Articles

Artificial intelligence in the real sector of economy (on the example of the food industry)

Received: 08.05.2024 Accepted: 23.05.2024 Published: 15.03.2024

DOI: 10.22394/1818-4049-2024-107-2-51-63

Full text

Abstract

The study is based on the hypothesis that the implementation of artifi intelligence into the real sector of economy is inevitable. The authors consider the regulatory framework of artifi intelligence as well as its historical aspects. The basic trajectories of the artifi intelligence implementation on the example of the food industry (modeling and optimizing food technologies, identifi assessing the quality and safety of food products) are indicated. It was noted that the prerequisite for implementation of artifi 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 artifi intelligence technologies can signifi optimize the resource of time, increase the effi 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 fi of artifi 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 artifi intelligence application, its advantages and possibilities, considers promising areas for further penetration of artifi intelligence in the food industry.

Keywords

Received: 8 May 2024 · Revised: 21 May 2024 · Accepted: 23 May 2024

How to cite

Для цитирования:

Алешков А.В., Синюков В.А., Ивашкин М.В. Искусственный интеллект в реальном секторе экономики (на примере пищевой индустрии) // Власть и управление на Востоке России. 2024. Т. 17, № 2. С. 51–63. https://doi.org/10.22394/1818-4049-2024-107-2-51-63

For citation:

Aleshkov A.V., Sinyukov V.A., Ivashkin M.V. Artificial intelligence in the real sector of economy (on the example of the food industry) // Power and Administration in the East of Russia. 2024. Vol. 17, no. 2. P. 51–63. https://doi.org/10.22394/1818-4049-2024-107-2-51-63

How to Cite

Aleshkov A.V., Sinyukov V.A., Ivashkin M.V. Artificial intelligence in the real sector of economy (on the example of the food industry) // Power and Administration in the East of Russia. 2024. Vol. 17, no. 2. P. 51–63. https://doi.org/10.22394/1818-4049-2024-107-2-51-63

