Identification of pharmaceutical excipients using NIR spectroscopy and SIMCA.

Soft independent modelling of class analogy (SIMCA) is applied to identify near-infrared (NIR) spectra of ten excipients used in the pharmaceutical industry. For each class at least 15 excipient samples were collected for the data base, considering different batches and occasionally various suppliers. Therefore the data of the classes are not always homogeneous. The performance of the original SIMCA method, which is usually described in the literature and also applied by the users, carried out at two confidence levels, 95 and 99%, on original data, SNV (standard normal variate transformation) and second derivative pre-processed data, is discussed. Reasons for the rejection rates are given. No objects were assigned to a wrong class using SIMCA.

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.

Comparison of classification approaches applied to NIR-spectra of clinical study lots.

NIR-spectroscopy combined with pattern recognition approaches is applied to classify samples of clinical study lots in the pharmaceutical industry. The performance of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and K-nearest neighbour (KNN) method is evaluated on a tablet data set and a capsule data set. To establish a classification model a strategy is followed, which is described in this work. Frequently, in the pharmaceutical industry, several batches of the same clinical study lot are produced. We tested whether it is possible to merge several batches in one class for modelling or, instead, whether it is necessary to model each batch individually.