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

In this paper, two different approaches are studied for the standardization of near-infrared spectrometric instruments. The first is a simple method based on a univariate slope/bias correction of the predicted values. The second approach proposed is the much more sophisticated piecewise direct standardization (PDS) based on a multivariate correction of spectra. Both standardization methods are applied to three different data sets, and the results obtained are compared. In certain cases, the simple slope/bias correction approach yields results at least as good as those obtained by the PDS procedure. In other cases, the complex PDS procedure is required to obtain acceptable results. A diagnostic tool is developed in order to decide whether the simple slope/bias correction approach can be applied instead of PDS.

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