Application of Partial Least Squares Support Vector Machines (PLS-SVM) in Spectroscopy Quantitative Analysis

In order to overcome the high dimension and collinearity problem of spectrum data in spectroscopy quantitative analysis, we introduce a partial least squares support vector machines (PLS-SVM) method, which integrates partial least squares (PLS) and support vector machines (SVM). The method uses PLS to extract the feature of spectrum. Then the feature serves as the input of SVM calibration model instead of the whole spectrum. Experimental results show that PLS-SVM is superior to PLS in terms of prediction precision, while the modeling time is greatly reduced compared with SVM without feature extraction