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.
Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: A review
Abstract Because honey is a natural product synthetized by bees from secretions of various flowers and plants, its composition and properties are determined by those unique floral origins. Aside from honey’s sweet and distinctive flavor, it can provide various human health benefits, which makes its market value favorable to those of other sweeteners. Consequently, honey has been a very common and easy target of adulteration for economic profit, making authenticity of honey a longtime concern for researchers and producers around the world. The oldest and most popular of these methods is melissopalynology, the analysis of pollen contained in honey, which can determine its floral origin. However, because this method is time consuming, requires a specialist, and is unable to detect fraudulent pollen contamination, several other methods for the determination of the floral and geographical origin of honey have been proposed in the scientific literature. Multivariate data analysis and machine learning are emerging areas that offer performance and cost advantages for extracting valuable information from raw data sets. They are capable of performing both exploratory and predictive analyses, which can help to uncover trends and hidden patterns within data. Most of the studies reviewed base their methods on atomic spectra and physicochemical properties as descriptive variables for honey. Sensorial data obtained from electronic tongue and nose, and color histograms of honey images also show high discriminative power for ascertaining honey origin. Principal component analysis (PCA), discriminant analysis (DA), and cluster analysis are the preferred techniques for performing exploratory and predictive analyses for the purpose of identifying origin. PCA and DA continue to be preferred due to their ease of application and interpretation, while machine learning algorithms are more complex to model. Nevertheless, both machine learning algorithms and PCA-DA models achieved excellent results in discriminating honey origin. Finally, a commonly observed tendency is the use of hybrid methods that combine multivariate data analysis and machine learning techniques, as each technique has its own strengths and weaknesses regarding uncovering and classifying patterns in honey data.
Development of Korean red wines using Vitis labrusca varieties: instrumental and sensory characterization
Abstract In this study, two red wines were developed, with and without sugar addition, from a mixture of two different grape varieties, Gerbong (Vitis labrusca L.) 60% and Campbell Early (V. labrusca B.) 40%. Their chemical and sensory profiles were determined and compared with a French Beaujolais Nouveau wine of Chateau Talance using descriptive, physicochemical and volatile analyses. Colour, six aroma, and five taste attributes were evaluated by an expert panel of nine judges. Sample wines were analyzed for titratable acidity, ethanol, pH, Hunter colorimeter value, phenolic compounds, and organic acids. From the volatile analysis of sample wines, 10 acids, 12 alcohols, 10 esters, 7 volatile phenols, 5 furans, and 3 miscellaneous compounds were identified. From the principal component analysis of the descriptive data, wines were primarily separated along the first PC, which explained 93% of the total variance between the Korean wines with high intensities of “ripe-fruit aroma”, “caramel aroma”, and “sour” attributes and the French wine that was high in “woody aroma”, “astringent”, “mineral aroma”, and “colour” attributes.