A Calibration Tutorial for Spectral Data. Part 2. Partial Least Squares Regression Using Matlab and Some Neural Network Results

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Abstract
Part 1 explained multiplicative scatter correction (MSC), the building of a principal component regression (PCR) model and how the test data can be used in prediction. Emphasis was on data pretreatment for linearistion and on spectral/chemical interpretation of the results. Part 2 discusses partial least squares (PLS or PLSR) regression. The data set prepared in Part 1 is also used here. Details on data pretreatment are, therefore, not repeated. Some details of PLS modeling are explained using the calculations of the example. Also, the interpretation of the PLS model gets some attention. Neural network calculation results are included for comparison. Artifical neural networks (ANN) are non-linear, so linearisation is not considered necessary. Latent variable regression methods such as PLS and PCR and ANNs are all successive approximations to the unknown function y = f(x) that forms the basis of all calibration methods. In latent variable regression, the rank of the model determines the degree of approximation. In ANNs, the number of hidden nodes and the number of iterations determine the degree of approximation.

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