Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
