## 2 Authenticity Management in Normative Systems and Food Safety Standards
Limitations of Classical Fraud Detection Methods and Their Potential Integration into Quality Management Systems  
Despite significant advances in food analytics, honey authenticity verification still relies primarily on classical physicochemical, spectroscopic, chromatographic, and documentary methods. While each of these approaches has important advantages, their \begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Description/Examples** \\ \hline \multirow{4}{*}{Normative Systems} & ISO 22000:2018 [23], IFS Food v.8, BRCGS \\  & v.9—standards that include requirements for \\  & authenticity assessment, traceability, and food fraud \\  & vulnerability management \\ \hline \multirow{4}{*}{Management Tools} & VACCP (Vulnerability Assessment and Critical \\  & Control Points), TACCP (Threat Assessment and \\  & Critical Control Points), traceability systems—enable \\  & the identification of risks and preventive measures \\  & across the food supply chain \\ \hline \multirow{4}{*}{Operational Procedures} & Fraud vulnerability assessment (VACCP), supply \\  & chain mapping, supplier verification (due diligence), \\  & key performance indicator (KPI) monitoring, and \\  & risk analysis \\ \hline \multirow{2}{*}{Supporting Technologies} & Advanced analytical techniques, Business \\  & Intelligence systems, predictive algorithms \\ \hline \multirow{2}{*}{Competency Requirements} & Staff training, risk documentation, internal and \\  & external audits, development of food safety culture \\ \hline \multirow{4}{*}{Supporting Elements} & Data integration (traceability, data analytics, risk \\  & assessment), digital decision-making support, \\  & AI-based analysis and machine learning models \\ \hline \hline \end{tabular} Source: own study based on [23, 24, 25, 26, 27, 36, 37].  
\end{table} 
Table 1: Key elements of food authenticity management systems. effectiveness can be limited under industrial conditions, where rapid, scalable, and repeatable verification is required [11; 38; 39; 40]. In particular, techniques such as LC-HRMS and NMR, despite their high precision and potential for detecting adulteration, are not yet formally accepted within many normative standards. This is due to the lack of clearly defined validation protocols, difficulties in harmonizing results, and the absence of full acceptance by regulatory bodies [12]. Similarly, DNA-based techniques (MDA), though promising, remain controversial in the industry due to the potential degradation of genetic material, variability in results depending on analytical conditions, and a lack of widespread acceptance in the sector [28]. The most commonly used methods include the following:  
1. Isotopic Analysis (IRMS)--used to detect the addition of plant-derived sugars. However, this method is costly and can be challenging to interpret, particularly when analyzing honeys with similar isotopic profiles [39; 41]. 2. Spectroscopy (FTIR, NIR, UV-Vis)--allows for rapid chemical profiling, but the effectiveness of this approach depends on the quality of calibration models, reference databases, and measurement accuracy [42; 43; 44]. 3. Chromatography (HPLC, GC-MS)--enables the identification of specific botanical markers and detection of adulterants but requires time-consuming sample preparation and expert interpretation of results [45; 46; 47; 48]. 4. DNA Analysis (MDA)--allows for highly precise botanical identification but requires clean samples, is susceptible to genetic material degradation, and remains controversial in the industry due to inconsistent results under varying analytical conditions [28; 49]. 5. High-Resolution Spectroscopic Techniques (NMR, LC-HRMS)--offer high analytical precision, but the lack of standardized validation protocols and accreditation requirements limit their acceptance in normative systems [12; 50]. 6. Documentary Analysis (traceability)--a fundamental tool for quality audits, but without the support of laboratory and digital analytics, its ability to detect fraud is limited [26; 27].