Preview

Theory and practice of meat processing

Advanced search

Intelligent engineering of a specialized meat-based product

https://doi.org/10.21323/2414-438X-2026-11-2-166-179

Abstract

This article discusses the development and application of a digital twin (DT) for intelligent engineering of a specialized meatbased product. The focus is on the use of advanced intelligent optimization methods to create a virtual model capable of adaptively real time managing the formula and process parameters, in accordance with the product’s final purpose. To solve a food engineering problem (optimization of product formula and production modes), three metaheuristic intelligent algorithms were applied and compared: genetic algorithm (GA), particle swarm optimization (PSO), and sparrow search algorithm (SSA). The results of the comparative analysis demonstrated that SSA algorithm provided the best accuracy and convergence in solving the assigned optimi zation problems, making it the preferred tool for integration into the digital twin system. SSA algorithm demonstrates the ability to escape local optima, thereby avoiding the problem of premature convergence typical for some metaheuristic methods, such as GA and PSO algorithms. Based on an optimized digital twin, this approach enables dynamic prediction of physicochemical pa rameters, their compliance with the established medical and biological requirements for the final product, promptly adjusting its formula to meet the intended purpose of providing complete nutrition and enhancing the rehabilitation of patients with traumatic brain injuries (TBI), and ensures flexibility when scaling the technology or transferring production to another facility. Digital twins, integrating data from smart sensors and biosensors based on big data analysis, are the foundation for creating a safe, trace able, and adaptive food system. The study demonstrates that intelligent engineering based on a digital twin is a key technology for creating personalized, safe, and effective specialized food products within the framework of modern manufacturing principles and One Health concept.

About the Authors

M. A. Nikitina
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Marina A. Nikitina, Doctor of Technical Sciences, Docent, Leading Scientific Worker, Head of the Direction of Information Technologies of the Center of Economic and Analytical Research and Information Technologies

26, Talalikhin str., 109316, Moscow



I. M. Chernukha
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Irina M. Chernukha, Doctor of Technical Sciences, Professor, Academician of the Russian Academy of Sciences, Head of the Department for Coordination of Initiative and International Projects

26, Talalikhin str., 109316, Moscow



A. B. Lisitsyn
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Andrey B. Lisitsyn, Doctor of Technical Sciences, Professor, Academician of the Russian Academy of Sciences, Scientific Supervisor

26, Talalikhin str., 109316, Moscow



A. S. Dydykin
V. M. Gorbatov Federal Research Center for Food Systems
Russian Federation

Andrey S. Dydykin, Doctor of Technical Sciences, Docent, Head of the Department Functional and Specialized Nutrition

26, Talalikhin str., 109316, Moscow



References

1. Zeng, F., Zhang, M., Law, C.L., Lin, J. (2025). Harnessing artificial intelligence for advancements in Rice/wheat functional food research and development. Food Research International , 209, Article 116306. https://doi.org/10.1016/j.foodres.2025.116306

2. Xia, B., Abidin, M.R.Z., Karim, S.A. (2024). From tradition to technology: A comprehensive review of contemporary food design. International Journal of Gastronomy and Food Science , 37, Article 100977. https://doi.org/10.1016/j.ijgfs.2024.100977

3. Lončar, B., Pezo, L., Knežević, V., Nićetin, M., Filipović, J., Petković, M. et al. (2024). Enhancing cookie formulations with combined dehydrated peach: A machine learning approach for technological quality assessment and optimization. Foods , 13(5), Article 782. https://doi.org/10.3390/foods13050782

4. Badaro, A.T., Amigo, J.M., Blasco, J., Aleixos, N., Ferreira, A.R., Clerici, M.T.P.S. et al. (2021). Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chemistry , 343, Article 128517. https://doi.org/10.1016/j.foodchem.2020.128517

5. Califano, G., Zhang, T., Spence, C. (2024). Would you trust an AI chef? Examining what people think when AI becomes creative with food. International Journal of Gastronomy and Food Science , 37(9), Article 100973. https://doi.org/10.1016/j.ijgfs.2024.100973

