1818-4049 3033-635X Власть и управление на Востоке России POWER AND ADMINISTRATION IN THE EAST OF RUSSIA 10.22394/1818-4049-2024-109-4-103-113 Искусственный интеллект в реальном секторе экономики (на примере пищевой индустрии) Artificial Intelligence in the Real Sector of the Economy (on the Example of the Food Industry) Алешков Алексей Викторович Алешков Алексей Викторович Aleshkov Alexey V. aleshkovalexey@gmail.com 0000-0003-3853-4772 Синюков Василий Алексеевич Синюков Василий Алексеевич Sinyukov Vasily A. v.sinukov@yandex.ru 0000-0001-6266-0088 Ивашкин Михаил Вячеславович Ивашкин Михаил Вячеславович Ivashkin Mikhail V. Ivashkin62@mail.ru 0009-0001-5558-4619 Тихоокеанский государственный университет, Хабаровск, Россия The Pacific State University, Khabarovsk, Russia 14 01 2025 4(109) 103 113 06 05 2024 14 10 2022 10 10 2024

В основу оригинального исследования положена гипотеза о неотвратимости внедрения искусственного интеллекта в реальный сектор экономики. Авторы рассматривают нормативно-правовые основы искусственного интеллекта, а также его исторические аспекты. При этом обозначены базовые траектории применения искусственного интеллекта на примере пищевой индустрии – при моделировании и оптимизации пищевых технологий, идентификации, оценке качества и безопасности пищевой продукции. Отмечено, что предпосылкой к внедрению технологий искусственного интеллекта в пищевой индустрии выступает четко наметившийся переход от массового питания к персонализированному, связанный с производством преимущественно комбинированных продуктов сложного ингредиентного и химического состава. Как следствие, технологии искусственного интеллекта позволяют существенно оптимизировать ресурс времени, повысить эффективность и точность совершаемых покупок, способствуют снижению затрат на приобретение продукции и устранению предубеждений при принятии решений, а также предоставляют максимально персонализированные рекомендации при покупке товаров. Результаты исследования включают обзор и анализ трудов российских и зарубежных ученых в области искусственного интеллекта и сфер его применения в пищевой индустрии, а методология исследования базируется на таких теоретических методах научного познания, как сравнение, анализ, систематизация, дедукция, абстракция, обобщение. Также статья раскрывает юридические и концептуальные аспекты искусственного интеллекта и областей его применения, рассматривает перспективные направления дальнейшего проникновения искусственного интеллекта в пищевой индустрии. В заключении обобщаются преимущества и возможности использования искусственного интеллекта по всей цепочке прослеживаемости пищевой продукции, использования его для анализа цифрового профиля при установлении аутентичности и идентификации пищевой продукции.

The study is based on the hypothesis that the implementation of artificial intelligence into the real sector of economy is inevitable. The authors consider the regulatory framework of artificial intelligence as well as its historical aspects. The basic trajectories of the artificial intelligence implementation on the example of the food industry (modeling and optimizing food technologies, identification, assessing the quality and safety of food products) are indicated. It was noted that the prerequisite for implementation of artificial intelligence technologies in the food industry is a clearly outlined transition from mass nutrition to personalized, associated with the production of mainly combined products of complex ingredient and chemical composition. Applying of artificial intelligence technologies can significantly optimize the resource of time, increase the efficiency and accuracy of purchases, help reduce the cost of purchasing products, eliminate biases in decision-making, and provide the most personalized recommendations for buying goods. The study results include the analysis of Russian and foreign scientists researches in the field of artificial intelligence and its application in the food industry. The research methodology is based on such theoretical methods as comparison, analysis, systematization, deduction, abstraction, and generalization. The article also reveals legal and conceptual aspects of artificial intelligence application, its advantages and possibilities, considers promising areas for further penetration of artificial intelligence in the food industry.

