Al Bakain Ramia Z, Al-Degs Yahya, Andri Bertyl, Thiébaut Didier, Vial Jérôme, Rivals Isabelle
Department of Chemistry, Faculty of Science, The University of Jordan, P.O. Box 11942, Amman, Jordan.
Chemistry Department, The Hashemite University, P.O. Box 150459, Zarqa, Jordan.
J Anal Methods Chem. 2017;2017:5340601. doi: 10.1155/2017/5340601. Epub 2017 Jun 11.
Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was employed to evaluate the chromatographic behavior of eight different stationary phases in a wide range of chromatographic conditions (temperature, pressure, and gradient elution composition). Design of Experiment was used to optimize experiments involving 14 pharmaceutical compounds present in biological and/or environmental samples and with dissimilar physicochemical properties. The results showed the superiority of PARAFAC for the analysis of the three-way (column × drug × condition) data array over unfolding the multiway array to matrices and performing several classical principal component analyses. Thanks to the PARAFAC components, similarity in columns' function, chromatographic trend of drugs, and correlation between separation conditions could be simply depicted: columns were grouped according to their H-bonding forces, while gradient composition was dominating for condition classification. Also, the number of drugs could be efficiently reduced for columns classification as some of them exhibited a similar behavior, as shown by hierarchical clustering based on PARAFAC components.
超临界流体色谱法（SFC）中的保留机制受相互依存的参数（温度、压力、流动相化学性质和固定相性质）影响，这种复杂性使得为特定分离选择合适的固定相成为一个具有挑战性的步骤。在SFC研究中，首次采用平行因子分析（PARAFAC）来评估八种不同固定相在广泛色谱条件（温度、压力和梯度洗脱组成）下的色谱行为。实验设计用于优化涉及生物和/或环境样品中存在的14种具有不同物理化学性质的药物化合物的实验。结果表明，对于三元（柱×药物×条件）数据阵列的分析，PARAFAC优于将多路阵列展开为矩阵并进行多次经典主成分分析。借助PARAFAC成分，可以简单地描述柱功能的相似性、药物的色谱趋势以及分离条件之间的相关性：柱根据其氢键作用力进行分组，而梯度组成在条件分类中起主导作用。此外，对于柱分类，可以有效地减少药物数量，因为其中一些药物表现出相似的行为，基于PARAFAC成分的层次聚类显示了这一点。
