PATTERN-RECOGNITION ANALYSIS OF NEAR-INFRARED SPECTRA BY ROBUST DISTANCE METHOD

Abstract

A method for pattern recognition analysis of near-infrared spectra has been developed using robust distances determined by minimum volume ellipsoid (MVE) estimators of multivariate location and scatter. Classical methods such as the Mahalanobis distance method often fail in the presence of a moderate number of outliers in a training data set, while robust distance methods can tolerate a considerably larger proportion of outliers in a training data set. Outliers can be detected by their relatively large robust distances and can be excluded from a training set without a priori knowledge of the nature of the data set. In this paper the properties of a robust distance method are examined using near-infrared spectra of sulfamethoxazole and mixtures with its major degradation products, sulfanilic acid and sulfanilamide. The robust distance method successfully detected unacceptable samples (71.4%-89.3% (alpha = 0.05) or 78.6%-92.9% (alpha = 0.10)) even when they were inadvertently included in the training data set.