 Ключевые слова реальный сектор экономики искусственный интеллект нейросеть пищевая индустрия Keywords real sector of economy artificial intelligence neural network food industry Алешков, А. В., Ивашкин, М. В., Синюков, В. А. Управление инновациями в пищевой индустрии с использованием нейросетей // Вестник Тихоокеанского государственного университета. 2023. № 4 (71). С. 97–104. EDN: MNFSMW Метленкин, Д. А., Платов, Ю. Т., Платова, Р. А. Идентификация кофе в зернах методами Фурье-ИК-спектроскопии и многомерного анализа // Известия высших учебных заведений. Пищевая технология. 2021. № 5–6 (383–384). С. 92–97 https://doi.org/10.26297/0579-3009.2021.5-6.17. Садовой, В. В. Разработка научных принципов проектирования состава и совершенствования технологии многокомпонентных мясных изделий с использованием вторичных ресурсов пищевой промышленности: автореф. дис. д-ра техн. наук: 05.18.04. Ставрополь, 2007. 38 с. 4. Abbasi-Tarighat, M., Shahbazi, E., Niknam, K. (2013) Simultaneous determination of Mn2+ and Fe3+ as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol) complexes in some foods, vegetable and water samples by artifi cial neural networks Food Chemistry, vol. 138, is. 2–3, pр. 991–997. https://doi.org/10.1016/j.foodchem.2012.09.099 5. Batista, L., Marques, C., Pires, A., A. Minim, L., Soares, N., Vidigal, M. (2021) Artifi cial neural networks modeling of non-fat yogurt texture properties: effect of process conditions and food composition, Food and Bioproducts Processing, vol. 126, pp. 164–174 https://doi. org/10.1016/j.fbp.2021.01.002. 6. Chakravartula, S., Moscetti, R., Bedini, G., Nardella, M., Massantini, R. (2022) Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee, Food Control, vol. 135 https://doi.org/10.1016/j. foodcont.2022.108816. 7. Cotrim, W. da Silva, Felix, L. B, Minim, V. P. R, Campos R. C., Minim, L. A. (2021) Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing Chemical Engineering Science, vol. 240, pp. 116679. ISSN 0009-2509. https://doi.org/10.1016/j. ces.2021.116679 8. Chaitanya, A., Shetty, J., Chiplunkar, P. (2023) Food Image Classifi cation and Data Extraction Using Convolutional Neural Network and Web Crawlers, Procedia Computer Science, vol. 218, pp. 143–152. https://doi.org/10.1016/j.procs.2022.12.410 9. Chen, D., Guo, C., Lu, W., Zhang, C., Xiao, C. (2023) Rapid quantifi cation of royal jelly quality by mid-infrared spectroscopy coupled with backpropagation neural network Food Chemistry, vol. 418. https://doi.org/10.1016/j.foodchem.2023.135996 10. O’Farrell M., Lewis E., Flanagan C., Lyons W., Jackman N. Combining principal component analysis with an ar tifi cial neural network to perform online quality assessment of food as it cooks in a large-scale industrial oven. Sensors and Actuators B: Chemical. 2005. Volume 107. Issue 1. Pages 104-112. https://doi.org/10.1016/j.snb.2004.09.050. 11. Dębska, B., Guzowska-Świder, B. (2011) Application of artifi cial neural network in food classifi cation Analytica Chimica Acta, vol. 705, is. 1–2, pp. 283–291 https://doi.org/10.1016/j. aca.2011.06.033 12. Furtado, P., Caldeira, M., Martins, P. (2020) Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison // Computer Vision and Image Understanding, vol. 191 https://doi.org/10.1016/j.cviu.2019.102878 13. Gonzаlez-Viveros, N., Gоmez-Gil, P., Castro-Ramos, J., Cerecedo-Nunez, H. (2021) On the estimation of sugars concentrations using Raman spectroscopy and artifi cial neural networks Food Chemistry, vol. 352. https://doi.org/10.1016/j.foodchem.2021.129375. 