Honey quality and adulteration: a risk-based monitoring model and its impact on consumer confidence

This paper examines the multifaceted problem of authenticating beekeeping products in the context of the current market transformation. The key research focus is the development and validation of a risk-based honey quality monitoring model that combines modern analytical approaches with expert producer verification practices. An analysis of changes in the scale and forms of honey adulteration on the Russian market is conducted, along with an assessment of the impact of regulatory changes that took effect on September 1, 2024, on compliance with mandatory product requirements. Data on the use of Raman spectroscopy with offset, DNA metabarcoding, and machine learning algorithms, which enable highly accurate identification of exogenous sugars in honey, is systematized and synthesized. Significant importance is attached to the inclusion of a proprietary methodology focused on a multi-level assessment of apiary reliability, enabling an expanded audit of product sources. The relationship between the effectiveness of government oversight and consumer trust parameters is theoretically substantiated, the key psychological barriers limiting purchase willingness are identified, and ways to reduce them through digital traceability are outlined. The findings and results have practical implications for regulatory practices, as they can be used to modernize food safety mechanisms and strengthen the protection of bona fide industry participants.