Rui-xue Zhong1,
Yu-jie Liu2,
Jun Wan3,
Li-yang Zhu1,
Yun-bei Chen1,
Hui-min Wen1,
Xiao-yun Duan1,
Xiao-ling Wang1,
Ting-sen Wei1,
Chuan-hong Wu1,
Guang-fu Feng1 
For correspondence:- Guang-fu Feng Email: fengguangfu65@163.com Tel:+8618908188545
Accepted: 26 September 2018 Published: 31 October 2018
Citation: Zhong R, Liu Y, Wan J, Zhu L, Chen Y, Wen H, et al. Online quality control of panaxatriol saponins percolation extraction using near-infrared technology. Trop J Pharm Res 2018; 17(10):2055-2060 doi: 10.4314/tjpr.v17i10.23
© 2018 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..
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 near-infrared 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.
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