Support Vector Regression with Feature Selection for the Multivariate Calibration of Spectrofluorimetric Determination of Aromatic Amino Acids

Several artificial intelligent methods, including support vector regression (SVR), artificial neural networks (ANNs), and partial least square (PLS) are used for the multivariate calibration in the determination of the three aromatic amino acids (phenylalanine, tyrosine and tryptophan) in their mixtures by fluorescence spectroscopy. The results of the leave-one-out method show that SVR perform better than other methods, and appear to be good methods for this task. Furthermore, feature selection is performed for SVR to remove redundant features and a novel algorithm named PRIFER (prediction risk based feature selection for support vector regression) is proposed. Results on the above multivariate calibration data set show that PRIFER is a powerful tool for solving the multivariate calibration problems.