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.
Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry
We used data mining with ICP-MS to evaluate origin of rice.An accuracy above 93% was obtained with SVM and RF algorithms.Our method can be used for authentication purposes of other food. Rice is one of the most consumed cereals in the world and the main food product in the diet of the Brazilian population. Brazil itself is among the ten largest producers of rice, and most of the harvest comes from the South and Midwest regions. This paper presents a data mining study of samples of rice obtained from producers in Goias (Midwest region) and Rio Grande do Sul (South region), and builds classification models capable of predicting the geographical origin of a rice sample based on its chemical components. We use three popular classification techniques, support vector machines, random forests and neural networks, along with the F-score formula which measures the relative importance of the input variables. We achieved very good performances for the SVM, RF and MLP models with 93.66%, 93.83% and 90% prediction accuracy, respectively, on the 10-fold cross validation. The F-score shows that Cd(cadmium), Rb(rubidium), Mg(magnesium) and K(potassium) are the four most relevant components for prediction.
DETERMINATION OF THE GEOGRAPHICAL ORIGIN OF WINES BY MEANS OF THE MINERAL CONTENT AND THE STABLE ISOTOPE RATIOS: A REVIEW
Numerous works have shown that the mineral composition of plants is influenced by the element levels in the ground, by fertilization methods, by the physiological aspects typical of the species from which they are produced, as well as by the technological conditions of production when it deals of transformation alimentary products. Therefore the multielement content method represents a valid tool for the determination of the origin of vegetable products. The current review contains the information present in literature concerning the influence of trace element concentration, or of minerals in general, and the stable isotope analysis in the geographical determination of wine origin. The most frequently used analytical techniques for mineral and isotope determination in wines are spettrophotometry by atomic absorption and the techniques evolved from it and the isotope ratio mass spectroscopy. The statistical techniques used are multivariate analysis, as PCA (principal component analysis), DA (discriminant analysis), CA (canonical analysis) and CLA (cluster analysis). These techniques have proven useful in the differentiation of wines and in their classification according to the place of origin.