MATHEMATICAL MODELING OF THE HONEY ASSESSMENT SYSTEM BASED ON QUALITY CRITERIA FOR THE FOOD INDUSTRY
DOI:
https://doi.org/10.31073/foodresources2025-24-18Keywords:
graphical model, mathematical analysis, quality parameters, natural honey, evaluation criteriaAbstract
Subject. The issue of quality control, identification, and prevention of honey falsification, as a health-promoting product and a raw material for the food industry, becomes increasingly relevant due to the globalization of world markets. Ukraine is a leading honey exporter, but the domestic market is developing rather slowly, with only a small percentage being used for deep processing into food products and beverages. This is primarily due to the complex process of identifying and establishing the properties of various botanical and geographical types of honey. The methodology for determining quality criteria within a single honey type, while meeting regulatory document requirements, remains unclear. Purpose. The objective of this work was to apply mathematical modeling methods to assess the quality of different types of honey. Methods. The study employed mathematical modeling methods to construct graphical models of both negative and positive parameters for evaluating honey quality. Additionally, advanced methods of melissopalynology, varietal determination characteristics, and decision trees based on the HACCP concept were applied to develop a technological scheme for the honey quality assessment system, both as a raw material and a finished food product. Results. The article presents the results of a part of comprehensive research on developing a honey safety and quality assessment system, focusing on the application of modeling methods to establish quality criteria. An experimental research base was formed using physicochemical parameters for honey varietal determination, and hypotheses were proposed for the implementation of mathematical modeling. Quality criteria were established for the use of honey as a functional ingredient in food systems, as a sweetener, or for deep processing with fermentation. As a result of constructing graphical models and mathematical calculations, coefficients were determined to establish the highest quality for various types of honey and the potential for deterioration or improvement of quality parameters. Scope of results. The results can be utilized by honey producers and other food product manufacturers to apply mathematical modeling methods for assessing the quality of different honey types.
Downloads
References
Adamchuk, L. O. (2021). Improvement of the method of botanical identification of honey. Food Science and Technology, 14(4), 31–42. https://doi.org/10.15673/fst.v14i4.1895.
Adamchuk, L., Sukhenko, V., & Skoryk, M. (2020). Adaptation of electronic scanning microscopy methods for investigation of pollen grains. Food Resources, 8(14), 20–28. https://doi.org/10.31073/foodresources2020-14-02.
Biswas, A., & Chaudhari, S. R. (2024). Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review. Food Chemistry, 445, 138712. https://doi.org/10.1016/j.foodchem.2024.138712.
Chenchouni, H., & Laallam, H. (2024). Revolutionizing food quality assessment: Unleashing the potential of artificial intelligence for enhancing honey physicochemical, biochemical, and melissopalynological insights. Journal of the Saudi Society of Agricultural Sciences. https://doi.org/10.1016/j.jssas.2024.01.002.
Jiang, W., Battesti, M. J., Yang, Y., Jean-Marie, É., Costa, J., Béreau, D., Paolini, J., & Robinson, J. C. (2024). Melissopalynological analysis of honey from French Guiana. Foods, 13(7), 1073. https://doi.org/10.3390/foods13071073.
Maicelo-Quintana, J. L., Reyna-Gonzales, K., Balcázar-Zumaeta, C. R., Auquiñivin-Silva, E. A., Castro-Alayo, E. M., Medina-Mendoza, M., Cayo-Colca, I. S., Maldonado-Ramirez, I., & Silva-Zuta, M. Z. (2024). Potential application of bee products in food industry: An exploratory review. Heliyon, 10(1), e24056. https://doi.org/10.1016/j.heliyon.2024.e24056.
Maina, M. W., Mburu, J., Maina, F. W., & Chimoita, E. L. (2024). Determinants of consumers’ perceptions of honey quality attributes which influence purchasing decisions in Nyandarua County, Kenya. Bee World, 1–6. https://doi.org/10.1080/0005772X.2023.2300492.
Tian, L., Bilamjian, S., Liu, L., Akiki, C., Cuthbertson, D. J., Anumol, T., & Bayen, S. (2024). Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys. Analytica Chimica Acta, 1304, 342536. https://doi.org/10.1016/j.aca.2024.342536.
Ulberth, F., Aries, E., De Rudder, O., Kaklamanos, G., & Maquet, A. (2024). Purity assessment of honey based on compound specific stable carbon isotope ratios obtained by LC-IRMS. Journal of AOAC International, qsae021. https://doi.org/10.1093/jaoacint/qsae021.
Downloads
Published
How to Cite
Issue
Section
License
Licensing and Copyright
The collection provides immediate open access, ensuring that the content is freely available to the global community, facilitating a broader exchange of knowledge in accordance with the principles of open science.
By submitting an article to the editors of the collection, authors confirm that the manuscript has not been submitted to other publications and grant the journal the right of first publication in accordance with the terms of the Copyright Transfer Agreement.
Copyright. Authors retain full copyright to their articles without restriction and simultaneously grant the journal a non-exclusive license for publication, distribution, and indexing.
Licensing. The collection publishes articles under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.uk
This license permits:
- copying and distributing the material in any medium or format;
- use of the material for educational and research purposes.
- Subject to the following conditions:
- attribution of the author, source, and license link;
- noncommercial use;
- no distribution of modified material (no derivatives).
First Publication Rights. Authors grant the collection “Food Resources” first publication rights, these allowing the collection to distribute the scientific material with attribution of authorship and the original publication in the collection.
Self-Archiving. Authors may upload any version of their articles (preprint, postprint, or published version) to institutional, disciplinary, or personal repositories without embargo, provided they indicate that the article was published in the collection "Food Resources" and include the CC BY-NC-ND license.
ISSN 
ISSN 
