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Cutting-edge approaches for honey authentication: Chemical, molecular, and AI-driven strategies for botanical origin verification

Authors: Tushar Khare, Kareem A. Mosa, Rania Hamdy, Attiat Elnaggar, Shifa Malik, Suad Kadeem Khan, Ali El-Keblawy, Fouad Lamghari, Ahmed M.S. Alhmoudi, Khawla M. Alyammahi.


Cutting-edge approaches for honey authentication


Abstract: Honey is a widely consumed food commodity with significant commercial value, often determined by its botanical and geographical origin. This has led to issues such as falsified labeling and adulteration. While rapid detection methods exist, honey authentication techniques are still evolving to enhance accuracy. This article reviews current trends in authenticating honey's botanical origin, which is linked to its pollen content and biochemical profile. Traditional authentication methods include melissopalynology and chromatographic/spectroscopic fingerprinting, which identify unique chemical markers of specific plant species. Additionally, pollen DNA metabarcoding is highlighted as a promising approach, using plant-specific DNA barcodes for precise botanical identification. The review also emphasizes the importance of chemometric-multivariate analysis for processing large datasets generated by these methods. Furthermore, artificial intelligence-driven machine learning tools have shown near-complete accuracy in classifying honey samples and detecting adulteration. Despite these advancements, challenges persist, including incomplete reference databases, non-standardized protocols, and the high cost of advanced methodologies, limiting accessibility for small-scale beekeepers. To address these gaps, the article advocates for collaborative, multidisciplinary research to refine authentication techniques, expand open-access reference libraries, and develop affordable, user-friendly sensor technologies. Such efforts aim to enhance consumer confidence and maintain market integrity by ensuring accurate honey authentication.


 
 
 

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