Partial least squares discriminant analysis , exploratory

Among the different chemometric methods, exploratory data analysis and pattern recognition are frequently used in the area of food analysis. Exploratory data analysis is focused on the possible relationships between samples and variables, while pattern recognition studies the behavior between samples and variables [95]. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) are the methods most commonly used for exploratory analysis and pattern recognition, respectively. The importance of these statistical tools has been demonstrated by the wide number of works in the field of food science where they have been applied. The majority of the applications are related to the characterization and authentication of olive oil, animal fats, marine and vegetable oils [95], wine [97], fruit juice [98], honey [99], cheese [100,101], and so on, although other important use of statistical tools is the detection of adulterants or frauds [96,102]. 

While principal components models are used mostly in an unsupervised or exploratory mode, models based on canonical variates are often applied in a supervisory way for the prediction of biological activities from chemical, physicochemical or other biological parameters. In this section we discuss briefly the methods of linear discriminant analysis (LDA) and canonical correlation analysis (CCA). Although there has been an early awareness of these methods in QSAR [7,50], they have not been widely accepted. More recently they have been superseded by the successful introduction of partial least squares analysis (PLS) in QSAR. Nevertheless, the early pattern recognition techniques have prepared the minds for the introduction of modem chemometric approaches. 

Using this approach the first multiplexed simultaneous detection of six different DNA sequences, corresponding to different strains of the Escherichia coli bacterium, each labeled with a different commercially available dye label (ROX, HEX, FAM, TET, Cy3, or TAMRA) was reported [52]. In this study, both exploratory discriminant analysis and supervised learning, by partial least squares (PLS) regression, were used and the ability to discriminate whether a particular labeled... 

Seven parameters of physicochemical properties, such as acid number, color, density, refractive index, moisture and volatility, saponification value and PV, were measured for quality and adnlter-ated soybean, as well as quality and rancid rapeseed oils. The chemometric methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods snch as exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA.260... 

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