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

Yulia B. Monakhova, Douglas N. Rutledge

    Research output: Contribution to journalReview articlepeer-review

    8 Citations (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

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