A multiproduct approach toward method development is presented for a fast and reliable analysis of the eight most important cholesterol-lowering drugs via ultrahigh-performance supercritical fluid chromatography. A two-step approach based on design of experiments was applied: (1) selection of the stationary phase, organic modifier, and diluent in the mobile phase through a multilevel categorical design and (2) optimization of the elution strength by varying the pressure, temperature, and gradient using a central composite design. Finally, the flow rate was adjusted. The first design selected UPC 2 Torus 1-AA as the column, ethanol:water as the organic modifier, and acetonitrile:ethanol 3:2 v/v as the diluent. The results led to a pressure, column temperature, and gradient elution of 14.83 MPa, 42 • C, and 5-15.5% of ethanol:water in CO 2 , respectively. The flow rate was set at 1.8 mL/min, providing a total analysis time of 4 min. This multiproduct method was validated and applied to 11 different commercial products available in the Brazilian market, and it was found to be accurate, with r > 0.990, recoveries between 95 and 105%, and precision not higher than 5.4%. Therefore, this method was shown to be a greener alternative for the analysis of these pharmaceuticals.
This revision presents applications of multivariate curve resolution alternating least squares (MCR-ALS) applied to chromatographic data. Initially, the fundamentals and recent advances of the MCR-ALS method will be presented. Several critical issues such as data organization, advantages of the modelling, constraints, evaluation of ambiguity and the use for mathematical separation is discussed. An extensive revision of the papers on MCR-ALS applied to chromatographic data reported up to 2020 is presented. A practical example of an innovative application of cholesterol lowering drugs using supercritical fluid chromatography (SFC) is described highlighting important aspects of the method. At the end, a list of links to MCR-ALS algorithms and graphical interfaces developed in Matlab, R and Python 3 is provided.
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