Band Target Entropy Minimization and Target Partial Least Squares for Spectral Recovery and Calibration

TL;DR

This paper introduces a novel combination of band target entropy minimization and target partial least squares to improve spectral recovery and calibration of minor components in chemical mixtures without prior knowledge.

Contribution

It presents a new method combining BTEM and T-PLS that allows targeted estimation of minor components and their concentrations in spectral data, overcoming limitations of existing methods.

Findings

The method produces estimates comparable to MCR-ALS on simple datasets.

It successfully models minor components in complex datasets where MCR-ALS fails.

The approach enables one-at-a-time calibration without prior pure component identification.

Abstract

The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS could model the pure component spectra and obtain concentration profiles of minor components but MCR-ALS could not.