[Near-infrared spectral quantitative analysis by combining classification with local PLS].
As a rapid analytical technology, near-infrared (NIR) spectroscopy has been developed fast in recent years. To improve the accuracy of near-infrared spectral quantitative analysis, the present paper first classifies a testing sample by a support vector machine classifier and selects some similar training samples of the same type to build the calibration model, than predicts the property of the testing sample. To avoid the negative influence of classification failure, a new hybrid algorithm (called H_PLS) was proposed. This algorithm consists of a local PLS model based on the same-type training samples (called C_PLS) and a local PLS model based on the total training samples (called D_PLS). H_PLS calculates the predictive value of the property for the testing sample by comparing the outputs of the two models. For a set of gasoline samples, experimental results show that the prediction accuracy of C_PLS is higher than that of D_PLS if there are no classification errors, otherwise the prediction accuracy of C_PLS will drop obviously. The novel proposed algorithm (H_PLS) combines the advantages of C_PLS and D_PLS. Using H_PLS, can increase from 0.9734 of D_PLS and 0.9656 of C_PLS to 0.9858 even though there are classification errors.
Zhang
Xiao-man
XM
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
Dai
Lian-kui
LK