Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

Yulia B. Monakhova, Douglas N. Rutledge

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.
Original languageEnglish
Article number120451
Pages (from-to)1-8
Number of pages8
JournalTalanta
Volume208
Early online date05 Oct 2019
DOIs
Publication statusPublished - 01 Feb 2020

Fingerprint Dive into the research topics of 'Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry'. Together they form a unique fingerprint.

  • Cite this