Application of modified alternating least squares regression to spectroscopic image analysis Original Research Article

Analysis of synthetically generated and real Raman imaging data sets were used to show the significance of modified alternating least squares (MALS) regression as a superior method of analysis compared with several other well established mathematical algorithms. The performance of MALS was compared with that of ordinary alternating least squares regression (ALS) and fast non-negative least squares (FNNLS) regression in applications of spectroscopic image analysis and self-modeling curve resolution (SMCR). The MALS algorithm is shown to be superior in terms of computational speed, stability, and component resolution ability in the analysis of both real and synthetic data sets. Results of the analysis show that MALS is significantly faster than FNNLS and generally produces equivalent or superior results. This work also shows that MALS is superior to ordinary ALS in all performance aspects. A detailed description of the regression equations is given along with a discussion of the application of MALS to the general spectroscopic image analysis problem.