Support vector machines for the discrimination of analytical chemical data: application to the determination of tablet production by pyrolysis-gas chromatography-mass spectrometry

Abstract

This paper describes the application of support vector machines (SVM) to analytical chemical data, and is exemplified by the application to the determination of tablet production using pyrolysis-gas chromatography-mass spectrometry. An approach relying on SVM in conjunction with other chemometrics tools such as principal component analysis and discriminant analysis is presented. The ability for SVM to generalize well makes this technique attractive when dealing with limited sized training sets. By using appropriate kernels, SVM result in classifiers of diverse complexity able to draw non-linear decision class boundaries that may suit composite distributions. Principal component analysis and discriminant analysis by means of Mahalanobis distance are used in a stepwise procedure for extracting and selecting meaningful features from the pyrolysis spectrum, in order to feed various SVM classifiers. Results show that discrimination is achievable between the two methods, with SVM performing better than discriminant analysis on the dataset investigated.