Diffuse reflectance FTIR spectroscopy (DRIFTS) coupled with modern multivariate calibration methods, namely artificial neural networks (ANNs) in two versions (ANN-raw and ANN-pca), support vector machines (SVMs), lazy learning (LL) and partial least squares (PLS) regression, is used in this study for the quantification of carbamazepine crystal forms in ternary powder mixtures (I, III, and IV). Two spectral regions (675-1180 and 3400-3600/cm) were selected and the data were partitioned into training and test subsets applying the Kennard-Stone design. It was found that all the selected algorithms perform better than the PLS regression (root mean squared error of prediction (RMSEP) from 3.0% to 8.2%). ANN-raw, trained on uncompressed spectral data, shows best predictive performance (RMSEP < 2.25%) but longest computation (up to 10 min). Principal component analysis (PCA) compression of the input spectral data accelerates significantly the computation (<16 s) at a relatively low cost in precision (RMSEP < 3.24%). The LL algorithm shows excellent performance in the 3400-3600/cm range (RMSEP < 1.6%), but in the 675-1180/cm range it shows strong dependence on data set structure (RMSEP between 1.6% and 8.9%). SVMs perform comparably well with ANNs (RMSEP < 3.1%), not showing the long computation time of ANNs (<1 s) and therefore may provide an attractive alternative to ANNs.
Copyright 2006 Wiley-Liss, Inc. and the American Pharmacists Association