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ORIGINAL ARTICLE
Year : 2016  |  Volume : 12  |  Issue : 47  |  Page : 188-192

Rapid determination of puerarin by near-infrared spectroscopy during percolation and concentration process of puerariae lobatae radix


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; Department of pharmacy, Sanquan Medical College, Xinxiang, PR China
4 Department of pharmacy, Puyang Health School, Puyang, Henan Province, PR China

Correspondence Address:
Wan Guangrui
School of Pharmacy, Xinxiang Medical University, Xinxiang, Henan Province
PR China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1296.186350

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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 (r2) 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.(r2) 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. Wan Guangrui


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