Rapid Determination of Puerarin by Near-infrared Spectroscopy During Percolation and Concentration Process of Puerariae Lobatae Radix

Xue Jintao; Yang Quanwei; Jing Yun; Liu Yufei; Li Chunyan; Yang Jing; Wu Yanfang; Li Peng; Wan Guangrui

doi:10.4103/0973-1296.186350

Rapid Determination of Puerarin by Near-infrared Spectroscopy During Percolation and Concentration Process of Puerariae Lobatae Radix

Pharmacogn Mag. 2016 Jul-Sep;12(47):188-92. doi: 10.4103/0973-1296.186350.

Authors

Xue Jintao¹, Yang Quanwei², Jing Yun¹, Liu Yufei¹, Li Chunyan³, Yang Jing⁴, Wu Yanfang¹, Li Peng¹, Wan Guangrui¹

Affiliations

¹ Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
² Department of pharmacy, Wu Han NO.1 Hospital, Wuhan, Hubei Province, PR China.
³ Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China; Department of pharmacy, Sanquan Medical College, Xinxiang, PR China.
⁴ Department of pharmacy, Puyang Health School, Puyang, Henan Province, PR China.

Abstract

Background: Gegen (Puerariae Labatae Radix) is one of the important medicines in Traditional Chinese Medicine. The studies showed that Gegen and its preparation had effective actions for atherosclerosis.

Objective: Near-infrared (NIR) was used to develop a method for rapid determination of puerarin during percolation and concentration process of Gegen.

Materials and methods: About ten batches of samples were collected with high-performance liquid chromatography analysis values as reference, calibration models are generated by partial least-squares (PLS) regression as linear regression, and artificial neural networks (ANN) as nonlinear regression.

Results: The root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively.

Conclusions: The NIR model for the rapid analysis of puerarin can be used for on-line quality control in the percolation and concentration process.

Summary: Near-infrared was used to develop a method for on-line quality control in the percolation and concentration process of GegenCalibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regressionThe root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r (2)) was 97.79% and 98.47%, respectively. Abbreviations used: NIR: Near-Infrared Spectroscopy; Gegen: Puerariae Loabatae Radix; TCM: Traditional Chinese Medicine; PLS: Partial least-squares; ANN: Artificial neural networks; RMSEP: Root mean square error of validation; R2: Correlation coefficients; PAT: Process analytical technology; FDA: The Food and Drug Administration; Rcal: Calibration set; RMSECV: Root mean square errors of cross-validation; RPD: Residual predictive deviation; SLS: Straight Line Subtraction; MLP: Multi-Layer Perceptron; MSE: Mean square error.

Keywords: Artificial neural networks; Gegen (Puerariae Lobatae Radix); near-infrared spectroscopy; partial least-squares.