Digital image-based tracing of geographic origin, winemaker, and grape type for red wine authentication.

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ID: 102848
2020
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Abstract
This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the São Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set; SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool; for tracing origins, grape type, and even (SFV) winemakers.
Reference Key
lima2020digitalfood Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lima, Carlos Monteiro de;Fernandes, David Douglas Sousa;Pereira, Giuliano Elias;Gomes, Adriano de Araújo;Araújo, Mário César Ugulino de;Diniz, Paulo Henrique Gonçalves Dias;
Journal Food chemistry
Year 2020
DOI
S0308-8146(19)32208-3
URL
Keywords
red wine cabernet sauvignon color histogram digital images geographical origin indication successive projections algorithm syrah touriga nacional

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