The molecular identity of honey: toward reliable biochemical authentication

Background: Honey fraud is a pervasive global challenge that compromises food safety, consumer trust, and the economic sustainability of apicultural systems. Despite honey's growing market value and well-recognized functional properties, current authentication practices remain hindered by fragmented biochemical marker panels, inconsistent analytical protocols, and limited regulatory alignment. Scope and approach: This commentary evaluates how multi-omic profiling, including glycomic, phenolic, volatile, isotopic, and elemental signatures, can be transformed into reproducible biochemical fingerprints for honey authentication. Advances in high-resolution techniques (UHPLC-HRMS, GC-MS, NMR, FTIR, and RAMAN) combined with artificial intelligence (AI), particularly deep learning and federated modeling, offer unprecedented classification accuracy, scalability, and adaptability across production systems. We further explore the integration of harmonized sampling practices (“Good Apicultural Sampling Practice”), open-access reference libraries, interlaboratory validation, and digital traceability enablers such as blockchain, Big Data, and the Internet of Things (IoT). Key findings and conclusions: Embedding explainable AI within authentication workflows enhances interpretability and regulatory acceptance, while blockchain and IoT provide tamper-resistant, real-time traceability across the supply chain. Together, these Industry 4.0 technologies can transform honey authentication from a retrospective laboratory task into a proactive surveillance system. By aligning robust science with transparent digital infrastructures and inclusive governance mechanisms, honey markets can move toward standardized, trusted frameworks that protect consumers, reward legitimate producers, and preserve the biodiversity and cultural heritage that underpin honey's unique identity.