Dumarey M, Put R, Van Gyseghem E, Vander Heyden Y
Department of Analytical Chemistry and Pharmaceutical Technology, Vrije Universiteit Brussel - VUB, Laarbeeklaan 103, B-1090 Brussels, Belgium.
Anal Chim Acta. 2008 Feb 25;609(2):223-34. doi: 10.1016/j.aca.2007.12.047. Epub 2008 Jan 15.
Developing an analytical separation procedure for an unknown mixture is a challenging issue. An important example is the separation and quantification of a new drug and its impurities. One approach to start method development is the screening of the mixture on dissimilar chromatographic systems, i.e. systems with large selectivity differences. After screening, the most suited system is retained for further method development. In a step prior to such strategy dissimilar chromatographic systems need to be selected. In this paper the performance of different chemometric selection approaches, described in the literature, was visually evaluated and compared. Additionally, orthogonal projection approach (OPA) was tested as another potential selection method. All techniques, including the OPA method, were able to select (a set of) dissimilar chromatographic systems and many similarities between the selections were observed. However, the Kennard and Stone algorithm performed best in selecting the most dissimilar systems in the earliest steps of the selection procedure. The generalized pairwise correlation method (GPCM) and the auto-associative multivariate regression trees (AAMRT) were also performing well. OPA and weighted pair group method using arithmetic averages (WPGMA) are less preferable.
为未知混合物开发一种分析分离程序是一个具有挑战性的问题。一个重要的例子是新药及其杂质的分离和定量。开始方法开发的一种方法是在不同的色谱系统上对混合物进行筛选,即具有较大选择性差异的系统。筛选后,保留最适合的系统用于进一步的方法开发。在实施该策略之前的一个步骤中,需要选择不同的色谱系统。本文对文献中描述的不同化学计量学选择方法的性能进行了直观评估和比较。此外,还测试了正交投影法(OPA)作为另一种潜在的选择方法。所有技术,包括OPA方法,都能够选择(一组)不同的色谱系统,并且在选择之间观察到许多相似之处。然而,肯纳德和斯通算法在选择过程的最早步骤中选择最不同系统方面表现最佳。广义成对相关法(GPCM)和自联想多元回归树(AAMRT)也表现良好。OPA和使用算术平均值的加权成对组法(WPGMA)不太可取。