Institute of Chemistry, University of Campinas (Unicamp), Campinas, São Paulo, Brazil.
Anal Chim Acta. 2012 Jun 20;731:11-23. doi: 10.1016/j.aca.2012.04.003. Epub 2012 Apr 11.
This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) and coupled to mass spectrometry (GC×GC-MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.
本文综述了自 2000 年以来发表的文献中，结合气相色谱 (GC) 数据，迭代和非迭代多元曲线分辨 (MCR) 方法的主要优点和缺陷，并重点介绍了与质谱 (GC-MS) 联用的 GC 最主要的组合，以及与火焰电离检测 (GC×GC-FID) 和质谱 (GC×GC-MS) 联用的全二维气相色谱。此外，还简要总结了一些预处理策略，以校正 GC 中的常见问题，例如保留时间漂移和基线/背景贡献。此外，本文还将介绍演进因子分析 (EFA)、启发式演进潜投影 (HELP)、子窗口因子分析 (SFA)、多元曲线分辨交替最小二乘法 (MCR-ALS)、正定矩阵因子分解 (PMF)、迭代目标变换因子分析 (ITTFA) 和正交投影分辨 (OPR) 等算法。此外，还将展示食品化学、脂质组学和药物化学以及精油研究等领域的应用实例。最后，还将简要说明 MCR 方法层次结构。
