[Research on modeling method to analyze Lonicerae Japonicae Flos extraction process with online MEMS-NIR based on two types of error detection theory].

To establish a rapid quantitative analysis method for online monitoring of chlorogenic acid in aqueous solution of Lonicera Japonica Flos extraction by using micro-electromechanical near infrared spectroscopy (MEMS-NIR). High performance liquid chromatography(HPLC) was used as reference method．Kennard-Stone (K-S) algorithm was used to divide sample sets, and partial least square(PLS) regression was adopted to establish the multivariate analysis model between the HPLC analysis contents and NIR spectra. The synergy interval partial least squares (SiPLS) was used to selected modeling waveband to establish PLS models. RPD was used to evaluate the prediction performance of the models. MDLs was calculated based on two types of error detection theory, on-line analytical modeling approach of Lonicera Japonica Flos extraction process was expressed scientifically by MDL. The result shows that the model established by multiplicative scatter correction(MSC) was the best, with the root mean square with cross validation(RMSECV), root mean square error of correction(RMSEC) and root mean square error of prediction(RMSEP) of chlorogenic acid as 1.707, 1.489, 2.362, respectively, the determination coefficient of the calibration model was 0.998 5, and the determination coefficient of the prediction was 0.988 1．The value of RPD is 9.468.The MDL (0.042 15 g•L⁻¹) selected by SiPLS is less than the original,which demonstrated that SiPLS was beneficial to improve the prediction performance of the model. In this study, a more accurate expression of the prediction performance of the model from the two types of error detection theory, to further illustrate MEMS-NIR spectroscopy can be used for on-line monitoring of Lonicera Japonica Flos extraction process.