[Spectral wavelength selection based on PLS projection analysis].

In order to enhance the prediction accuracy of spectral analysis models and reduce their input number, this paper presents a simple and rapid wavelength selection method based on PLS projection correlation coefficients. These correlation coefficients are decided by both the changes in spectra data and the PLS regression coefficients between spectra matrix and concentration vector. Compared with the traditional wavelength selection method based on correlation analysis, the novel proposed method obviously improves the robustness of spectral analysis models and reduces their input number sharply. Applying the proposed method to 208 gasoline samples, the experimental results show that the number of calibration model input decreases to 30% of the original wavelength number, and the root mean square error of cross validation is reduced from 0.44 to 0.34. This method can be widely used in wavelength selection and data compression in spectral quantitative analysis.