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    唐辉, 孔德鑫, 梁惠凌, 王满莲, 史艳财, 韦记青. 不同产地地枫皮的红外光谱和化学计量学快速评价[J]. 北京林业大学学报, 2012, 34(3): 137-141.
    引用本文: 唐辉, 孔德鑫, 梁惠凌, 王满莲, 史艳财, 韦记青. 不同产地地枫皮的红外光谱和化学计量学快速评价[J]. 北京林业大学学报, 2012, 34(3): 137-141.
    TANG Hui, KONG De-xin, LIANG Hui-ling, WANG Man-lian, SHI Yan-cai, WEI Ji-qing.. Rapid assessment of infrared spectroscopy and chemometrics of Illicium difengpi from different regions by fourier transform[J]. Journal of Beijing Forestry University, 2012, 34(3): 137-141.
    Citation: TANG Hui, KONG De-xin, LIANG Hui-ling, WANG Man-lian, SHI Yan-cai, WEI Ji-qing.. Rapid assessment of infrared spectroscopy and chemometrics of Illicium difengpi from different regions by fourier transform[J]. Journal of Beijing Forestry University, 2012, 34(3): 137-141.

    不同产地地枫皮的红外光谱和化学计量学快速评价

    Rapid assessment of infrared spectroscopy and chemometrics of Illicium difengpi from different regions by fourier transform

    • 摘要: 为实现不同产地地枫皮快速鉴别并筛选出高药效成分的种质,利用红外光谱结合主成分分析模型和簇类独立软模式法(SIMCA)对不同产地地枫皮药材进行检测,同时在主成分分析模型中提取载荷因子对其进行分析。结果显示:1)各产地红外光谱经过SNV标准归一化后,主成分分析模型中前3个主成分能够分析出代表87%的样品变量信息,样本在主成分空间中聚集成为7个不同的类别。2)SIMCA模型能成功地对不同产地未知地枫皮样本进行预测且判断准确率均达100%。3)载荷因子分析表明,A、B及E产地样本地枫皮素、厚朴醇及芳香类化合物的含量比其他产地高,不同产地样品中芳香类物质差异主要体现在槲皮素含量的不同。

       

      Abstract: In order to rapidly select the germplasm with high medical compositions as well as assessing the quality of Illicium difengpi from different regions, combined with the chemometrics software such as principal componential analysis model(PCA) and soft independent modeling of class analogy (SIMCA)), the fourier transform infrared spectroscopy(FTIR) was utilized to investigate I. difengpi from different regions. Meanwhile, extracting loading factors from PCA model were taken to analyze the differences of chemical composition among determined samples. The results were listed as follows: firstly, when the data of principal analysis model were preprocessed by standard normal variate(SNV),the first three principal components could account for 87% variance information in fingerprint and the samples were formed into 7 different categories in principal component space. Secondly, the identification model of SIMCA could be successfully applied to predict unknown samples from different regions and the recognition rate was up to 100%. Last but not lest, the loading factor analysis demonstrated that the content of difengpin,magnolol, aromatic mixture from A, B and E was higher than other regions among identified samples. In addition, the differences of aromatic mixture of I. difengpi mainly embodied in content difference of quercetin among determined samples in different regions.

       

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