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.
Using Support Vector Machines and neural networks to classify Merlot wines from South America
Abstract Wines with a clear geographical origin are an issue of interest for consumers and food industries. This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin. A group of samples from Argentina (n = 17), Brazil (n = 12), Chile (n = 48), and Uruguay (n = 6) was analyzed. Twenty chemical compounds were determined by high-performance liquid chromatography (HPLC). These compounds include antioxidant activity, total polyphenols, total anthocyanins, individual anthocyanins and color. Four binary classification problems were performed (Brazil versus non-Brazil, Argentina versus non-Argentina, Chile versus non-Chile, and Uruguay versus non-Uruguay) to investigate the geographic characteristics of each country. Through the evaluation of binary classifications in our dataset it was possible to identify the main variables (chemical compounds) that discriminate between the countries. We used the following algorithms: Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach, the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines, Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment. SVM model obtained the highest performance measures among the classifiers for each dataset (93.73% of accuracy for the Brazil versus non-Brazil, 91.18% for the Argentina versus non-Argentina, 79.16% for the Chile versus non-Chile, and 91.67% for the Uruguay versus non-Uruguay classification). These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach. We found that some variables, such as DPPH, wine color and individual anthocyanins, are among the most important variables in the characterization of Merlot wines.
Synergistic Effect of High Temperature and High pH on the Destruction of Salmonella enteritidis and Escherichia co1i O157:H7.
This study was undertaken to determine if high temperature and high pH interact synergistically to enhance the rate of destruction of two important gram-negative foodborne pathogens, Escherichia coli O157:H7 and Salmonella enteritidis . The rates of destruction in NaHCO3-NaOH buffers at pH 7.0, 10.0, and 11.0 were determined at 35, 40, 45, 50, 55, 60, and 65°C. Use of an improved heating protocol eliminated a "tailing effect" at longer exposure times. The present study demonstrated that the combination of high pH and high temperature resulted in a highly significant synergistic interaction (P > F = 0.0001), which caused rapid death of both E. coli O157:H7 and S. enteritidis . This "alka-therm" technology might be used commercially to destroy gram-negative foodborne pathogens on various raw agricultural commodities.