Debrus Benjamin, Lebrun Pierre, Ceccato Attilio, Caliaro Gabriel, Govaerts Bernadette, Olsen Bernard A, Rozet Eric, Boulanger Bruno, Hubert Philippe
Laboratory of Analytical Chemistry, CIRM, Department of Pharmacy, University of Liège, Avenue de l'Hôpital 1, B36, B-4000 Liège, Belgium.
Talanta. 2009 Jun 30;79(1):77-85. doi: 10.1016/j.talanta.2009.03.009. Epub 2009 Mar 14.
One of the major issues within the context of the fully automated development of chromatographic methods consists of the automated detection and identification of peaks coming from complex samples such as multi-component pharmaceutical formulations or stability studies of these formulations. The same problem can also occur with plant materials or biological matrices. This step is thus critical and time-consuming, especially when a Design of Experiments (DOE) approach is used to generate chromatograms. The use of DOE will often maximize the changes of the analytical conditions in order to explore an experimental domain. Unfortunately, this generally provides very different and "unpredictable" chromatograms which can be difficult to interpret, thus complicating peak detection and peak tracking (i.e. matching peaks among all the chromatograms). In this context, Independent Components Analysis (ICA), a new statistically based signal processing methods was investigated to solve this problem. The ICA principle assumes that the observed signal is the resultant of several phenomena (known as sources) and that all these sources are statistically independent. Under those assumptions, ICA is able to recover the sources which will have a high probability of representing the constitutive components of a chromatogram. In the present study, ICA was successfully applied for the first time to HPLC-UV-DAD chromatograms and it was shown that ICA allows differentiation of noise and artifact components from those of interest by applying clustering methods based on high-order statistics computed on these components. Furthermore, on the basis of the described numerical strategy, it was also possible to reconstruct a cleaned chromatogram with minimum influence of noise and baseline artifacts. This can present a significant advance towards the objective of providing helpful tools for the automated development of liquid chromatography (LC) methods. It seems that analytical investigations could be shortened when using this type of methodologies.
在色谱方法全自动化开发背景下的一个主要问题，在于对来自复杂样品（如多组分药物制剂或这些制剂的稳定性研究）的峰进行自动检测和识别。同样的问题在植物材料或生物基质中也可能出现。因此，这一步骤至关重要且耗时，尤其是当采用实验设计（DOE）方法来生成色谱图时。使用DOE通常会使分析条件的变化最大化，以便探索一个实验域。不幸的是，这通常会产生非常不同且“不可预测”的色谱图，难以解释，从而使峰检测和峰跟踪（即在所有色谱图中匹配峰）变得复杂。在此背景下，研究了一种基于统计的新信号处理方法——独立成分分析（ICA）来解决这个问题。ICA原理假定观测信号是几种现象（称为源）的结果，并且所有这些源在统计上是独立的。在这些假设下，ICA能够恢复很有可能代表色谱图组成成分的源。在本研究中，ICA首次成功应用于HPLC - UV - DAD色谱图，结果表明，通过应用基于对这些成分计算的高阶统计量的聚类方法，ICA能够区分噪声和伪影成分与感兴趣的成分。此外，基于所描述的数值策略，还能够重建受噪声和基线伪影影响最小的净化色谱图。这对于为液相色谱（LC）方法的自动化开发提供有用工具这一目标而言，可能是一个重大进展。使用这类方法似乎可以缩短分析研究时间。
