## 2 Authenticity Management in Normative Systems and Food Safety Standards
However, some of these modern techniques, such as DNA analysis (e.g., MDA, PCR), LC-HRMS, and NMR, while offering precise botanical and geographical origin identification, remain controversial within the honey sector. This is primarily due to the lack of fully harmonized procedures, challenges related to sample degradation, and high implementation costs, which can limit their availability for small- and medium-sized enterprises (SMEs) [12; 28]. Additionally, many of these methods still lack formal accreditation in line with ISO/IEC 17025 [51], which can restrict their acceptance in certification and auditing systems. Key challenges include limited access to technology for smaller producers, high investment costs, the absence of open, interoperable reference databases, and stringent accreditation and validation requirements [27; 35]. For example, pollen analysis--although considered a reference method for botanical origin assessment--is often criticized for its high degree of subjectivity, limited repeatability, and low specificity, particularly when analyzing honey blends. The need for standardizing this technique and harmonizing analytical protocols has been repeatedly emphasized by the scientific community [55; 56]. As a response to these challenges, there is growing interest in AI-based solutions that enable the automation of analytical processes. An example is the use of convolutional neural networks (CNNs) and segmentation algorithms (e.g., UNet) for automated analysis of microscopic pollen images, which significantly improves repeatability, reduces analysis time, and minimizes the impact of human error [57; 58; 59]. These approaches can also be integrated with hybrid models and transfer learning techniques, allowing for their application even with small datasets, thus improving classification accuracy [60; 61]. Despite the development of chemometric tools and multivariate models, there is still a lack of universal platforms that integrate data from various sources into a single decision-making system. Therefore, it is essential to develop systems that allow for the automated integration of spectroscopic, pollen, and documentation analysis results, providing a foundation for robust and scalable authenticity control systems. Given the limitations of classical analytical methods, one of the most promising directions for future development is the automation of honey pollen analysis using deep learning tools. Implementing an AI-based approach can not only increase repeatability and objectivity but also enable integration with digital traceability systems and comprehensive food quality and safety management systems. The practical application of this approach--as developed within a recent research and development project--is presented in the following section. Despite numerous advances in analytical methods and their gradual integration into food quality systems, there remains a critical gap in practical, scalable, and automated solutions for honey authenticity verification, especially in the context of SMEs. Current methods often lack repeatability, are resource-intensive, and are poorly suited for real-time decision making. Moreover, the absence of standardized, interoperable platforms hampers integration with traceability and certification workflows [11; 12; 26; 27; 55; 56; 11]. These constraints highlight the need for novel, AI-supported solutions capable of automating key steps in the authentication process, particularly pollen identification, and embeddingthem within broader food safety and quality assurance systems. The present study directly addresses this gap through a process-oriented implementation of digital pathology supported by deep learning and industrial traceability frameworks.