Parametric classification defined

Theory. SIMCA is a parametric classification method introduced by Wold (29), which supposes that the objects of a given class are normally distributed. The particularity of this PCA-based method is that one model is built for each class separately, that is, disjoint class modeling is performed. The algorithm starts by determining the optimal number of PCs for each individual model with CV. The resulting PCs are then used to define a hypervolume for each class. The boundary around one group of objects is then the confidence limit for the residuals of all objects determined by a statistical T-test (30, 31). The direction of the PCs and the limits established for these PCs define the model of a class (Fig. 13.13). 

The distance method is a geometric central parametric method its modified algorithm is described in references [105, 109]. In this case, the object of classification (chemical compound C) is defined by a set of determined features (Cj,..., c whose values are interpreted as coordinates of a point in a multidimensional space of m dimension. The classification metric is the distance from the object in question to the geometric center of class k. Compound C belongs to the class placed at a shorter distance. 

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