Personalized nutrition and “digital twins” of food


  • Marina Nikitina V. M. Gorbatov Federal Research Centre for Food Systems of RAS, Centre of Economic and Analytical Research and Information Technologies, Direction of Information Technologies, Talalikhina str., 26, 109316, Moscow, Russia, Tel.: +74956769214
  • Irina Chernukha Irina Chernukha, V. M. Gorbatov Federal Research Centre for Food Systems of RAS, Experimental Clinic ”˜Biologically Active Substances of an Animal Origin” Laboratory, Talalikhina str., 26, 109316, Moscow, Russia, Tel.: +74956769718



simulation model, personalized nutrition, digital twin, food product, integral indicator


Mathematization of research is one of the most effective methods of virtual substantiation of foodstuff recipe and technology. This approach allows creating a product that meets consumer's individual needs, i.e. personalized foodstuff (ethnicity, cultural preferences, regional and environmental characteristics, lifestyle), and at the same time reducing the time and cost of decision-making. The article discusses the hypothesis that the “digital twin” of a food product is a virtual model of the product, namely its mathematical model (simulation model). A simulation model is a logical and mathematical description of a food product that is used to conduct a computerized experiment in order to design desired characteristics and properties. The “digital twin” combines all variety of factors from chemical composition, functional and technological properties to organoleptic indicators. The application of the “digital twin” model of the foodstuff will allow: (1) reacting quickly to changes in the composition, properties and types of raw ingredients, (2) adjusting the product recipe in response to changes in consumer preferences, (3) designing products with a given chemical composition, nutritional value and functional orientation, (4) creating functional, specialized products taking into account the metabolism of nutrients (ethnicity, cultural preferences, health status and clinical factors). Products adapted to the needs of small categories of people will help reducing the risks for those who already have diseases, and will meet the needs of those who would like to make their diet more appropriate to individual needs. The proposed approach to creating a model of the “digital twin” of the foodstuff includes several stages. The first stage involves optimization of the nutritional and biological value of the designed product. The second stage is related to designing the food product’s structural forms. But even if the recipe of a food product is optimally selected in the first stage, it does not guarantee its transformation during processing into a stable system with the required structural, mechanical, functional and technological parameters. Evaluation of the developed food product’s efficiency is possible only by analysing numerous and various parameters and indicators. It is convenient to generalize (convolute) many parameters and indicators into a single quantitative dimensionless indicator. To assess the quality and adequacy of the food product, it is suggested to use an integral indicator in the form of additive convolution – the ‘functional’ of the food product quality.


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Bolton, R. N., McColl-Kennedy, J. R., Cheung, L., Gallan, A., Orsingher, C., Witell, L., Zaki, M. 2018. Customer experience challenges: bringing together digital, physical and social realms. Journal of Service Management, vol. 29, no. 5, p. 766-808. DOI:

Červenka, L., Frühbauerová, M., Velichová, H. 2019. Functional properties of muffin as affected by substituting wheat flour with carob powder. Potravinarstvo, vol. 13, no. 1, p. 212-217. DOI:

Claessgen, E., Stargel, D. 2012. The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, vol. 8, p. 7247-7260. DOI:

Chernukha, I., Fedulova, L., Kotenkova, E. 2018. Hypolipidemic action of the meat product: in vivo study. Potravinarstvo, vol. 12, no. 1, p. 566-569. DOI:

El Saddik, A. 2018. Digital twins: the convergence of multimedia technologies. IEEE MultiMedia, vol. 25, no. 2, p. 87-92. DOI:

Grieves, M. W. 2014. Digital twin: manufacturing excellence through virtual indicatory replication. LLC, 7 p.

Harrington, E. C. Jr. 1965. The desirability Function. Industrial Quality Control, vol. 21, no. 10, p. 494-498.

Ivashkin, Y. A., Nikitina, M. A. 2016. Structural-parametric model of adequate nutrition (Структурно-параметрическая модель адекватного питания). Mathematical Methods in Processes and Technologies - ММТТ, vol. 9, no. 91, p. 151-154. (In Russian)

Ivashkin, Y. A. 2016. Multiagent modeling in Simplex3 simulation system (Мультиагентное моделирование в имитационной системе Simplex3). Moscow: Binom, Laboratory of Knowledge, 350 p. (In Russian).

Karpov, Y. G. 2005. Simulation of systems. Introduction to modeling with AnyLogic 5 (Имитационное моделирование систем. Введение в моделирование с AnyLogic 5). Saint Petersburg: BHV-Saint Petersburg, 400 p. (In Russian).

Kotenkova, E., Chernucha, I. 2019. Influence of technological processing on lipid-lowering activity of substances containing in porcine hearts and aortas. Potravinarstvo Slovak Journal of Food Sciences, vol. 13, no. 1, p. 331-336. DOI:

Lee, J., Bagheri, B., Kao, H. A. 2015. A cyber-physical systems architecture for industry 4.0-based manufacturing system. Manufacturing Letters, vol. 3, p. 18-23. DOI:

Mastyaeva, I. N., Goremykina, G. I., Semenikhina, O. N. 2016. Methods of optimal solutions (Методы оптимальных решений). Moscow: KURS, SRC INFRA-М, 384 p. (In Russian)

Mukha, V. S. 2010. Computational methods and computer algebra (Вычислительные методы и компьютерная алгебра). 2nd ed., rev. and add. Minsk: BSUIR, 148 p. (In Russian)

Musina O., Rashidinejad A., Putnik P., Barba F. J., Abbaspourrad A., Greiner R., Roohinejad S. 2018. The use of whey protein extract for manufacture of a whipped frozen dairy dessert. Mljekarstvo, vol. 68, no. 4, p. 254-271. DOI:

Nikitina, M. A., Chernukha, I. M., Nurmukhanbetova, D. E. 2019. Principal approaches to design and optimization of a diet for target groups of consumers. News of the National Academy of Sciences of the Republic of Kazakhstan. Series of Geology and Engineering, vol. 1, no. 433, p. 231-241. DOI:

Ordovas, J. M., Ferguson, L. R., Tai, E. S., Mathers, J. C. 2018. Personalised nutrition and health. BMJ, 361 bmj k2173. DOI:

Schmidt, B. 2001. The Art of Modelling and Simulation. SCS-Europe BVBA, Chent: Belgium, 480 p.

Stanke, M., Zettel, V., Hitzmann B. 2014. Measurement and mathematical modeling of the relative volume of wheat dough during proofing. Journal of Food Engineering, vol. 131, p. 58-64. DOI:

Söderberg, R., Wärmefjord, K., Carlson, J. S., Lindkvist, L. 2017. Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annals, vol. 66, no. 1, p. 137-140. DOI:

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y., Nee, A. Y. C. 2018. Digital twin-driven product design framework. International Journal of Production Research, vol. 2, p. 1-19. DOI:

Zharinov, A. I. 1994. Fundamentals of modern meat processing technologies. Part 1. Emulsified and coarsely ground meat products (Основы современных технологий переработки мяса Ч. 1 Эмульгированные и грубоизмельченные мясопродукты). Voyakina, M. P. Moscow: Itar-TASS, 154 p. (In Russian)



How to Cite

Nikitina, M., & Chernukha, I. (2020). Personalized nutrition and “digital twins” of food. Potravinarstvo Slovak Journal of Food Sciences, 14, 264–270.

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