Study on online monitoring and endpoint judging methods for the percolation and extraction process of Sinomenii caulis based on ultraviolet and near-infrared spectroscopy technologies
Objective:Using ultraviolet spectroscopy(UVS)and near-infrared spectroscopy(NIRS)for online monitoring and endpoint determination of the percolation and extraction processes of Sinomenii caulis.Methods:Collecting UV spectra of the filtrate during the percolation process,using partial least squares regression(PLSR)to establish a quantitative correction model for the content of sinomenine hydrochloride(SH),principal components(PCs),root mean square error of estimation(RMSEE),determination coefficients R2 and Q2,root mean square error of cross validation(RMSECV)and root mean square error of prediction(RMSEP)were used to evaluate the fitting and predictive ability of the model;PLSR was used to establish quantitative calibration model for UVS and NIRS in the extraction process,and factor number,RMSEE,R2,Q2,RMSECV,and RMSEP were also used as indicators to evaluate the fitting and predictive ability of the model.Results:The performance of the UVS model for the percolation process without preprocessing was similar to that of the model with 1st derivative preprocessing,and the predictive ability was strong.After separate modeling,the NIRS and UVS models of the extraction process showed better predictive performance for SH concentration.The water phase model established by UVS alone had a smaller RMSECV(0.21),while the chloroform phase model established by NIRS alone had a smaller RMSECV(2.68).Conclusion:The use of UVS and NIRS technology to establish a SH concentration model for the percolation and extraction processes of Sinomenii caulisi has good predictive performance and robustness,and is expected to achieve visualization of the percolation and extraction processes.
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