Independent components in spectroscopic analysis of complex mixtures

Here you can find original experimental data and simple matlab scripts to reproduce results of our paper

Chemometrics and Intelligent Laboratory Systems, 103, 108-115

Independent components in spectroscopic analysis of complex mixtures (preprint, doi)

Yulia B. Monakhova , Sergey A. Astakhov, Alexander Kraskov, Svetlana P. Mushtakova

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

Fig 1 and 6: mixtures, sources (o-xylene, toluene, benzene), figure

Fig 2: mixtures, sources (anthracene, phenantrene, benz[a]phenantrene, benz[a]antracene), figure

Fig 5: mixtures, sources (tryptophan, tyrosine), figure

Matlab scripts to reproduce the figures (fig1.m, fig2.m, fig5.m)

Archive with all information.

Please contact Yulia Monakhova (yul-monakhova at mail.ru) if you have questions about the paper, or Alexander Kraskov (akraskov at ion.ucl.ac.uk) if you have questions about codes