14. Kowalski, R., Pietrysiak, E., Ganjyal, G. (2021) Optimizing screw profi les for twin-screw food extrusion processing through genetic algorithms and neural networks, Journal of Food Engineering, vol., 303. https://doi.org/10.1016/j.jfoodeng.2021.110589 15. Kupongsak, S., Tan, J. (2006) Application of fuzzy set and neural network techniques in determining food process control set points Fuzzy Sets and Systems, vol., 157, is. 9, pp. 1169–1178. https://doi.org/10.1016/j.fss.2005.12.015 16. Lee, G., Kim, R., Lim, T., Kim, J., Kim, S., Kim, H., Taek, Hwang K. (2022) Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artifi cial neural network coupled with genetic algorithm, Food Chemistry, vol. 396. https://doi.org/10.1016/j. foodchem.2022.133712 17. Lewis, E., Sheridan, C., O’Farrell, M., Flanagan, C., Kerry, J., Jackman, N. (2008) Optical fi bre sensors for assessing food quality in full scale production ovens – a principal component analysis and artifi cial neural network based approach Nonlinear Analysis: Hybrid Systems, vol. 2, is. 1, pp. 51–57 https://doi.org/10.1016/j.nahs.2006.05.008 18. Li, Y., Fei, C., Mao, C., Ji D., Gong, J., Qin, Y., Qu, L., Zhang, W., Bian, Z., Su, L., Lu, T. (2022) Physicochemical parameters combined fl ash GC e-nose and artifi cial neural network for quality and volatile characterization of vinegar with different brewing techniques, Food Chemistry, vol. 374. https://doi.org/10.1016/j.foodchem.2021.131658 19. Nayak, J., Vakula, K., Dinesh, P., Naik, B., Pelusi, D. (2020) Intelligent food processing: Journey from artifi cial neural network to deep learning, Computer Science Review, vol. 38. https://doi.org/10.1016/j.cosrev.2020.100297 20. Silva, C. E. Tanajura da, Filardi, V. L., Pepe, I. M., Chaves, M. A., Santos, C. M. S. (2015) Classifi cation of food vegetable oils by fl uorimetry and artifi cial neural networks Food Control, vol. 47, pp. 86–91. https://doi.org/10.1016/j.foodcont.2014.06.030 21. Senizza, B., Ganugi, P., Trevisan, M., Lucini, L. (2023) Combining untargeted profi ling of phenolics and sterols, supervised multivariate class modelling and artifi cial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure, Food Chemistry, vol. 404, part A. https://doi.org/10.1016/j.foodchem.2022.134543 22. Sathish, S., Ashwin, S., Quadir, Md., Pavithra, L. (2022) Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories, Materials Today: Proceedings, vol. 62, part 7, pp. 4665–4670. https://doi.org/10.1016/j.matpr.2022.03.122 23. Soni, A., Al-Sarayreh, M., Reis, M., Brightwell, G. (2021) Hyperspectral imaging and deep learning for quantifi cation of Clostridium sporogenes spores in food products using 1D-convolutional neural networks and random forest model, Food Research International, vol. 147 https://doi.org/10.1016/j.foodres.2021.110577 1. Алешков, А. В., Ивашкин, М. В., Синюков, В. А. Управление инновациями в пи- щевой индустрии с использованием нейросетей // Вестник Тихоокеанского государ- ственного университета. 2023. № 4 (71). С. 97–104. EDN: MNFSMW 2. Метленкин, Д. А., Платов, Ю. Т., Платова, Р. А. Идентификация кофе в зер- нах методами Фурье-ИК-спектроскопии и многомерного анализа // Известия высших учебных заведений. Пищевая технология. 2021. № 5–6 (383–384). С. 92–97 https://doi. org/10.26297/0579-3009.2021.5-6.17. 3. Садовой, В. В. Разработка научных принципов проектирования состава и совер- шенствования технологии многокомпонентных мясных изделий с использованием вто- ричных ресурсов пищевой промышленности: автореф. дис. д-ра техн. наук: 05.18.04. Ставрополь, 2007. 38 с. 4. Abbasi-Tarighat, M., Shahbazi, E., Niknam, K. (2013) Simultaneous determination of Mn2+ and Fe3+ as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol) complexes in some foods, vegetable and water samples by artificial neural networks Food Chemistry, vol. 138, is. 2–3, pр. 991–997. https://doi.org/10.1016/j.foodchem.2012.09.099 5. Batista, L., Marques, C., Pires, A., A. Minim, L., Soares, N., Vidigal, M. (2021) Artificial neural networks modeling of non-fat yogurt texture properties: effect of process conditions and food composition, Food and Bioproducts Processing, vol. 126, pp. 164–174 https://doi.org/10.1016/j.fbp.2021.01.002. 6. Chakravartula, S., Moscetti, R., Bedini, G., Nardella, M., Massantini, R. (2022) Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee, Food Control, vol. 135 https://doi.org/10.1016/j.foodcont.2022.108816. 