Multi‑ontological Architecture of the Food Bioinformational Matrix for Verification of Dairy Products

Introduction. Existing systems for describing food objects solve partial tasks: ontologies provide terminological unification and product classification, composition databases record nutrient profiles, traceability systems register product movement and production-logistics events, and laboratory data reflect the results of individual measurements. However, such data remain distributed across different descriptive contours and do not provide a transition to a unified system for food object authentication.

Purpose. To develop and test an operational architecture of the Food Bioinformational Matrix (FBM) that links the declared identity of a food object, its matrix organization, methods of data acquisition, verification criteria, and production history into a single verifiable model.

Materials and Methods. The methodological basis of the study was provided by the systems engineering standards ISO/IEC/IEEE 42010:2022 and ISO/IEC/IEEE 15288:2023. These standards were used to distinguish architectural representations of a food object, representation levels, verification procedures, and the production layer. The research object was the operational architecture for representing a dairy food object in the FBM system. The subject of the study was the product-specific signature, which included the identification, description, measurement, and verification levels, as well as the production layer. Six dairy products with different matrix organizations were selected as the material for testing: pasteurized milk, kefir, unsalted butter, salted butter, whole milk powder, and yoghurt. The methodology included sequential signature formation, distribution of attributes across levels, selection of measurement methods and verification criteria, pairwise comparison of signatures, and localization of differences between related food objects. The production layer was described using a graph model in Neo4j with the Cypher query language.

Results. Product-specific FBM signatures were formed for six dairy products, including the identification, description, measurement, and verification levels. The signatures were presented in tabular form and contained attributes required for the description, comparison, and verification of each object. Pairwise comparison of the signatures made it possible to localize differences between related dairy products at the level of matrix organization, microbial profile, measurement method, verification criterion, or production layer. A fragment of the kefir production layer was represented as a graph model.

Conclusion. The FBM provides a formalized transition from data to an authentication model for a food object. The study formed product-specific signatures for six dairy products, demonstrated the cascade relationship between identification, description, measurement, and verification, compared related products, and represented a fragment of the kefir production layer as a graph model. Quality control, raw material management, recycling, additive technologies, and decision-support systems are considered as potential areas for further application of the proposed architecture.

1. Galstyan, A. G. (2023). Food industry X.O. Food Metaengineering, 1, 7-10. https://doi.org/10.37442/fme.2023.2.33

2. Galstyan, A. G., Fomenko, O. Y., & Kruchinin, A. G. (2024). Facets of dairy science: Evolutionary imperatives and determinants of development. VNIMI. https://doi.org/10.37442/978-5-6051842-8-7

3. Semipyatny, V. K. (2020). Development of bioinformational matrices for describing and managing the quality of dairy products [Doctoral dissertation, VNIMI]. Moscow, Russia.

4. Semipyatny, V. K., Klimova, D. V., Khurshudyan, S. A., Ryskin, D. S., & Kosareva, A. V. (2025). Food bioinformational matrices as basic elements of technological singularity. Food Industry (in press).

5. Blasche, S., Kim, Y., Mars, R. A. T., Machado, D., Maansson, M., Kafkia, E., Milanese, A., Zeller, G., Teusink, B., Nielsen, J., Benes, V., Neves, R., Sauer, U., & Patil, K. R. (2021). Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nature Microbiology, 6, 196-208. https://doi.org/10.1038/s41564-020-00816-5

6. Dooley, D. M., Griffiths, E. J., Gosal, G. S., Buttigieg, P. L., Hoehndorf, R., Lange, M. C., Schriml, L. M., Brinkman, F. S. L., & Hsiao, W. W. L. (2018). FoodOn: A harmonized food ontology to increase global food traceability, quality control and data integration. npj Science of Food, 2, 23. https://doi.org/10.1038/s41538-018-0032-6

7. Eastham, J. L., & Leman, A. R. (2024). Precision fermentation for food proteins. Current Opinion in Food Science, 58, 101194. https://doi.org/10.1016/j.cofs.2024.101194

8. EFSA. (2015). The food classification and description system FoodEx2 (revision 2). EFSA Supporting Publications, 12, 804. https://doi.org/10.2903/sp.efsa.2015.EN-804

9. Finglas, P. M., Berry, R., & Astley, S. (2014). Assessing and improving the quality of food composition databases for nutrition and health applications in Europe: The contribution of EuroFIR. Advances in Nutrition, 5, 608S-614S. https://doi.org/10.3945/an.113.005470

10. Fukagawa, N. K., McKillop, K., Pehrsson, P. R., Moshfegh, A., Harnly, J., & Finley, J. (2022). USDA’s FoodData Central: What is it and why is it needed today? The American Journal of Clinical Nutrition, 115, 619-624. https://doi.org/10.1093/ajcn/nqab397

11. Gehlot, A., Malik, P. K., Singh, R., Akram, S. V., & Alsuwian, T. (2022). Dairy 4.0. Applied Sciences, 12, 7316. https://doi.org/10.3390/app12147316

12. Georgalaki, M., Zoumpopoulou, G., Anastasiou, R., Kazou, M., & Tsakalidou, E. (2021). Lactobacillus kefiranofaciens: From isolation and taxonomy to probiotic properties and applications. Microorganisms, 9, 2158. https://doi.org/10.3390/microorganisms9102158

13. GS1. (2022). EPCIS and Core Business Vocabulary Standard (Release 2.0). GS1.

14. Hassoun, A., Garcia-Garcia, G., Trollman, H., Jagtap, S., Parra-López, C., Cropotova, J., Bhat, Z., Centobelli, P., & Aït-Kaddour, A. (2023). Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives. Current Research in Food Science, 7, 100535. https://doi.org/10.1016/j.crfs.2023.100535

15. ISO 5725-1:2023. (2023). Accuracy (trueness and precision) of measurement methods and results – Part 1: General principles and definitions. ISO.

16. ISO 22005:2007. (2007). Traceability in the feed and food chain – General principles and basic requirements for system design and implementation. ISO.

17. ISO/IEC 17025:2017. (2017). General requirements for the competence of testing and calibration laboratories. ISO.

18. ISO/IEC/IEEE 15288:2023. (2023). Systems and software engineering – System life cycle processes. ISO.

19. ISO/IEC/IEEE 42010:2022. (2022). Software, systems and enterprise – Architecture description. ISO.

20. LanguaL. (2025). The International Framework for Food Description. Danish Food Informatics. https://www.langual.org

21. Neo4j. (2020). Introduction – Cypher Manual. Neo4j Documentation. https://neo4j.com/docs/cypher-manual

22. Prado, M. R., Blandón, L. M., Vandenberghe, L. P. S., Rodrigues, C., Castro, G. R., Thomaz-Soccol, V., & Soccol, C. R. (2015). Milk kefir: Composition, microbial cultures, biological activities, and related products. Frontiers in Microbiology, 6, 1177. https://doi.org/10.3389/fmicb.2015.01177

23. Robinson, I., Webber, J., & Eifrem, E. (2015). Graph databases: New opportunities for connected data (2nd ed.). O'Reilly Media.

24. Shahidi, F., & Pan, Y. (2022). Influence of food matrix and food processing. Critical Reviews in Food Science and Nutrition, 63, 6421-6445.

25. Stadie, J., Gulitz, A., Ehrmann, M. A., & Vogel, R. F. (2013). Metabolic activity and symbiotic interactions of lactic acid bacteria and yeasts isolated from water kefir. Food Microbiology, 35, 92-98. https://doi.org/10.1016/j.fm.2013.02.009