Objective: To established a near-infrared spectroscopy quantitative model for the rapid determination of volatile oils from Rhizoma wenyujin concisum. Methods:Firstly, the volatile oils from Rhizoma wenyujin was determined by the distillation method described in Chinese Pharmacopoeia. The quantitative calibration model was established and optimized by fourier transformation near-infrared spectroscopy ( FT-NIR) combined with partial least square ( PLS) regression. The calibration model was evaluated by the coef-ficient (r), root-mean-square error of calibration (RMSEC) and root mean square of cross-validation (RMSECV) of the calibration model as well as the root mean square of prediction ( RMSEP) of prediction model. Results: In the combination of FT-NIR and PLS regression, the spectrum of 7189-4227 cm-1 , 8813-7478 cm-1 and"second spectrum+MSC" were chosen to establishe and optimize the quantitative calibration model. For the quantitative calibration model, the r, RMSEC and RMSECV of volatile oils was 0. 9769, 0. 0907 and 0. 3773, respectively. For the prediction model, the r and RMSEP of volatile oils was 0. 9053 and 0. 1960, respective-ly. Conclusion:The established near-infrared spectroscopy quantitative model is relatively stable, accurate and reliable in the simulta-neous quantitative analysis of volatile oils, and is expected to be used for the rapid determination of volatile oils from Rhizoma wenyujin concisum.

Objective: To established a near-infrared spectroscopy quantitative model for the rapid determination of volatile oils from Rhizoma wenyujin concisum. Methods:Firstly, the volatile oils from Rhizoma wenyujin was determined by the distillation method described in Chinese Pharmacopoeia. The quantitative calibration model was established and optimized by fourier transformation near-infrared spectroscopy ( FT-NIR) combined with partial least square ( PLS) regression. The calibration model was evaluated by the coef-ficient (r), root-mean-square error of calibration (RMSEC) and root mean square of cross-validation (RMSECV) of the calibration model as well as the root mean square of prediction ( RMSEP) of prediction model. Results: In the combination of FT-NIR and PLS regression, the spectrum of 7189-4227 cm-1 , 8813-7478 cm-1 and"second spectrum+MSC" were chosen to establishe and optimize the quantitative calibration model. For the quantitative calibration model, the r, RMSEC and RMSECV of volatile oils was 0. 9769, 0. 0907 and 0. 3773, respectively. For the prediction model, the r and RMSEP of volatile oils was 0. 9053 and 0. 1960, respective-ly. Conclusion:The established near-infrared spectroscopy quantitative model is relatively stable, accurate and reliable in the simulta-neous quantitative analysis of volatile oils, and is expected to be used for the rapid determination of volatile oils from Rhizoma wenyujin concisum.