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.
Finding the most important sensory descriptors to differentiate some Vitis vinifera L. South American wines using support vector machines
The geographical recognition of wines has been extensively attempted based on chemical parameters. However, few studies have used wine sensory properties to characterize wines according to their geographical origin. This paper presents a machine learning study to classify and to find the most important sensory descriptors of Cabernet Sauvignon, Syrah, Tannat, and Merlot wines from Argentina, Brazil, Chile and Uruguay. Four feature selection methods (F score, relief, $${\chi ^2}$$χ2, and random forest importance) were used to generate the order of importance of the sensory descriptors. The feature subsets were generated based on the feature selection ranking order to use as input features for the support vector machines classifier. Very good results with 85–100% accuracy were achieved, and the results showed that few sensory descriptors discriminate the origin of wines better than when using all the descriptors and that there is a specific subset of most important features to each wine variety. As far as we know, this is the first study to analyze South American wines based solely on sensory descriptors and support vector machines along with feature selection methods.
Instrumentation and Automated Photometric Titration Procedure for Total Acidity Determination in Red Wine Employing a Multicommuted Flow System
An automated procedure for photometric titration of red wine and associated instrumentation is described. The procedure was based on the flow-batch approach implemented employing multicommutation. The photometric detection was carried out using a homemade LED-based photometer. The mixing device, LED, and photodetector were attached to the titration chamber in order to form a compact and small-sized unit. The flow system comprised an automatic injector and three-way solenoid valves, which were controlled by a microcomputer through an electronic interface card. The software, written in Quick BASIC 4.5, was designed with abilities to accomplish all steps of the titration procedure including data acquisition and real-time processing to decide about the course of titration in the following step and so forth, until the titration endpoint was reached. The usefulness of the proposed titration system was demonstrated by analyzing red wine samples. When results were compared with those obtained using the AOAC reference method, no significant difference was observed at the 95% confidence level. A relative standard deviation of ca  2% (n=9 ) was obtained when processing a typical red wine sample containing 7.3 gl -1 total acidity expressed as tartaric acid.