GENETIC ALGORITHMS AS A TOOL FOR WAVELENGTH SELECTION IN MULTIVARIATE CALIBRATION

A comparison of multiple linear regression (MLR) with partial least-squares (PLS) regression is presented, for the multivariate modeling of hydroxyl number in a certain polymer of a heterogeneous near-IR spectroscopic data set. The MLR model was performed by selecting the variables with a genetic algorithm. A good model could be obtained with both methods. It was shown that the MLR and PLS solutions are very similar. The two models include the same number of variables, and the first variables in each model have similar, chemically understandable functions. It is concluded that both solutions are equivalent and that each has some advantages and disadvantages. This also means that even in very complex situations such as here, MLR can replace PLS in certain cases.