GENETIC ALGORITHMS AS A TOOL FOR WAVELENGTH SELECTION IN MULTIVARIATECALIBRATION

A comparison of multiple linear regression (MLR) with partial least-sq uares (PLS) regression is presented, for the multivariate modeling of hydroxyl number in a certain polymer of a heterogeneous near-IR spectr oscopic data set. The MLR model was performed by selecting the variabl es 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 va riables in each model have similar, chemically understandable function s. It is concluded that both solutions are equivalent and that each ha s some advantages and disadvantages. This also means that even in very complex situations such as here, MLR can replace PLS in certain cases .