Input analysis, process data multivariate methods

To ensure that the training data for input into multivariate calibration methods encompassed the full range under study a variation of the Duplex method [6] called Multiplex [7] was used to split the data. Multiplex-processed data were then used as inputs for multivariate analysis. 

The most common algorithms used in the analysis of multivariate process and spectroscopic data are principle component analysis (PCA) and PLS. PLS and PCA are similar in that they are both factor analysis methods, and they both significantly reduce the dimensionality of the variable space. This is done by representing the data matrix (X) with a few orthogonal variables that explain most of the variance. The main difference between PLS and PCA is that PLS can be referred to as a supervised technique that maximizes the covariance between the response (Y) and input variables (X) in as few factors as possible while PCA... 

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