Grandis, M. M., Xouris, C., Lo Bianco, R., Doddy, I., Elegbede, I., D'Urso Labate, G. F., García del Moral, L. F. ve Martos, V. (2024). AI can empower agriculture for global food security: Challenges and prospects in developing nations. Frontiers in Artificial Intelligence, 7, 1328530. https://doi.org/10.3389/frai.2024.1328530
• Anonim. (2019). Food Safety Magazine. Artificial intelligence and food safety: Hype vs. reality. Food Safety Magazine. Retrieved November 27, 2024, from https://www.food-safety.com/articles/6416-artificial-intelligence-and-food-safety-hype-vs-reality
• Ben Ayed, R. ve Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021, Article 5584754, 7. https://doi.org/10.1155/2021/5584754
• Bhagya Raj, G. V. S. ve 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
• DiMucci, D., Kon, M. ve Segrè, D. (2018). Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks. mSystems, 3(5). https://doi.org/10.1128/msystems.00181-18
• Ding, Y., Sun, Q., Lin, Y., Ping, Q., Peng, N., Wang, L. ve Li, Y. (2024). Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction. Water Research, 253, 121267. https://doi.org/10.1016/j.watres.2024.121267
• Gharaei, A., Karimi, M. ve Hoseini Shekarabi, S. A. (2019). An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control policies and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm. Applied Mathematical Modelling, 69, 223–254. https://doi.org/10.1016/j.apm.2018.11.035
• Goodfellow, I., Bengio, Y. ve Courville, A. (2016). Deep learning. MIT Press. Retrieved from http://www.deeplearningbook.org. Healthcare Informatics Research, 22(4), 351-354.
• Haiminen, N., Edlund, S., Chambliss, D., Kunitomi, M., Weimer, B. C., Ganesan, B., Baker, R., Markwell, P., Davis, M., Huang, B. C., Kong, N., Prill, R. J., Marlowe, C. H., Quintanar, A., Pierre, S., Dubois, G., Kaufman, J. H., Parida, L. ve Beck, K. L. (2019). Food authentication from shotgun sequencing reads with an application on high protein powders. npj Science of Food, 3(1), 24. https://doi.org/10.1038/s41538-019-0056-6
• Hamet, P. ve Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism: Clinical and Experimental, 69(Supplement), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
• Harrand, A. S., Strawn, L. K., Illas-Ortiz, P. M., Wiedmann, M. ve Weller, D. L. (2020). Listeria monocytogenes Prevalence Varies More within Fields Than between Fields or over Time on Conventionally Farmed New York Produce Fields. Journal of Food Protection, 83(11), 1958–1966. https://doi.org/10.4315/JFP-20-120
• Imanian, B., Donaghy, J., Jackson, T., Gummalla, S., Ganesan, B., Robert C. Baker, Henderson, M., Butler, E. K., Hong, Y., Ring, B., Thorp, C., Khaksar, R., Samadpour, M., Lawless, K. A., MacLaren-Lee, I., Carleton, H. A., Tian, R., Zhang, W. ve Wan, J. (2022). The power, potential, benefits, and challenges of implementing high-throughput sequencing in food safety systems. npj Science of Food, 6(35). https://doi.org/10.1038/s41538-022-00150-6
• Jordan, M. I. ve Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
• Kilimann, K., Hartmann, C., Delgado, A., Vogel, R. ve Ganzle, M. (2005). A fuzzy logic-based model for the multistage high-pressure inactivation of Lactococcus lactis ssp. MG 1363. International Journal of Food Microbiology, 98(1), 89–105. https://doi.org/10.1016/j.ijfoodmicro.2004.05.010
• Kim, D., Kim, S. Y., Yoo, R., Choo, J. ve Yang, H. (2024). Innovative AI methods for monitoring front-of-package information: A case study on infant foods. PloS one, 19(5), e0303083. https://doi.org/10.1371/journal.pone.0303083
• Kodogiannis, V. S., Kontogianni, E. ve Lygouras, J. N. (2014). Neural network-based identification of meat spoilage using Fourier-transform infrared spectra. Journal of Food Engineering, 142, 118–131. https://doi.org/10.1016/j.jfoodeng.2014.06.018
• Kyaw, T., Oo, N. N. ve Zaw, W. (2019). Building travel speed estimation model for Yangon city from public transport trajectory data. In T. Zin & J. W. Lin (Eds.), Big data analysis and deep learning applications. ICBDL 2018. Advances in Intelligent Systems and Computing, 744, 250-257. Springer. https://doi.org/10.1007/978-981-13-0869-7_28
• Lei, M., Xu, L., Liu, T., Liu, S. ve Sun, C. (2022). Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges. Foods, 11(15), 2262. https://doi.org/10.3390/foods11152262
• Liu, Z., Wang, S., Zhang, Y., Feng, Y., Liu, J. ve Zhu, H. (2023). Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods, 12(6), 1242. https://doi.org/10.3390/foods12061242
• Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H. ve Abd Rahman, N. (2022). Application of artificial intelligence in food industry—a guideline. Food Engineering Reviews, 14(2), 134–175. https://doi.org/10.1007/s12393-021-09290-z
• Mavani, N. R., Mohd Ali, J., Hussain, M. A., Abd Rahman, N. ve Hashim, H. (2024). Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration. Heliyon, 10(4), e26273. https://doi.org/10.1016/j.heliyon.2024.e26273
• McKenzie, F. C. ve Williams, J. (2015). Sustainable food production: Constraints, challenges and choices by 2050. Food Security, 7(2), 221–233. https://doi.org/10.1007/s12571-015-0441-1
• Nychas, G. J., Sims, E., Tsakanikas, P. ve Mohareb, F. (2021). Data Science in the Food Industry. Annual Review of Biomedical Data Science, 4, 341–367. https://doi.org/10.1146/annurev-biodatasci-020221-123602
• Pfisterer, K. J., Amelard, R., Chung, A. G. ve Wong, A. (2018). A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared puréed food concentrations. Journal of Food Engineering, 223, 220–235. https://doi.org/10.1016/j.jfoodeng.2017.10.016
• Qian, C., Murphy, S. I., Orsi, R. H. ve Wiedmann, M. (2023). How can AI help improve food safety? Annual Review of Food Science and Technology, 14(1), 517–538. https://doi.org/10.1146/annurev-food-060721-013815
• Sahu, M., Gupta, R., Ambasta, R. K. ve Kumar, P. (2022). Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. In D. B. Teplow (Ed.), Progress in Molecular Biology and Translational Science, 190(1), 57–100. Academic Press. https://doi.org/10.1016/bs.pmbts.2022.03.002
• Schroeder, T. C., Tonsor, G. T., Pennings, J. M. E. ve Mintert, J. (2007). Consumer food safety risk perceptions and attitudes: Impacts on beef consumption across countries. The B.E. Journal of Economic Analysis & Policy, 7(1), Article 65. https://doi.org/10.2202/1935-1682.1655
• Simeone, A., Woolley, E., Escrig, J. ve Watson, N. J. (2020). Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression. Sensors, 20(13), 3642. https://doi.org/10.3390/s20133642
• Spalding, A., Goodhue, R. E., Kiesel, K. ve Sexton, R. J. (2023). Economic impacts of food safety incidents in a modern supply chain: E. coli in the romaine lettuce industry. American Journal of Agricultural Economics, 105(2), 597–623. https://doi.org/10.1111/ajae.12341
• Sun, Q., Zhang, M. ve Mujumdar, A. S. (2018). Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition, 58(14), 2258-2275. https://doi.org/10.1080/10408398.2018.1446900
• Talon, R., Walter, D., Viallon, C. ve Berdagué, J. L. (2002). Prediction of Streptococcus salivarius subsp. thermophilus and Lactobacillus delbrueckii subsp. bulgaricus populations in yoghurt by Curie point pyrolysis-mass spectrometry. Journal of Microbiological Methods, 48(2-3), 271–279. https://doi.org/10.1016/s0167-7012(01)00329-3
• Umoh, U. A., Eyoh, I. J., Murugesan, V. S. ve Nyoho, E. E. (2022). Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis. In R. Pandey, S. K. Khatri, N. K. Singh, & P. Verma (Eds.), Artificial Intelligence and Machine Learning for EDGE Computing, 207–233. Academic Press. https://doi.org/10.1016/B978-0-12-824054-0.00025-3
• Vivek, K., Subbarao, K. V., Routray, W., Kamini, N. R. ve Dash, K. K. (2020). Application of fuzzy logic in sensory evaluation of food products: A comprehensive study. Food and Bioprocess Technology, 13(1), 1–29. https://doi.org/10.1007/s11947-019-02337-4
• Xu, W.-L., Wang, Y.-J., Wang, Y.-T., Li, J.-G., Zeng, Y.-N., Guo, H.-W., Liu, H., Dong, K.-L. ve Zhang, L.-Y. (2024). Application and innovation of artificial intelligence models in wastewater treatment. Journal of Contaminant Hydrology, 267, 104426. https://doi.org/10.1016/j.jconhyd.2024.104426
• Yu, Q., Zhang, M., Mujumdar, A. S. ve Li, J. (2024). AI-based additive manufacturing for future food: Potential applications, challenges and possible solutions. Innovative Food Science & Emerging Technologies, 92, 103599. https://doi.org/10.1016/j.ifset.2024.103599
• Zare, D. ve Ghazali, H. M. (2017). Assessing the quality of sardine based on biogenic amines using a fuzzy logic model. Food Chemistry, 221, 936–943. https://doi.org/10.1016/j.foodchem.2016.11.071
• Zoellner, C., Jennings, R., Wiedmann, M. ve Nightingale, K. (2019). EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities. Scientific Reports, 9(1), 495. https://doi.org/10.1038/s41598-018-36654-z