OPLS methods for the analysis of hyperspectral images—comparison with MCR‐ALS

Abstract

Two new, orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization of multiple chemical compounds in one matrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR‐ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS‐based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm (pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR‐ALS method enabled easier visual interpretation compared to the OPLS‐based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound. Copyright © 2014 John Wiley & Sons, Ltd.