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

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.

Comprehensive chromatographic profiling of cannabis from 23 USA States marketed for medical purposes

“…As a promising field combining computer science and analytical chemistry, chemometrics has increasingly found application in natural products chemistry, and has been used extensively for analytical data mining, graphical visualization, and class discrimination and prediction [37][38][39][40][41]. An important step in this research is data analysis where mathematical algorithms were used to extract useful information from huge data sets obtained from GC-FID as will be shown in the following section.…”

“…The method was validated and evaluated for selectivity and precision (i.e., repeatability). The advanced multivariate tools including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are efficient towards sample clustering [36][37][38][39][40][41]. Hence, these tools were performed in this study to; (1) identify the compounds most important in distinguishing cannabis varieties, (2) find the variation on cannabis chemical profiles as a result of growing plants in different States and with different in growth times, (3) confirm whether the cultivars (i.e., States) in the cluster analysis would also be grouped together, (4) reveal the compounds that were responsible for grouping cultivars between clusters and (5) develop a database that can predict the origins and type of unknown cannabis grown in the USA.…”

Development and Validation of Gas Chromatography-Triple Quadrupole Mass Spectrometric Method for Quantitative Determination of Regulated Plasticizers in Medical Infusion Sets

“…Several analytical methods capable of detecting and quantifying the alternative plasticizers in medical devices have been developed and validated by means of separative and nonseparative methods up to date [ 18 ]. The separative methods have been adopted with gas chromatography, supercritical fluid chromatography [ 19 ], and liquid chromatography combined with various detectors such as mass spectrometer, flame ionization detector, evaporative light scattering detector, or UV techniques. Nonseparative methods have been performed on a nuclear magnetic resonance and Fourier transform infrared spectrometry [ 20 , 21 ].…”