Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions. (11th June 2017)

• Record Type: Journal Article
• Authors: Al Bakain, Ramia Z.; Al-Degs, Yahya; Andri, Bertyl; Thiébaut, Didier; Vial, Jérôme; Rivals, Isabelle
• Other Names: Aboul Enein Hassan Y. Academic Editor.
• Is Part Of: Journal of analytical methods in chemistry. Volume 2017(2017)
• Publication Date: 2017-06-11
• Subjects: Chemistry, Analytic -- Periodicals; Chemistry, Analytic -- Technique -- Periodicals; 543.05
• DOI: 10.1155/2017/5340601
• Languages: English
• ISSNs: 2090-8865

Abstract

Abstract : 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.