熵最小算法解析玫瑰精油GC/MS混合谱中的未知组分

Analysis of Unknown Components in Rose Essential Oil by Entropy Minimization Algorithm

  • 摘要: 熵最小算法是一种化学计量学算法,它根据实验数据本身就可以从混合谱中重建出纯谱。本研究采用熵最小算法对玫瑰精油GC/MS图谱中的2个共流出峰进行分析,从这些共流出峰中重建出8个纯谱和它们的总离子流浓度,并通过NIST数据库比对定性了其中4个组分。熵最小算法能够快速分析共流出峰中的组分,提高天然产物的分析效率,可应用于天然产物、中药、食品等体系的快速分析。

     

    Abstract: Entropy minimization algorithm (EM) is a chemometrics method, which reconstructs pure spectra out from mixture spectra based on experimental data only, and no prior information needed. A rose essential oil was analyzed by GC/MS, two co-eluted mixture spectra were analyzed by EM, and unknown pure spectra were estimated and identified. The result shows that 8 pure spectra and their TIC concentration are estimated, and 4 of these pure spectra are identified by NIST library match in these two coeluted peaks. EM algorithm can analyze unknown components inside co-eluted peaks quickly, enhance the efficiency of nature product analyses, which is applicable in quick analyses of nature product, traditional Chinese medicine and food etc.

     

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