Abstract：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 0.973 4 of D_PLS and 0.965 6 of C_PLS to 0.985 8 even though there are classification errors.
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