6. Ali, S., Mayo, S., Gostar, A.K., Tennakoon, R., BabHadiashar, A., MCann, T. et al. (2021). Automatic segmentation for synchrotronbased imaging of porous bread dough using deep learning approach. Journal of Synchrotron Radiation , 28(Pt 2), 566–575. https://doi.org/10.1107/S1600577521001314

7. Patil, N.S., Mote, G., Desai, V., Prathapan, K.P. et al. (2025). A comprehensive study of microstructural, functional, and nutritional quality of functional cookies utilizing finger millet and Jaggery, and optimization using simplex lattice mixture design and artificial neural networks. Journal of Food Mea surement and Characterization , 20, 2881–2893. https://doi.org/10.1007/s11694-025-03867-6

8. Lisitsyn, A. B., Chernukha, I. M., Nikitina, M. A. (2023). Development of a personalized diet using the structural optimization method. Food Systems , 6(1), 64–71. https://doi.org/10.21323/2618-9771-2023-6-1-64-71 (In Russian)

9. Ivashkin, Yu.A., Nikitina, M.A., Shchur, D.A. (2007). Modelling and optimization of an adequate feed in view of individual medical and biologic requirements. Storage and Process ing of Farm Products , 2, 71–74. (In Russian)

10. Türkmenoglu, C., Uyar, A. S. E., Kiraz, B. (2021). Recommending healthy meal plans by optimising natureinspired many-objective diet problem. Health Informat ics Journal , 27(1), Ar ticle 1460458220976719. https://doi.org/10.1177/1460458220976719

11. Razali, A. N. A. M., Bakar, E. M. N. E. A., Mahamud, K. R. K., Arbin, N., Rusiman, M. R. (2018). Malaysian menu planning model using self-adaptive hybrid genetic algorithm (SHGA). Far East Journal of Mathematical Sciences , 103(1), 171–190. http://doi.org/10.17654/ms103010171

12. Rani, S., Karnati, R., Patel, V., Ranganathaswamy, M.K., Tomar, P., Kataria, A. et al. (2026). AI-driven optimization techniques for smart sustainable manufacturing in Industry 5.0 ecosystem: A comprehensive review. Alexandria Engineering Journal , 137(2), 133–158. https://doi.org/10.1016/j.aej.2026.01.016

13. Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications, Production and Manufacturing Research: An Open Access Journal , 4(1), 23–45. http://doi.org/10.1080/21693277.2016.1192517

14. Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M.K., Gaur, V., Krolczyk, G.M. et al. (2023). Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. Journal of Intelligent Manufacturing , 34, 21–55. https://doi.org/10.1007/s10845022-02029-5

15. Li, Y., Carabelli, S., Fadda, E., Manerba, D., Tadei, R., Terzo, O. (2020). Machine learning and optimization for production rescheduling in Industry 4.0. The International Jour nal of Advanced Manufacturing Technology , 110, 2445–2463. https://doi.org/10.1007/s00170-020-05850-5

16. del Real Torres, A., Andreiana, D.S., Roldán, Á.O., Bustos, A.H., Galicia, L.E.A. (2022). A review of deep reinforcement learning approaches for smart manufacturing in industry 4.0 and 5.0 framework. Applied Sciences , 12(23), Article 12377. https://doi.org/10.3390/app122312377

17. Gen, M., Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-theart survey. Journal of Intelligent Manufacturing , 25, 849–866. https://doi.org/10.1007/s10845-013-0804-4

18. Gen, M., Lin, L., Zhang, H. (2009). Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey. Computers and Industrial Engineering , 56(3), 779–808. https://doi.org/10.1016/j.cie.2008.09.034

19. Scrucca, L. (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software , 53(4), 1–37. https://doi.org/10.18637/jss.v053.i04

20. Holland, J.H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor, Michigan: University of Michigan Press, 1975.

21. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: AddisonWesley, 1989.

