## 1 Introduction  
Food authenticity is a critical aspect of quality and safety management systems in global supply chains. It involves not only the precise identification of the raw material composition and geographical origin of products but also the prevention of practices that can mislead consumers [1]. According to the definition, food authenticity refers to the consistency of actual product characteristics, such as botanical composition, production technology, and origin, with the declared information. It also implies the absence of intentional practices that might deceive consumers, including composition adulteration, incorrect labeling, or unauthorized health claims [2; 3; 4]. In the context of global supply chains, where products often travel thousands of kilometers before reaching consumers, authenticity is essential for ensuring food safety, protecting public health, and building consumer trust [5]. Honey, one of the most frequently adulterated food products, presents particular challenges in terms of authenticity. These challenges arise from the high market value of honey, the diversity of its varieties, and the difficulty in precisely determining its botanical and geographical origin [6; 7]. Additionally, the globalization of trade and complex supply chains create numerous opportunities for introducing products of questionable origin into the market [8]. Therefore, it is essential to develop advanced analytical methods and quality management tools that enable effective fraud detection and origin verification for honey [9; 10; 11]. However, some modern analytical techniques, such as metagenomic DNA analysis (MDA) or nuclear magnetic resonance (NMR), remain controversial within the industry. These concerns primarily relate to their practical application, costs, and the availability of reliable reference databases [12]. This requires further validation studies and standardization of analytical protocols before full implementation [13]. Modern forms of honey adulteration include the following:  
1. Addition of sugar syrups--significantly alters the chemical composition and sensory properties of honey (the most common form of adulteration). 2. Blending of honey--mixing honey from different origins to reduce production costs while maintaining a higher market price. 3. Incorrect labeling--falsely declaring geographical or botanical origin to increase the market value of the product. Results from a recent EU-wide control program, coordinated by the European Commission, indicated that up to 46% of honey samples from non-EU sources exhibited characteristics indicative of adulteration--primarily through the addition of syrups or incorrect origin declarations [13; 14]. In light of these findings, the issue of honey authenticity has become not only a technological challenge but also a strategic concern in terms of consumer trust and fair competition. Although numerous analytical methods are available, including isotopic analysis (IRMS), NMR, FTIR spectroscopy and pollen analysis, none provide full reliability under operational conditions, particularly for small- and medium-sized enterprises (SMEs) [6; 15]. Effective honey authenticity verification, therefore, requires not only high-quality data but also the ability to efficiently integrate these data into quality management systems [16]. In response to this challenge, integrated process-oriented approaches that combine laboratory validation with food safety management systems and international quality standards (BRCGS, IFS Food, FSSC 22000), vulnerability assessment of product adulteration (VACCP), and advanced digital technologies have emerged as promising solutions [17]. Special attention is given to artificial intelligence (AI) tools, including deep learning methods and image processing techniques, such as the segmentation and classification of microscopic pollen images.