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Keywords = guindo santo honey

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14 pages, 5122 KB  
Article
Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools
by Guillermo Machuca, Juan Staforelli, Mauricio Rondanelli-Reyes, Rene Garces, Braulio Contreras-Trigo, Jorge Tapia, Ignacio Sanhueza, Anselmo Jara, Iván Lamas, Jose Max Troncoso and Pablo Coelho
Foods 2022, 11(23), 3868; https://doi.org/10.3390/foods11233868 - 30 Nov 2022
Cited by 9 | Viewed by 3698
Abstract
Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. [...] Read more.
Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities—mostly natives—to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision. Full article
(This article belongs to the Special Issue Food Fraud and Food Authenticity across the Food Supply Chain)
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