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

摘要

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 Gegen Calibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression 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. 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.