College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
J Chromatogr A. 2011 Oct 7;1218(40):7219-25. doi: 10.1016/j.chroma.2011.08.010. Epub 2011 Aug 12.
The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).
迭代目标转换因子分析（ITTFA）中浓度向量的初始化和纯变量或关键变量的识别是多变量曲线分辨中的重要问题。在本研究中，采用不相似性分析和演化因子分析（EFA）相结合的方法，寻找选择性或关键变量，并随后通过 ITTFA 获得气相色谱/质谱（GC/MS）数据解析的浓度向量的初始估计。对于含有质谱高度相似的组分的系统，使用一种新的约束条件（将洗脱窗之外的元素设置为 0）来提高收敛速度和结果的准确性。通过两种蜡酯标准混合物和实际的 97#汽油 GC/MS 数据的测试，基于不相似性的 ITTFA 可以得到准确的结果（浓度向量的平均点积、峰面积比的平均偏差和质谱的平均相似度分别为 0.9929、0.0228 和 981.0）。