References

  1. 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. URL: https://elibrary.ru/download/elibrary_56751814_10676705.pdf (дата обращения 29.04.2024).
  2. 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.
  3. 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
  5. of Mn2+ and Fe3+ as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1Hpyrazol-
  6. 5-ol) complexes in some foods, vegetable and water samples by artifi cial neural
  7. networks Food Chemistry, vol. 138, is. 2–3, pр. 991–997. https://doi.org/10.1016/j.
  8. foodchem.2012.09.099. URL: https://www.sciencedirect.com/science/article/pii/
  9. S030881461201549X (дата обращения 07.07.2023).
  10. Batista, L., Marques, C., Pires, A., A. Minim, L., Soares, N., Vidigal, M. (2021) Artifi
  11. cial neural networks modeling of non-fat yogurt texture properties: effect of process
  12. conditions and food composition, Food and Bioproducts Processing, vol. 126, pp. 164–174
  13. https://doi.org/10.1016/j.fbp.2021.01.002. URL: https://www.sciencedirect.com/science/
  14. article/pii/S0960308521000043) (дата обращения 07.05.2024).
  15. Chakravartula, S., Moscetti, R., Bedini, G., Nardella, M., Massantini, R. (2022) Use
  16. of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food
  17. adulteration: A case study on coffee, Food Control, vol. 135 https://doi.org/10.1016/j.
  18. foodcont.2022.108816. URL: https://www.sciencedirect.com/science/article/pii/
  19. S0956713522000093 (дата обращения 07.07.2023).
  20. Cotrim, W. da Silva, Felix, L. B, Minim, V. P. R, Campos R. C., Minim, L. A. (2021)
  21. Development of a hybrid system based on convolutional neural networks and support
  22. vector machines for recognition and tracking color changes in food during thermal processing
  23. Chemical Engineering Science, vol. 240, pp. 116679. ISSN 0009-2509. https://
  24. doi.org/10.1016/j.ces.2021.116679. URL: https://www.sciencedirect.com/science/article/
  25. pii/S000925092100244X) (дата обращения 07.05.2024).
  26. Chaitanya, A., Shetty, J., Chiplunkar, P. (2023) Food Image Classifi cation and Data
  27. Extraction Using Convolutional Neural Network and Web Crawlers, Procedia Computer
  28. Science, vol. 218, pp. 143–152. https://doi.org/10.1016/j.procs.2022.12.410
  29. Chen, D., Guo, C., Lu, W., Zhang, C., Xiao, C. (2023) Rapid quantifi cation of royal
  30. jelly quality by mid-infrared spectroscopy coupled with backpropagation neural network
  31. Food Chemistry, vol. 418. https://doi.org/10.1016/j.foodchem.2023.135996 URL:
  32. https://www.sciencedirect.com/science/article/pii/S0308814623006131 (дата обра-
  33. щения 07.07.2023).
  34. O’Farrell M., Lewis E., Flanagan C., Lyons W., Jackman N. Combining principal
  35. component analysis with an ar tifi cial neural network to perform online quality assessment
  36. of food as it cooks in a large-scale industrial oven. Sensors and Actuators B: Chemical.
  37. 2005. Volume 107. Issue 1. Pages 104-112. https://doi.org/10.1016/j.snb.2004.09.050.
  38. Электронный ресурс. Режим доступа https://www.sciencedirect.com/science/article/
  39. pii/S0925400505000870 (дата обращения 29.04.2024 г.).
  40. Dębska, B., Guzowska-Świder, B. (2011) Application of artifi cial neural network
  41. in food classifi cation Analytica Chimica Acta, vol. 705, is. 1–2, pp. 283–291 https://doi.
  42. org/10.1016/j.aca.2011.06.033. URL: https://www.sciencedirect.com/science/article/
  43. pii/S0003267011008622 (дата обращения 07.07.2023).
  44. Furtado, P., Caldeira, M., Martins, P. (2020) Human Visual System vs Convolution
  45. Neural Networks in food recognition task: An empirical comparison // Computer Vision
  46. and Image Understanding, vol. 191 https://doi.org/10.1016/j.cviu.2019.102878. URL:
  47. https://www.sciencedirect.com/science/article/pii/S1077314219301675 (дата обра-
  48. щения 07.05.2024).
  49. Gonzаlez-Viveros, N., Gоmez-Gil, P., Castro-Ramos, J., Cerecedo-Nunez, H. (2021)
  50. 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.
  51. URL: https://www.sciencedirect.com/science/article/pii/S0308814621003812 (дата
  52. обращения 07.07.2023).
  53. Kowalski, R., Pietrysiak, E., Ganjyal, G. (2021) Optimizing screw profi les for twin-screw
  54. food extrusion processing through genetic algorithms and neural networks, Journal of Food
