LAUSR

Purpose : To establish a new prediction model for online quality control of the percolation extraction of panaxatriol saponins (PTS), viz, ginsenoside Rg1, ginsenoside Re and notoginsenoside R1, from notoginseng by near-infrared (NIR) technology coupled with partial least squares (PLS) analysis. Methods : Ten batches of PTS (420 samples) were collected and the  constituents were determined using HPLC. The NIR spectroscopy of samples was determined using a Fourier-Transform nearinfrared spectrometer with an optical fiber transmission PbS detector. Eight sample batches were the calibration set, and two batches were the forecast set. Calibration models were established based on min-max normalization (MMN). Results : The root mean square errors of cross-validation (RMSECV) of Rg1, Re, and R1 were 0.798, 0.095, and 0.259 mg/mL, respectively. The root mean square errors of prediction (RMSEP) were 1.110, 0.496, and 0.390 mg/mL, respectively. The correlation coefficients (R2) of cross-validation were 0.9682, 0.9681, and 0.9626, respectively, while the correlation coefficients (R2) of prediction were 0.9831, 0.9198, and 0.9661,  respectively. Conclusion : The results indicate that NIR is a quick and effective tool for online quality control of PTS (ginsenoside Rg1, ginsenoside Re, and notoginsenoside R1) in the percolation extraction process. Keywords : Online monitoring, Near infrared technology, Panaxatriol  saponins, Partial least squares