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Application of variable selection in the origin discrimination of Wolfiporia cocos (F.A. Wolf) Ryvarden Gilb. based on near infrared spectroscopy

机译:变量选择在狼尾草(F.A. Wolf)Ryvarden&Gilb的起源鉴别中的应用。基于近红外光谱

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摘要

Dried sclerotium of Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. is a traditional Chinese medicine. Its chemical components showed difference among geographical origins, which made it difficult to keep therapeutic potency consistent. The identification of the geographical origin of W. cocos is the fundamental prerequisite for its worldwide recognition and acceptance. Four variable selection methods were employed for near infrared spectroscopy (NIR) variable selection and the characteristic variables were screened for the establishment of Fisher function models in further identification of the origin of W. cocos from Yunnan, China. For the obvious differences between poriae cutis (fu-ling-pi in Chinese, or FLP) and the inner part (bai-fu-ling in Chinese, or BFL) of the sclerotia of W. cocos in the pattern space of principal component analysis (PCA), we established discriminant models for FLP and BFL separately. Through variable selection, the models were significant improved and also the models were simplified by using only a small part of the variables. The characteristic variables were screened (13 for BFL and 10 for FLP) to build Fisher discriminant function models and the validation results showed the models were reliable and effective. Additionally, the characteristic variables were interpreted.
机译:Wolfiporia cocos(F.A. Wolf)Ryvarden&Gilb的干菌核。是中药。其化学成分在地理来源之间显示出差异,这使得难以保持治疗效力的一致性。 W. cocos地理起源的识别是其在世界范围内得到认可和接受的基本前提。四种变量选择方法用于近红外光谱(NIR)变量选择,并筛选特征变量以建立Fisher函数模型,以进一步鉴定来自中国云南的椰子。在主要成分分析的模式空间中,co的硬皮病(中文为Ful-ling-pi,与FLP)和菌核的内部(中文为Bai-fu-ling,或BFL)之间存在明显差异。 (PCA),我们分别为FLP和BFL建立了判别模型。通过变量选择,模型得到了显着改进,并且仅通过使用一小部分变量就简化了模型。筛选特征变量(BFL为13,FLP为10)以建立Fisher判别函数模型,验证结果表明该模型是可靠且有效的。另外,解释了特征变量。

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