  55. Engineering, vol., 303. https://doi.org/10.1016/j.jfoodeng.2021.110589. URL: https://
  56. www.sciencedirect.com/science/article/abs/pii/S026087742100114X?via%3Dihub
  57. (дата обращения 07.05.2024).
  58. Kupongsak, S., Tan, J. (2006) Application of fuzzy set and neural network techniques
  59. in determining food process control set points Fuzzy Sets and Systems, vol., 157, is. 9,
  60. pp. 1169–1178. https://doi.org/10.1016/j.fss.2005.12.015. URL: https://www.sciencedirect.
  61. com/science/article/pii/S0165011405006123 (дата обращения 07.05.2024).
  62. Lee, G., Kim, R., Lim, T., Kim, J., Kim, S., Kim, H., Taek, Hwang K. (2022) Optimization
  63. of accelerated solvent extraction of ellagitannins in black raspberry seeds using
  64. artifi cial neural network coupled with genetic algorithm, Food Chemistry, vol. 396.
  65. https://doi.org/10.1016/j.foodchem.2022.133712. URL: https://www.sciencedirect.
  66. com/science/article/pii/S0308814622016740 (дата обращения 07.07.2023).
  67. Lewis, E., Sheridan, C., O’Farrell, M., Flanagan, C., Kerry, J., Jackman, N. (2008)
  68. Optical fi bre sensors for assessing food quality in full scale production ovens – a principal
  69. component analysis and artifi cial neural network based approach Nonlinear Analysis:
  70. Hybrid Systems, vol. 2, is. 1, pp. 51–57 https://doi.org/10.1016/j.nahs.2006.05.008.
  71. URL: https://www.sciencedirect.com/science/article/pii/S1751570X07000878) (дата
  72. обращения 07.05.2024).
  73. Li, Y., Fei, C., Mao, C., Ji D., Gong, J., Qin, Y., Qu, L., Zhang, W., Bian, Z., Su, L.,
  74. Lu, T. (2022) Physicochemical parameters combined fl ash GC e-nose and artifi cial neural
  75. network for quality and volatile characterization of vinegar with different brewing techniques,
  76. Food Chemistry, vol. 374. https://doi.org/10.1016/j.foodchem.2021.131658.
  77. URL: https://www.sciencedirect.com/science/article/pii/S0308814621026649) (дата
  78. обращения 07.07.2023).
  79. Nayak, J., Vakula, K., Dinesh, P., Naik, B., Pelusi, D. (2020) Intelligent food processing:
  80. Journey from artifi cial neural network to deep learning, Computer Science Review,
  81. vol. 38. https://doi.org/10.1016/j.cosrev.2020.100297. URL: https://www.sciencedirect.
  82. com/science/article/pii/S157401372030397X (дата обращения 29.04.2024).
  83. Silva, C. E. Tanajura da, Filardi, V. L., Pepe, I. M., Chaves, M. A., Santos, C. M. S.
  84. (2015) Classifi cation of food vegetable oils by fl uorimetry and artifi cial neural networks
  85. Food Control, vol. 47, pp. 86–91. https://doi.org/10.1016/j.foodcont.2014.06.030. URL:
  86. https://www.sciencedirect.com/science/article/pii/S0956713514003594) (дата обра-
  87. щения 07.05.2024).
  88. Senizza, B., Ganugi, P., Trevisan, M., Lucini, L. (2023) Combining untargeted
  89. profi ling of phenolics and sterols, supervised multivariate class modelling and artifi
  90. cial neural networks for the origin and authenticity of extra-virgin olive oil: A case
  91. study on Taggiasca Ligure, Food Chemistry, vol. 404, part A. https://doi.org/10.1016/j.
  92. foodchem.2022.134543. URL: https://www.sciencedirect.com/science/article/pii/
  93. S0308814622025055 (дата обращения 07.07.2023).
  94. Sathish, S., Ashwin, S., Quadir, Md., Pavithra, L. (2022) Analysis of Convolutional
  95. Neural Networks on Indian food detection and estimation of calories, Materials Today: Proceedings,
  96. vol. 62, part 7, pp. 4665–4670. https://doi.org/10.1016/j.matpr.2022.03.122
  97. Soni, A., Al-Sarayreh, M., Reis, M., Brightwell, G. (2021) Hyperspectral imaging and
  98. deep learning for quantifi cation of Clostridium sporogenes spores in food products using
  99. 1D- convolutional neural networks and random forest model, Food Research International,
  100. vol. 147 https://doi.org/10.1016/j.foodres.2021.110577. URL: https://www.sciencedirect.
  101. com/science/article/pii/S0963996921004762 (дата обращения 07.07.2023).
  102. Aleshkov, A. V., Ivashkin, M. V., Sinyukov, V. A. (2023) Innovation management in the food industry using neural networks, Vestnik Tikhookeanskogo gosudarstvennogo universiteta [Bulletin of Pacific national university], no. 4 (71), pp. 97–104 (in Russ.) URL: https://elibrary.ru/download/elibrary_56751814_10676705.pdf (accessed: 05.02.2024).
