The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra

The effect of data pre-processing (no pre-processing, offset correction, de-trending, standard normal variate transformation (SNV), SNV + de-trending, multiplicative scatter correction, first and second derivative transformation after smoothing) on the identification of ten pharmaceutical excipients is investigated. Four pattern recognition methods are tested in the study, namely the Mahalanobis distance method, the SIMCA residual variance method, the wavelength distance method and a method based on triangular potential functions. The performance of the 32 method combinations is evaluated on the basis of two NIR data sets. The first one, measured in 1994, is used to build the classification models, the second, measured from 1994-1997, is used to assess the quality of the models. The best approach for the given data sets is the wavelength distance method combined with de-trending, a simple baseline correction method. More general recommendations for pre-processing excipient NIR data and for choosing an appropriate classification method are given.