Decision criteria for soft independent modelling of class analogy applied to near infrared data

SIMCA (soft independent modelling of class analogy) is a well known pattern recognition method which describes each class separately in a principal components (PC) space. New objects are considered to belong to the class if their Euclidean distance towards the constructed PC space is not significantly larger than the Euclidean distance of the class objects towards their PC space. The large number of wrongly rejected objects (α-error), which is a known problem of SIMCA, was examined. Using scores, predicted by leave-one-out cross-validation, instead of the original scores, obtained after PCA on the class objects, to compute the distance towards the class model clearly improves the decision criterion of the original SIMCA for a data set consisting of near infrared (NIR) spectra of tablets. The original SIMCA and modifications using different distance measures as defined by Hawkins (Mahalanobis distance) and Gnanadesikan were compared with respect to classification and their robustness towards the number of PCs selected to describe the different classes. SIMCA modified with the Mahalanobis distance was found to be a good alternative of the original SIMCA which, for the presented NIR data set, seems to be more robust for finding outliers when the exact number of PCs to build the model is not known.