Resolution of overlapping fluorescence spectra using the kernel learning machine

A novel method based on combination of highly sensitive spectrofluorimetry and flexible the machine learning techniques was proposed for the simultaneous spectrofluorimetric determination of α-naphthol, β-naphthylamine and carbazole with overlapping peaks. This method addresses multivariate calibration based on the least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The LS-SVM technique has the advantages to offer the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity by only requiring to solve a set of linear equations instead of a quadratic programming problem. Experimental results showed the LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. The relative standard errors of prediction (RSEP) obtained for total components using LS-SVM and PLS were compared. It is found that the LS-SVM method is better than the conventional PLS methods.