Classification of Cabernet Sauvignon from Two Different Countries in South America by Chemical Compounds and Support Vector Machines
ABSTRACT A simple approach is proposed for the classification of Cabernet Sauvignon wines from two different countries in South America (Brazil and Chile). The strategy combines the wines’ functionality, designed as antioxidant activity (DPPH and ORAC), total polyphenols (TP), total anthocyanins (TA), and color, with a data mining technique known as support vector machines (SVM). The original dataset has 16 wine samples from Brazil and 113 from Chile. Algorithms were used to balance the dataset. Using resampling algorithms, we extended the Brazilian wines to 32 samples and reduced the Chilean wines to 32. With the proposed methodology, it was possible to classify the origin of the wine with an accuracy of 89% when using the 20 original elements. An accuracy of 83% was found using only 5 elements (L, DPPH, delph-3-acetylglu, peon-3-(coum)glu, and pet-3-acetylglu). Our methodology can be used for origin certification of other wines.
Intraregional classification of wine via ICP-MS elemental fingerprinting.
The feasibility of elemental fingerprinting in the classification of wines according to their provenance vineyard soil was investigated in the relatively small geographical area of a single wine district. Results for the Stellenbosch wine district (Western Cape Wine Region, South Africa), comprising an area of less than 1,000 km(2), suggest that classification of wines from different estates (120 wines from 23 estates) is indeed possible using accurate elemental data and multivariate statistical analysis based on a combination of principal component analysis, cluster analysis, and discriminant analysis. This is the first study to demonstrate the successful classification of wines at estate level in a single wine district in South Africa. The elements B, Ba, Cs, Cu, Mg, Rb, Sr, Tl and Zn were identified as suitable indicators. White and red wines were grouped in separate data sets to allow successful classification of wines. Correlation between wine classification and soil type distributions in the area was observed.
Markers of typical red wine varieties from the Valley of Tulum (San Juan-Argentina) based on VOCs profile and chemometrics.
We studied the VOCs profile of three red wine varieties, produced in the Valley of Tulum (San Juan-Argentina), over 4 consecutive years. Our main goal was to verify if different wine varieties could be differentiated from their VOCs profile, considering changes induced by their age, the yeast inoculated and the type of alcoholic fermentation, establishing those compounds that could be used as chemical markers of a particular variety. Stepwise LDA of selected VOCs allowed 100% differentiation between studied wines, showing that high levels of 1-hexanol were characteristic for Malbec, while low level of ethyl caproate was characteristic for Bonarda. Using controlled fermentations, 1-hexanol, a pre-fermentative VOC, presented a similar trend in wines produced from different yeast; while other fermentative VOCs, like ethyl caproate and ethyl caprilate, presented lower levels for Bonarda but also for Syrah. To our knowledge, this is the first report on characterization of VOCs from Bonarda. Additionally, the quantitative analysis of VOCs profile, coupled to chemometrics, present a good alternative to differentiate wines from different varieties and also for studying wine fermentation.