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Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review

Jiménez-Carvelo, Ana M., González-Casado, Antonio, Bagur-González, M. Gracia, Cuadros-Rodríguez, Luis
Food research international 2019 v.122 pp. 25-39
data collection, factor analysis, food quality, least squares, models, principal component analysis, support vector machines, terminology
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.