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Abstract
Our aim was to develop a rapid non-targeted approach to authenticate commercial honey combining Raman spectroscopy and pattern recognition analysis. Honey samples (n = 97) were collected from local and global grocery stores and online vendors. Spectra were collected by handheld and compact benchtop Raman systems equipped with a 1064 nm excitation laser and analyzed by pattern recognition techniques (Soft Independent Model of Class Analogies, SIMCA) to develop classification algorithms. High-performance liquid chromatography (HPLC-RI, AOAC#977.20) was used to verify the authenticity of honeys. Both Raman systems formed distinct SIMCA clusters for the samples associated with their unique sugar profile. Evaluation of store-bought commercial samples indicated adulteration in 16 samples (17%); all samples predicted by the Raman algorithms as non-authentic were confirmed by HPLC-RI. Raman technology provides analytical features for ingredient verification, offering the industry and regulatory agencies valuable resources to ensure accurate labeling of honey products.
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