7. Cotrim, W. da Silva, Felix, L. B, Minim, V. P. R, Campos R. C., Minim, L. A. (2021) Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing Chemical Engineering Science, vol. 240, pp. 116679. ISSN 0009-2509. https://doi.org/10.1016/j.ces.2021.116679 8. Chaitanya, A., Shetty, J., Chiplunkar, P. (2023) Food Image Classification and Data Extraction Using Convolutional Neural Network and Web Crawlers, Procedia Computer Science, vol. 218, pp. 143–152. https://doi.org/10.1016/j.procs.2022.12.410 9. Chen, D., Guo, C., Lu, W., Zhang, C., Xiao, C. (2023) Rapid quantification of royal jelly quality by mid-infrared spectroscopy coupled with backpropagation neural network Food Chemistry, vol. 418. https://doi.org/10.1016/j.foodchem.2023.135996 10. O’Farrell M., Lewis E., Flanagan C., Lyons W., Jackman N. Combining principal component analysis with an artificial neural network to perform online quality assessment of food as it cooks in a large-scale industrial oven. Sensors and Actuators B: Chemical. 2005. Volume 107. Issue 1. Pages 104-112. https://doi.org/10.1016/j.snb.2004.09.050. 11. Dębska, B., Guzowska-Świder, B. (2011) Application of artificial neural network in food classification Analytica Chimica Acta, vol. 705, is. 1–2, pp. 283–291 https://doi.org/10.1016/j.aca.2011.06.033 12. Furtado, P., Caldeira, M., Martins, P. (2020) Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison // Computer Vision and Image Understanding, vol. 191 https://doi.org/10.1016/j.cviu.2019.102878 13. Gonzаlez-Viveros, N., Gоmez-Gil, P., Castro-Ramos, J., Cerecedo-Nunez, H. (2021) On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks Food Chemistry, vol. 352. https://doi.org/10.1016/j.foodchem.2021.129375. 14. Kowalski, R., Pietrysiak, E., Ganjyal, G. (2021) Optimizing screw profiles for twin-screw food extrusion processing through genetic algorithms and neural networks, Journal of Food Engineering, vol., 303. https://doi.org/10.1016/j.jfoodeng.2021.110589 15. Kupongsak, S., Tan, J. (2006) Application of fuzzy set and neural network techniques in determining food process control set points Fuzzy Sets and Systems, vol., 157, is. 9, pp. 1169–1178. https://doi.org/10.1016/j.fss.2005.12.015 16. Lee, G., Kim, R., Lim, T., Kim, J., Kim, S., Kim, H., Taek, Hwang K. (2022) Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artificial neural network coupled with genetic algorithm, Food Chemistry, vol. 396. https://doi.org/10.1016/j.foodchem.2022.133712 17. Lewis, E., Sheridan, C., O’Farrell, M., Flanagan, C., Kerry, J., Jackman, N. (2008) Optical fibre sensors for assessing food quality in full scale production ovens – a principal component analysis and artificial neural network based approach Nonlinear Analysis: Hybrid Systems, vol. 2, is. 1, pp. 51–57 https://doi.org/10.1016/j.nahs.2006.05.008 18. Li, Y., Fei, C., Mao, C., Ji D., Gong, J., Qin, Y., Qu, L., Zhang, W., Bian, Z., Su, L., Lu, T. (2022) Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques, Food Chemistry, vol. 374. https://doi.org/10.1016/j.foodchem.2021.131658 19. Nayak, J., Vakula, K., Dinesh, P., Naik, B., Pelusi, D. (2020) Intelligent food processing: Journey from artificial neural network to deep learning, Computer Science Review, vol. 38. https://doi.org/10.1016/j.cosrev.2020.100297 20. Silva, C. E. Tanajura da, Filardi, V. L., Pepe, I. M., Chaves, M. A., Santos, C. M. S. (2015) Classification of food vegetable oils by fluorimetry and artificial neural networks Food Control, vol. 47, pp. 86–91. https://doi.org/10.1016/j.foodcont.2014.06.030 21. Senizza, B., Ganugi, P., Trevisan, M., Lucini, L. (2023) Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure, Food Chemistry, vol. 404, part A. https://doi.org/10.1016/j.foodchem.2022.134543 22. Sathish, S., Ashwin, S., Quadir, Md., Pavithra, L. (2022) Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories, Materials Today: Proceedings, vol. 62, part 7, pp. 4665–4670. https://doi.org/10.1016/j.matpr.2022.03.122 23. Soni, A., Al-Sarayreh, M., Reis, M., Brightwell, G. (2021) Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model, Food Research International, vol. 147 https://doi.org/10.1016/j.foodres.2021.110577