Multivariate Curve Resolution (MCR) methods include a number of techniques designed to extract the underlying response profiles (e.g. pure component spectra, concentration time profiles) from spectroscopic mixture data. Also known as Self-Modeling Mixture Analysis (SMMA), these semi-quantitative methods are used to resolve mixtures into physically meaningful components. Unlike typical quantification methods, MCR attempts to determine the composition of the mixtures without, or with incomplete prior knowledge of the components of the system or their response in the variables (i.e. “pure-component spectra”).
This course discusses the relationship of MCR to Classical Least Squares (CLS) and Principal Component Analysis (PCA) and covers a number of MCR methods. Central to this course’s objectives are an understanding of the challenges in MCR and how the different MCR approaches can be applied depending on the information that is known about the system under study. Hands-on exercises will be done using PLS_Toolbox/Solo.