利用熵最小算法快速定性定量分析GC-MS混合谱中未知组分

Quick qualification and quantification analysis of unknown compounds on GC-MS mixture spectra using entropy minimization algorithms

定性定量分析复杂组分中未知混合次要成分是非常困难的。常用的色谱质谱联用分析方法,对于其中的次要成分混合物峰,由于分开它们耗时耗力,所以较少进行深入研究。本文利用熵最小算法,以一个合成航空煤油的GC-MS谱图为例,对其中的某个混合物峰,利用熵最小算法,对其中含有的各个组分进行快速定性定量分析。结果显示,利用熵最小算法,不但能定性未知物,对未知物的定量也非常有用。利用熵最小算法帮助分离混合物谱图,能大大减少现行化学分析中组分分离的繁琐步骤。

It is a very difficult task to do qualification and quantification analysis on unknown minor components inside complex mixture. Currently chromatography separation techniques has difficult in separation minor components out from mixture spectra, since tremendous time and cost will raise, therefore most minor components in mixture spectra always be ignored. In this work, entropy minimization algorithms have been used to reconstruct minor unknown compounds out from mixture, and do qualification and quantification analysis of these unknown compounds as well. A GC-MS mixture spectrum from a synthesized jet fuel was used to do demonstrated here. The results show that entropy minimization algorithm is a great help in separation minor components inside complex mixtures, and save great time and cost for analyzers.