Quantitative Analysis of Methanol in Methanol Gasoline by Calibration Transfer Strategy Based on Kernel Domain Adaptive Partial Least Squares(kda-PLS)

The application of near-infrared(NIR) spectroscopy combined with multivariate calibration methods can achieve the rapid analysis of methanol gasoline. However, instrumental or environmental differences found for spectra make it impossible to continuously apply the previously developed calibration model. Therefore, the calibration transfer technique would be required to solve the time-consuming and laborious problem of reestablishing a new model. In this work, a calibration transfer method named kernel domain adaptive partial least squares(kda-PLS) was applied to the calibration transfer from the primary instrument to the secondary ones. Firstly, wavelet transform(WT) and variable importance in projection(VIP) were employed to enhance the predictive performance of the kda-PLS transfer model. Then, the results found for the calibration transfer by piecewise direct standardization(PDS) and domain adaptive partial least squares(da-PLS) were compared to verify the calibration transfer(CT) effect of kda-PLS. The results point that the kda-PLS method can transfer the PLS model developed on the primary instrument to the secondary ones, and achieve results comparable to the those of reestablishing a new PLS model on the secondary instrument, with R P 2 =0.9979( R P 2 : coefficients of determination of the prediction set), RMSE P =0.0040 (RMSE P : root mean square error of the prediction set), and MRE P =3.03%(MRE P : mean relative error of the prediction set). Therefore, kda-PLS will provide a new method for quantitative analysis of methanol content in methanol gasoline.