Model Updating for the Identification of NIR Spectra from a Pharmaceutical Excipient

Abstract

The effect of model updating on the identification of a pharmaceutical excipient based on its near-infrared (NIR) spectra has been investigated. A pragmatic updating approach, consisting of adding stepwise newly available samples to the training set and rebuilding the classification model, was applied. Its performance is compared for three pattern recognition methods: the wavelength distance method, the Mahalanobis distance method, and the SIMCA (soft independent modeling of class analogy) residual variance method. For the wavelength distance method, the updating approach is straightforward. In the case of the multivariate classification methods, which are based on a certain number of significant principal components (PCs), the selection of the number of PCs included in the model must be performed with care, as this number has a major impact on the classification results.