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Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

机译:药物的超临界流体色谱:在广泛的操作条件下进行柱测试的并行因子分析

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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.
机译:超临界流体色谱(SFC)涉及的保留机制受相互依赖的参数(温度,压力,流动相的化学性质和固定相的性质)影响,这种复杂性使得必须为给定的分离选择合适的固定相。具有挑战性的一步。在SFC研究中,首次使用平行因子分析(PARAFAC)来评估在宽范围的色谱条件(温度,压力和梯度洗脱组成)下八个不同固定相的色谱行为。实验设计用于优化涉及生物和/或环境样品中存在的,具有不同理化特性的14种药物化合物的实验。结果表明,PARAFAC在分析三向(列×药物×条件)数据阵列方面优于将多向阵列展开为矩阵并进行几种经典主成分分析。得益于PARAFAC组分,可以简单地描述色谱柱功能的相似性,药物的色谱趋势以及分离条件之间的相关性:色谱柱根据其H键合力进行分组,而梯度成分则主要用于条件分类。同样,可以有效地减少用于列分类的药物数量,因为其中一些药物表现出相似的行为,如基于PARAFAC组件的层次聚类所示。

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