Résumé
OBJECTIVE: To discriminate and quantify of Gleditsia japonica Miq. thorn (SZJ) and Gleditsia microphylla Gordon ex Y. T. Lee thorn (YZJ) in the Gleditsia sinensis Lam thorn (GST). METHODS: Fourier transform near-infrared spectroscopy (FT-NIR) combined with linear discriminate analysis (LDA), support vector machine (SVM), as while as back propagation neural network (BPNN) algorithms were applied to construct the identification models. The SZJ and YZJ content in adulterated GST were determined by partial least squares regression (PLSR). RESULTS: The SVM models performance best compared with LDA and BP-NN models for it could reach 100% accuracy in training and validation set for identifying authentic GST and GST adulterated with SZJ and YZJ based on the spectral region of 5 000-4 200 cm-1 combined with SG+VN processing. The rp, RMSEP (the root mean standard error of prediction) and bias for the prediction by PLS regression model were 0.993, 2.919% and -0.330 3 for SZJ, 0.995, 2.57% and 0.364 9 for YZJ, respectively. CONCLUSION: Our results suggest that the combination of NIR spectroscopy and chemometric methods offers a simple, fast and reliable method for classifification and quantifification of SZJ and YZJ adulterants in the GST.
Résumé
Objective: To combine macroscopical characteristic indices and chemical indices of Andrographis Herba to evaluate its quality grade. Methods: Both macroscopical characteristic indices and chemical indices (the content of four active diterpenoids and the content of ethanol-soluble extractives) of different batches of Andrographis Herba were determined. The macroscopical characteristic indices were encoded using the method of numerical taxonomy, and the content of four active diterpenoids were determined by HPLC. To screen out the appropriate indices for classification, the correlational analyses were conducted between encoded macroscopical characteristic indices and chemical indices. The quality grade was made by principal component clustering analysis according these evaluation indices, and then was analyzed through partial least squares discriminant analysis (PLS-DA). Furthermore, a partial least squares (PLS) regression was constructed for the quality grade prediction of Andrographis Herba. Results: It showed that the samples could be divided into three grades according to the principal component clustering analysis, and was reasonable evaluating by PLS-DA. The PLS regression model for quality grade of Andrographis Herba was constructed as follows: grade Y=3.761-0.020×the leaf content-0.388×the content of andrographolide-1.117×the content of neoandrographolide-0.274×the content of deoxyandrographolide-0.287×the content of 14-deoxy-11,12-didehydro-andrographolide-0.302×the content of four active diterpenoids-0.104×the content of ethanol-soluble extractives-0.015×the color of stem-0.008 4×the color of leaf-0.003×the diameter of base part of stem+0.020×the number of branch+0.137×the diameter of the upper stem+0.011×plant height, if Y=0.7-1.3, the predicted quality was grade A, if Y=1.7-2.3, then B grade, and if Y=2.7-3.3, C grade or qualified product. Conclusion: The model of grade evaluation we constructed using principal component clustering analysis combing with PLS regression analysis performed well, which was applicable in evaluating the quality grade of Andrographis Herba and other traditional Chinese medicines. It also provided a new strategy for study on grade standards of traditional Chinese medicines.
Résumé
The growing interest of the pharmaceutical industry in Near Infrared-Chemical Imaging (NIR-CI) is a result of its high usefulness for quality control analyses of drugs throughout their production process (particularly of its non-destructive nature and expeditious data acquisition).In this work,the concentration and distribution of the major and minor components of pharmaceutical tablets are determined and the spatial distribution from the internal and external sides has been obtained.In addition,the same NIR-CI allowed the coating thickness and its surface distribution to be quantified.Images were processed to extract the target data and calibration models constructed using the Partial Least Squares (PLS) algorithms.The concentrations of Active Pharmaceutical Ingredient (API) and excipients obtained for uncoated cores were essentially identical to the nominal values of the pharmaceutical formulation.But the predictive ability of the calibration models applied to the coated tablets decreased as the coating thickness increased.