Independent components in spectroscopic analysis of complex mixtures

TL;DR

This study demonstrates that independent component analysis methods MILCA and SNICA effectively decompose complex spectroscopic mixtures, accurately recovering component spectra and concentrations, often outperforming traditional chemometric techniques.

Contribution

The paper introduces the application of MILCA and SNICA for spectroscopic mixture analysis, showing their effectiveness and potential advantages over existing methods.

Findings

Both methods accurately recovered component spectra and concentrations.

Performance was comparable or superior to other chemometric techniques.

Methods are suitable for real-world spectroscopic applications.

Abstract

We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to quantitative and qualitative analysis of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications. Data used in this paper along with simple matlab codes to reproduce paper figures can be found at http://www.klab.caltech.edu/~kraskov/MILCA/spectra