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

TALANTA

卷:208

Review

Monakhova, Yulia B.^1,2,3  Rutledge, Douglas N.^4,5 

[1] Spectral Serv AG, Emil Hoffmann Str 33, D-50996 Cologne, Germany

[2] Saratov NG Chernyshevskii State Univ, Inst Chem, Astrakhanskaya St 83, Saratov 410012, Russia

[3] St Petersburg State Univ, Inst Chem, 13B Univ Skaya Emb, St Petersburg 199034, Russia

[4] Univ Paris Saclay, INRA, AgroParisTech, UMR Ingn Proc Aliments, Massy, France

[5] Charles Sturt Univ, Natl Wine & Grape Ind Ctr, Wagga Wagga, NSW, Australia

关键词: Independent components analysis;    Chemometrics;    Spectroscopy;    Chromatography;   

DOI  :  10.1016/j.talanta.2019.120451

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free