Multivariate calibration: applications to pharmaceutical analysis

Abstract

The principles of multivariate calibration (MC) are presented, with reference to the main objectives of this chemometrics technique: the reduction of the variance in the prediction of a response variable (generally, a chemical quantity) and the possibility of the determination of the response in complex matrices with no or limited sample preparation, as in the case of the determination of a drug in a medicament. In both cases MC uses the whole information in a spectrum (a series of predictors). The possibility of the improvement of the MC performances, eliminating some useless, noisy, predictors is shown. Variable selection has been performed using two original techniques: a stepwise elimination procedure, based on the normalised coefficients of the regression equation relating the response to the predictors and a technique based on iterative repetitions of the regression technique (partial least squares regression, PLS), each time by weighting the predictors by their normalised regression coefficient computed in the previous cycle. These strategies are illustrated by means of different data sets, a synthetic example and a real example where MC, applied to near infrared spectroscopy, is used in the analysis of a drug. In this case also the application of an original MC technique is shown, where a joint regression model is obtained for two different instruments.