In this study, the Raman spectroscopy was used to detect honey adulterated with fructose (F), glucose (G), inverted sugar (IS), hydrolyzed inulin syrup (IN), and malt must (M). Thus, 56 samples of authentic honeys (acacia, sunflower, tilia, polyfloral, and honeydew) and 900 adulterated samples (with 5, 10, 20, 30, 40, and 50% fructose, glucose, inverted sugar, malt must, and hydrolyzed inulin syrup) were analyzed. The classification of honey authenticity has been made using the partial least square linear discriminant analysis (PLS-LDA), and a total accuracy of 96.54% (authentic honey vs. adulterated honey) was observed, while in the case of adulterated honey, a total accuracy of 90.00% was observed, respectively. The determination of the adulterant agent concentration has been made using partial least squares regression (PLSR) and principal component regression (PCR) methods. The proposed method can be considered easy and rapid for honey adulteration detection to provide continuous in-line information.
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Mircea Oroian, Sergiu Paduret, and Sorina Ropciuc have been financed by the National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, grant number PN-II-RU-TE-2014-4-0110.
Conflict of Interest
Mircea Oroian declares that he has no conflict of interest. Sorina Ropciuc declares that she has no conflict of interest. Sergiu Paduret declares that he has no conflict of interest.
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Oroian, M., Ropciuc, S. & Paduret, S. Honey Adulteration Detection Using Raman Spectroscopy. Food Anal. Methods 11, 959–968 (2018). https://doi.org/10.1007/s12161-017-1072-2
- Raman spectra
- Statistical analysis