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茶叶化学成分指纹识别中样品制备方法的优化

Experimental Optimization of Sample Pretreatment for Fingerprinting of Chemical Components in Tea

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【作者】 赵蕊; 欧阳捷; 杜树山; 耿珠峰; 许美凤; 邓志威;

【Author】 ZHAO Rui1,OUYANG Jie1,DU Shu-Shan2,GENG Zhu-Feng1,XU Mei-Feng1,DENG Zhi-Wei1(1. Analytical & Testing Center,Beijing Normal University,Beijing 100875,China;2.Center for Natural Medicine Engineering,Ministry of Education,College of Resources Science & Technology,Beijing Normal University,Beijing 100088,China)

【机构】 北京师范大学分析测试中心; 北京师范大学资源学院,教育部资源药物工程研究中心;

【摘要】 根据指纹识别实验对样品的要求,以1H NMR和主成分分析(PCA)为测试与评估手段,对茶叶中化学成分提取条件的优化方法进行了研究.针对生物指纹识别实验中样本体系复杂及化学组分含量多等特点,利用主成分分析对检测样品质控的多因素进行综合分析,最终确立了茶叶中化学成分提取制备的实验条件.利用所建立的样品制备程序对4种茶叶进行预处理和测试,结果表明,所建立的方法稳定、可靠,可以满足茶叶代谢物1H NMR指纹识别研究的要求.本文所提出的优化方法简单可靠,可用于复杂样本体系标准样品预处理方法的建立.

【Abstract】 A certain pattern recognition method was suggested to optimize the experimental conditions of the sample pretreatment for chemical finger-print experiments. 1H NMR plus Principle component analysis(PCA) were used as the testing and evaluating means,considering that the test samples of finger-print experiments usually consist of multi-components. The data exposed in 1H NMR spectra were wholly involved in PCA to provide the predictable information for the design of the sample pretreatment. With this sleight,we optimized the experimental conditions such as the prescription of extraction solution,ratio between solution and sample,experimental temperature and extraction duration,and proposed a standard procedure of the sample pretreatment for 1H NMR finger-print experiments of tea. Finally,the newly designed procedure was tested with four tea samples and the 1H NMR spectra obtained were well matched the requirements of chemical finger-print experiments. The results show that this method is reliable and can be used in designing a standard procedure of sample pretreatments for complicated testing system.

【基金】 国家科技支撑计划课题(批准号:2007BAI48B10);北京市新医药学科群重点支持项目(批准号:xk100270569)资助
  • 【文献出处】 高等学校化学学报 ,Chemical Journal of Chinese Universities , 编辑部邮箱 ,2010年01期
  • 【分类号】TS272.7
  • 【被引频次】8
  • 【下载频次】383
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