  103. Metlenkin, D. A., Platov, Yu. T., Platova, R. A. (2021) Identification of coffee beans using Fourier-IR spectroscopy and multidimensional analysis methods Izvestiya vysshikh uchebnykh zavedenii. Pishchevaya tekhnologiya [News of higher educational institutions. Food technology], no. 5–6 (383–384), pp. 92–97 (in Russ.) https://doi.org/10.26297/0579-3009.2021.5-6.17.
  104. Sadovoy, V. V. (2007) Development of scientific principles for designing the composition and improving the technology of multi-component meat products using secondary resources of the food industry: abstract of thesis. dis. Doctor of Food Science: 05.18.04. Stavropol, 38 p. (in Russ.)
  105. Abbasi-Tarighat, M., Shahbazi, E., Niknam, K. (2013) Simultaneous determination
  106. of Mn2+ and Fe3+ as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1Hpyrazol-
  107. 5-ol) complexes in some foods, vegetable and water samples by artifi cial neural
  108. networks Food Chemistry, vol. 138, is. 2–3, pр. 991–997. https://doi.org/10.1016/j.
  109. foodchem.2012.09.099. URL: https://www.sciencedirect.com/science/article/pii/
  110. S030881461201549X (accessed: 07.07.2023).
  111. Batista, L., Marques, C., Pires, A., A. Minim, L., Soares, N., Vidigal, M. (2021) Artifi
  112. cial neural networks modeling of non-fat yogurt texture properties: effect of process
  113. conditions and food composition, Food and Bioproducts Processing, vol. 126, pp. 164–174
  114. https://doi.org/10.1016/j.fbp.2021.01.002. URL: https://www.sciencedirect.com/science/
  115. article/pii/S0960308521000043) (accessed: 07.05.2024).
  116. Chakravartula, S., Moscetti, R., Bedini, G., Nardella, M., Massantini, R. (2022) Use
  117. of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food
  118. adulteration: A case study on coffee, Food Control, vol. 135 https://doi.org/10.1016/j.
  119. foodcont.2022.108816. URL: https://www.sciencedirect.com/science/article/pii/
  120. S0956713522000093 (accessed: 07.07.2023).
  121. Cotrim, W. da Silva, Felix, L. B, Minim, V. P. R, Campos R. C., Minim, L. A. (2021)
  122. Development of a hybrid system based on convolutional neural networks and support
  123. vector machines for recognition and tracking color changes in food during thermal processing
  124. Chemical Engineering Science, vol. 240, pp. 116679. ISSN 0009-2509. https://
  125. doi.org/10.1016/j.ces.2021.116679. URL: https://www.sciencedirect.com/science/article/
  126. pii/S000925092100244X) (accessed: 07.05.2024).
  127. Chaitanya, A., Shetty, J., Chiplunkar, P. (2023) Food Image Classifi cation and Data
  128. Extraction Using Convolutional Neural Network and Web Crawlers, Procedia Computer
  129. Science, vol. 218, pp. 143–152. https://doi.org/10.1016/j.procs.2022.12.410
  130. Chen, D., Guo, C., Lu, W., Zhang, C., Xiao, C. (2023) Rapid quantifi cation of royal jelly
  131. quality by mid-infrared spectroscopy coupled with backpropagation neural network Food
  132. Chemistry, vol. 418. https://doi.org/10.1016/j.foodchem.2023.135996 URL: https://
  133. www.sciencedirect.com/science/article/pii/S0308814623006131 (accessed: 07.07.2023).
  134. O’Farrell M., Lewis E., Flanagan C., Lyons W., Jackman N. Combining principal
  135. component analysis with an ar tifi cial neural network to perform online quality assessment
  136. of food as it cooks in a large-scale industrial oven. Sensors and Actuators B: Chemical.
  137. 2005. Volume 107. Issue 1. Pages 104-112. https://doi.org/10.1016/j.snb.2004.09.050.
  138. Электронный ресурс. Режим доступа https://www.sciencedirect.com/science/article/
  139. pii/S0925400505000870 (accessed: 29.04.2024).
  140. Dębska, B., Guzowska-Świder, B. (2011) Application of artifi cial neural network
  141. in food classifi cation Analytica Chimica Acta, vol. 705, is. 1–2, pp. 283–291 https://doi.
  142. org/10.1016/j.aca.2011.06.033. URL: https://www.sciencedirect.com/science/article/pii/S0003267011008622 (accessed: 07.07.2023).
  143. Furtado, P., Caldeira, M., Martins, P. (2020) Human Visual System vs Convolution
  144. Neural Networks in food recognition task: An empirical comparison // Computer Vision and
  145. Image Understanding, vol. 191 https://doi.org/10.1016/j.cviu.2019.102878. URL: https://
  146. www.sciencedirect.com/science/article/pii/S1077314219301675 (accessed: 07.05.2024).
  147. Gonzаlez-Viveros, N., Gоmez-Gil, P., Castro-Ramos, J., Cerecedo-Nunez, H. (2021)
