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


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.


CONCLUSIONS
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.
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Xue
X
Jintao
Department of pharmacy, Wu Han NO.1 Hospital, Wuhan, Hubei Province, PR China.
Yang
Y
Quanwei
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Jing
J
Yun
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Liu
L
Yufei
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China; Department of pharmacy, Sanquan Medical College, Xinxiang, PR China.
Li
L
Chunyan
Department of pharmacy, Puyang Health School, Puyang, Henan Province, PR China.
Yang
Y
Jing
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Wu
W
Yanfang
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Li
L
Peng
Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, PR China.
Wan
W
Guangrui
Artificial neural networks; Gegen (Puerariae Lobatae Radix); near-infrared spectroscopy; partial least-squares