Modeling of the composition of a probiotic fish product using the Python
https://doi.org/10.36038/0131-6184-2024-3-122-129
Abstract
Among the elements of a healthy diet that improve the performance of the human body, probiotics or probiotic foods are isolated, which contribute to correcting the composition of the internal indigenous microflora of the intestinal microbiota. In accordance with this, the article presents research on the design of models of the composition of a probiotic food fish product using the high-level Python programming language, as well as the development of technology for obtaining this type of product. The Python programming language using popular libraries such as SciPy and PuLP allows you to implement a linear programming method that solves similar problems related to the design of formulations of multicomponent food systems. As a result, 8 formulations of probiotic edible fish product of the group of semi-canned fish, in particular pates based on biotransformed bacterial starter cultures (L. acidophilus and S. thermophilus) fish fillets (pollock (Theragra chalcogramma), cod (Gadus macrocephalus), small-eyed macrurus (Albatrossia pectoralis), Gilbert’s half-shelled Goby (Hemilepidotus gilberti)) with the subsequent development of a technological scheme for obtaining this type of product. The designed formulations and the developed technology contribute to the production of a fish product with the presence of live forms of probiotics in the amount of 106-109 CFU/g.
About the Authors
E. V. Lavrukhina
Department of Innovative Technologies of the Department of Technical Regulation of the Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)
Russian Federation
Russian Federation
Elizaveta V. Lavrukhina – Senior Specialist
105187, Moscow, Okruzhnoy proezd, 19
N. Yu. Zarubin
Department of Innovative Technologies of the Department of Technical Regulation of the Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)
Russian Federation
Russian Federation
Nikita Yu. Zarubin – Candidate of Technical Sciences, Leading Researcher
105187, Moscow, Okruzhnoy proezd, 19
O. V. Bredikhina
Department of Innovative Technologies of the Department of Technical Regulation of the Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)
Russian Federation
Russian Federation
Olga V. Bredikhina – Doctor of Technical Sciences, Leading Researcher
105187, Moscow, Okruzhnoy proezd, 19
A. I. Grinevich
Department of Innovative Technologies of the Department of Technical Regulation of the Russian Federal Research Institute of Fisheries and Oceanography (VNIRO)
Russian Federation
Russian Federation
Alexandra I. Grinevich – Candidate of Technical Sciences, Senior Researcher
105187, Moscow, Okruzhnoy proezd, 19
References
1. Rathod N., Phadke G., Tabanelli G., Mane A., Ranveer D., Pagarkar A., Ozogul F. (2021). Recent advances in bio-preservatives impacts of lactic acid bacteria and their metabolites on aquatic food products/ // Food Biosci. Т. 44. P. 101440.
2. Giorgi G., Jiménez B., Novo V. (2023). «Linear Programming and Quadratic Programming». Pp. 275- 316. doi: 10.1007/978-3-031-30324-1_9.
3. Parab J., Lanjewar M., Sequeira M., Naik G., Shaikh A. (2023). Python Programming Recipes for IoT Applications. // Singapore: Springer Nature Singapore.
4. Downey A.B. (2015). Think Python: How to Think Like a Computer Scientist. // Published by O’Reilly Media, Inc. Pp 447.
5. Lavrukhina E., Zarubin N., Bredikhina O., Grinevich A. (2022). Integration of bacterial starter cultures with raw fish: selection and justification/ // Fisheries (Bethesda). 2022. Т. № 6. С. 107-114.
6. Lavruhina E., Zarubin N., Bredikhina O., Grinevich A., Mezhonov A. (2023). Optimal conditions and parameters verification of fish fillets muscle tissue biotransformation by bacterial starter cultures/ // Vestnik of Astrakhan State Technical University. Series: Fishing industry. Т. 2023. № 4. Pp. 127-138.
7. Baranenko D., Lu W., Golovinskaia O., Lepeshkin A., Ilina V. (2020). Optimization of baby food formulations using spreadsheets/ // IOP Conf Ser Mater Sci Eng. Т. 940. № 1. P. 012085.
8. Barlow E. (2023). Integer Linear Programming: Spreadsheet Solver Excellence Without Excel // INFORMS Transactions on Education.
9. Singh T., Pandey V., Dash K., Zanwar S., Singh R. (2023). Natural bio-colorant and pigments: Sources and applications in food processing // J Agric Food Res. P. 100628.
10. Nabi B., Mukhtar K., Ahmed W., Manzoor M., Ranjha M., Kieliszek M., Bhat Z. (2023). Natural pigments: Anthocyanins, carotenoids, chlorophylls, and betalains as colorants in food products // Food Biosci. P. 102403.
11. Brenner T., Tuvikene R., Parker A., Matsukawa S., Nishinari K. (2014). Rheology and structure of mixed kappa- carrageenan/iota-carrageenan gels/ // Food Hydrocoll. Т. 39. P. 272-279.
12. Phillips G. O., Williams P. A. (2009). Handbook of Hydrocolloids // Woodhead Publishing. P. 924.
13. Sarıyer S., Duranoğlu D., Doğan Ö., Küçük İ. (2020). pH-responsive double network alginate/kappa-carrageenan hydrogel beads for controlled protein release: Effect of pH and crosslinking agent/ // J Drug Deliv Sci Technol. Т. 56. С. 101551.
14. Zarubin N.Yu., Lavruhina E.V., Bredihina O.V., Grinevich A.I. (2023). Prognozirovanie parametrov biotransformacii rybnogo syr’ya bakterial’nymi zakvasochnymi kul’turami s primeneniem matematicheskih modelej // Pishchevaya promyshlennost. № 3. Pp. 92-96. DOI 10.52653/PPI.2023.3.3.019. (In Russ.).
Review
For citations:
Lavrukhina E.V., Zarubin N.Yu., Bredikhina O.V., Grinevich A.I. Modeling of the composition of a probiotic fish product using the Python. Fisheries. 2024;(3):122-129. (In Russ.) https://doi.org/10.36038/0131-6184-2024-3-122-129
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