Comparison of various data analysis techniques applied for the classification of pharmaceutical samples by electronic tongue

This work reports a critical evaluation of performance of various pattern recognition techniques applied to the classification of pharmaceutical taste-masked samples. Data obtained by potentiometric electronic tongue equipped with 16 ion-selective electrodes (ISEs) were processed by the most frequently used techniques in the analysis of electronic tongue data. Principal component analysis, partial least squares discriminant analysis, soft independent modelling of class analogy, principal component regression, support vector machine − discriminant analysis, 3-way partial least squares, K-nearest neighbours as well as combination of principal components analysis and back propagation neural networks were tested. In order to compare their ability to estimate class affinity of pharmaceutical samples, sensitivity, precision, percent of correct classification (%cc) and root mean square error (RMSE) were calculated. Additionally, 4 different kinds of data matrices: dynamic responses, stationary responses, combinations of them both, CPA values (change of the membrane potential caused by adsorption) were processed by pattern recognition techniques for the determination of the influence of the extraction of the data on the classification results. SVM-DA is proved to exhibit the best performance for the most commonly applied data extraction i.e. the steady-state response of the sensor array. Furthermore, it is shown, that including dynamic responses in the data matrix better classification abilities of the majority of the studied pattern recognition techniques are obtained. It must be underlined, that the presented findings are based on studying 399 models for whom all performance factors (sensitivity, precision, %cc, RMSE) were determined for both train and test sets to obtain reliable and repeatable results.