A Study on Consistency Evaluation of Preparation of Sinomenine Hydrochloride Based on Multi-source Data Fusion Technique

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Abstract

To investigate the quality difference of sinomenine hydrochloride prepared with different extraction solvents and explore the feasibility of changing the extraction process,the quality of sino⁃ menine hydrochloride samples prepared by benzene and acid water percolation-chloroform extrac⁃ tion were systematically evaluated using near-infrared spectroscopy,Raman spectroscopy combined with multi-source data fusion technique. Subsequently, the potential effects of the two solvents on product were compared.The overall quality of the samples were characterized with the different sta⁃ tistics named similarity matching value(SMV),Hotelling T2 and DModX,under three fusion modes of low, medium and high levels. Under the low level fusion mode, the results showed that the DModX values of nine samples exceeded the control limit.Under the medium level fusion mode,the SMV of one sample was lower than the control limit.Under the high level fusion mode,the SMVs of three samples were below the control limit, and the DModX values of five samples were above the control limit. Compared with the samples obtained by acid water percolation-chloroform extraction (obtained from manufacturer A), the quality consistency of the samples prepared by benzene(ob⁃ tained from manufacturer B)and chloroform extraction(obtained from manufacturer C)is good,ex⁃ cept that several samples need arbitration analysis for quality confirmation.In the process of sinome⁃ nine extraction,chloroform with low toxicity could be used to replace benzene which is harmful to hu⁃ man health.The study is aimed to develop a new strategy for evaluating the quality difference of tradi⁃ tional Chinese medicine products before and after the change of preparation process ..

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Wu, S. J., Wang, L., Wu, H. G., He, X. L., Qiu, P., Li, Z., & Li, W. L. (2022). A Study on Consistency Evaluation of Preparation of Sinomenine Hydrochloride Based on Multi-source Data Fusion Technique. Journal of Instrumental Analysis, 41(1), 121–127. https://doi.org/10.19969/j.fxcsxb.21093007

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