22. Sivanandam, S.N., Deepa, S.N. (2007). Introduction to Genetic Algorithms. Berlin: SpringerVerlag, 2007.

23. Abualigah, L., Sheikhan, A., Ikotun, A.M., Zitar, R., Alsoud, A., AlShourbaji, I. et al. (2024). Particle swarm optimization algorithm: Review and applications. Chapter in a book: Metaheuristic Optimization Algorithms (Optimizers, Analysis, and Applications). Elsevier Inc., 2024. https://doi.org/10.1016/B978-0-443-13925-3.00019-4

24. Gad, A.G. (2022). Particle swarm optimization algorithm and its applications: A systematic review. Archives of Computational

25. Methods in Engineering , 29, 2531–2561. https://doi.org/10.1007/s11831-021-09694-4

26. Kennedy, J., Eberhart, R. (November 25, 1995). Particle swarm optimization . Proceedings of ICNN’95-international conference on neural networks. Perth, WA, Australia, 1995. https://doi.org/10.1109/ICNN.1995.488968

27. Shami, T.M., ElSaleh, A.A., Alswaitti, M., AlTashi, Q., Summakieh, M.A., Mirjalili, S. (2022). Particle swarm optimization: A comprehensive survey. IEEE Access , 10, 10031–10061. https://doi.org/10.1109/ACCESS.2022.3142859

28. Xue, J., Shen, B. (2020). A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science and Control Engineering , 8(1), 22–34. https://doi.org/10.1080/21642583.2019.1708830

29. Bhadani, R. (2021). Nonlinear Optimization in R using nlopt. https://doi.org/10.48550/arXiv.2101.02912

30. Kabacoff, R.I. (2022). R in Action: Data analysis and graphics with R and Tidyverse. New York: Manning, 2022.

31. Scrucca, L. (2017). On some extensions to GA package: Hybrid optimisation, parallelisation and islands evolution. The R Jour nal , 9(1), 187–206. https://doi.org/10.32614/RJ-2017-008

32. Holland, J.H. (1992). Genetic algorithms. Scientific American , 267(1), 66–72. http://doi.org/10.1038/scientificamerican0792-66

33. Goldberg, D.E., Holland, J.H. (1998). Genetic algorithms and machine learning. Machine Learning , 3(1), 95–99. http://doi.org/10.1023/a:1022602019183

34. Li, Q., Zeng, X., Wei, W. (2023). Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance. International Journal of Intelligent Computing and Cybernetics, 16(2), 250–276. http://doi.org/10.1108/IJICC-04-2022-0118

35. Aboud, A., Rokbani, N., Fdhila, R., Qahtani, A.M., Almutiry, O., Dhahri, H. et al. (2022). DPbMOPSO: A dynamic Pareto bi-level multiobjective particle swarm optimization algorithm. Applied Soft Computing , 129(2), 1–19, http://doi.org/10.1016/j.asoc.2022.109622

36. Hong, J., Shen, B., Xue, J., Pan, A. (2022). A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems. Applied Soft Computing , 131, Article 109777. https://doi.org/10.1016/j.asoc.2022.109777

37. Lisitsyn, A.B., Chernukha, I.M., Nikitina, M.A. (2021). Designing multicomponent food products. Moscow: MGUPP, 2021. (In Russian)

38. Aguilera, J.M. (2022). Rational food design and food microstructure. Trends in Food Science and Technology , 122, 256264. https://doi.org/10.1016/j.tifs.2022.02.006

39. Co, E.D., Peyronel, F., Yada, R.Y., Marangoni, A.G. (2012). Towards the rational design of foods: The 4th delivery of functionality in complex foods conference. Food and Func tion , 3(3), 200–201. https://doi.org/10.1039/c2fo90005j

40. Ubbink, J. (2012). Soft matter approaches to structured foods: From “cook-and-look” to rational food design? Faraday Dis cussions , 158, 9–35. https://doi.org/10.1039/C2FD20125A

41. Lisitsyn, A., Chernukha, I., Nikitina, M. (2020). Russian methodology for designing multicomponent foods in retrospect. Foods and Raw Materials , 8(1), 2–11. https://doi.org/10.21603/2308-4057-2020-1-2-11


Review

For citations:


Nikitina M.A., Chernukha I.M., Lisitsyn A.B., Dydykin A.S. Intelligent engineering of a specialized meat-based product. Theory and practice of meat processing. 2026;11(2):166-179. https://doi.org/10.21323/2414-438X-2026-11-2-166-179

Views: 44

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2414-438X (Print)
ISSN 2414-441X (Online)