  148. On the estimation of sugars concentrations using Raman spectroscopy and artifi cial neural
  149. networks Food Chemistry, vol. 352. https://doi.org/10.1016/j.foodchem.2021.129375.
  150. URL: https://www.sciencedirect.com/science/article/pii/S0308814621003812 (accessed:
  151. 07.07.2023).
  152. Kowalski, R., Pietrysiak, E., Ganjyal, G. (2021) Optimizing screw profi les for twin-screw
  153. food extrusion processing through genetic algorithms and neural networks, Journal of Food
  154. Engineering, vol., 303. https://doi.org/10.1016/j.jfoodeng.2021.110589. URL: https://
  155. www.sciencedirect.com/science/article/abs/pii/S026087742100114X?via%3Dihub (accessed:
  156. 07.05.2024).
  157. Kupongsak, S., Tan, J. (2006) Application of fuzzy set and neural network techniques
  158. in determining food process control set points Fuzzy Sets and Systems, vol., 157,
  159. is. 9, pp. 1169–1178. https://doi.org/10.1016/j.fss.2005.12.015. URL: https://www.
  160. sciencedirect.com/science/article/pii/S0165011405006123 (accessed: 07.05.2024).
  161. Lee, G., Kim, R., Lim, T., Kim, J., Kim, S., Kim, H., Taek, Hwang K. (2022) Optimization
  162. of accelerated solvent extraction of ellagitannins in black raspberry seeds using
  163. artifi cial neural network coupled with genetic algorithm, Food Chemistry, vol. 396.
  164. https://doi.org/10.1016/j.foodchem.2022.133712. URL: https://www.sciencedirect.
  165. com/science/article/pii/S0308814622016740 (accessed: 07.07.2023).
  166. Lewis, E., Sheridan, C., O’Farrell, M., Flanagan, C., Kerry, J., Jackman, N. (2008)
  167. Optical fi bre sensors for assessing food quality in full scale production ovens – a principal
  168. component analysis and artifi cial neural network based approach Nonlinear Analysis:
  169. Hybrid Systems, vol. 2, is. 1, pp. 51–57 https://doi.org/10.1016/j.nahs.2006.05.008.
  170. URL: https://www.sciencedirect.com/science/article/pii/S1751570X07000878) (accessed:
  171. 07.05.2024).
  172. Li, Y., Fei, C., Mao, C., Ji D., Gong, J., Qin, Y., Qu, L., Zhang, W., Bian, Z., Su, L.,
  173. Lu, T. (2022) Physicochemical parameters combined fl ash GC e-nose and artifi cial neural
  174. network for quality and volatile characterization of vinegar with different brewing techniques,
  175. Food Chemistry, vol. 374. https://doi.org/10.1016/j.foodchem.2021.131658.
  176. URL: https://www.sciencedirect.com/science/article/pii/S0308814621026649) (accessed:
  177. 07.07.2023).
  178. Nayak, J., Vakula, K., Dinesh, P., Naik, B., Pelusi, D. (2020) Intelligent food processing:
  179. Journey from artifi cial neural network to deep learning, Computer Science Review,
  180. vol. 38. https://doi.org/10.1016/j.cosrev.2020.100297. URL: https://www.sciencedirect.
  181. com/science/article/pii/S157401372030397X (accessed: 29.04.2024).
  182. Silva, C. E. Tanajura da, Filardi, V. L., Pepe, I. M., Chaves, M. A., Santos, C. M. S.
  183. (2015) Classifi cation of food vegetable oils by fl uorimetry and artifi cial neural networks
  184. Food Control, vol. 47, pp. 86–91. https://doi.org/10.1016/j.foodcont.2014.06.030.
  185. URL: https://www.sciencedirect.com/science/article/pii/S0956713514003594) (accessed:
  186. 07.05.2024).
  187. Senizza, B., Ganugi, P., Trevisan, M., Lucini, L. (2023) Combining untargeted
  188. profi ling of phenolics and sterols, supervised multivariate class modelling and artifi
  189. cial neural networks for the origin and authenticity of extra-virgin olive oil: A case
  190. study on Taggiasca Ligure, Food Chemistry, vol. 404, part A. https://doi.org/10.1016/j.
  191. foodchem.2022.134543. URL: https://www.sciencedirect.com/science/article/pii/
  192. S0308814622025055 (accessed: 07.07.2023).
  193. Sathish, S., Ashwin, S., Quadir, Md., Pavithra, L. (2022) Analysis of Convolutional
  194. 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
  195. Soni, A., Al-Sarayreh, M., Reis, M., Brightwell, G. (2021) Hyperspectral imaging
  196. and deep learning for quantifi cation of Clostridium sporogenes spores in food products
  197. using 1D- convolutional neural networks and random forest model, Food Research International,
  198. vol. 147 https://doi.org/10.1016/j.foodres.2021.110577. URL: https://www.
  199. sciencedirect.com/science/article/pii/S0963996921004762 (accessed: 07.07.2023).