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Differentiation and comparison of Wolfiporia cocos raw materials based on multi-spectral information fusion and chemometric methods.


ABSTRACT: In order to achieve the target of deeper insight into the differentiation and comparison of Wolfiporia cocos, a total of 350 samples including distinct growth patterns, various collection regions and different medicinal parts were investigated using multi-spectral information fusion based on ultraviolet (UV) and Fourier transform infrared (FT-IR) spectroscopies coupled with chemometrics. From the results, the discrimination of samples was obtained successfully and good classification performances were shown according to partial least squares discriminant analysis (PLS-DA) models. Comparatively, the distinctness of chemical information in the two medicinal parts of W. cocos were much more than that in the same part with different growth patterns and collection areas. Meanwhile, an interesting finding suggested that growth patterns rather than geographical origins could be the dominant factor to effect the chemical properties of the same part samples, especially for the epidermis. Compared with the epidermis samples, there were better quality consistency for the inner part of W. cocos. Totally, this study demonstrated that the developed method proved to be reliable to perform comparative analysis of W. cocos. Moreover, it could provide more comprehensive chemical evidence for the critical supplement of quality assessment on the raw materials of W. cocos.

SUBMITTER: Li Y 

PROVIDER: S-EPMC6115471 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Differentiation and comparison of Wolfiporia cocos raw materials based on multi-spectral information fusion and chemometric methods.

Li Yan Y   Wang Yuanzhong Y  

Scientific reports 20180829 1


In order to achieve the target of deeper insight into the differentiation and comparison of Wolfiporia cocos, a total of 350 samples including distinct growth patterns, various collection regions and different medicinal parts were investigated using multi-spectral information fusion based on ultraviolet (UV) and Fourier transform infrared (FT-IR) spectroscopies coupled with chemometrics. From the results, the discrimination of samples was obtained successfully and good classification performance  ...[more]

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