Unsupervised Screening of Vibrational Spectra by Principal Component Analysis for Identifying Molecular Clusters

Johannes Kiefer*, Kristina Eisen

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Vibrational spectra are commonly used to study molecular interactions in solutions. However, the data analysis is often demanding and requires significant experience in order to obtain meaningful results. This study demonstrates that principal component analysis (PCA) can serve as an unsupervised tool for initial screening of non-ideal mixture systems. Taking the aqueous solutions of dimethyl sulfoxide (DMSO) as an example, PCA revealseasily and fastthe two prominent stoichiometries at 1:2 and 1:1 molar DMSO:water ratio and significantly outperforms elaborate spectral profile analysis or common algorithms as indirect hard modeling (IHM) or multivariate curve resolution (MCR). The corresponding molecular 1:1 and 1:2 clusters are known to be dominating configurations in the solutions.

Original languageEnglish
Pages (from-to)795-800
Number of pages6
JournalChemPhysChem
Volume19
Issue number7
Early online date23 Feb 2018
DOIs
Publication statusPublished - 5 Apr 2018

Keywords

  • chemometrics
  • hydrogen bonds
  • molecular clusters
  • solvent mixtures
  • vibrational spectroscopy
  • DIMETHYL-SULFOXIDE
  • HYDROGEN-BONDS
  • WATER
  • SPECTROSCOPY
  • DMSO
  • MIXTURES
  • CURVE

Cite this

Unsupervised Screening of Vibrational Spectra by Principal Component Analysis for Identifying Molecular Clusters. / Kiefer, Johannes; Eisen, Kristina.

In: ChemPhysChem, Vol. 19, No. 7, 05.04.2018, p. 795-800.

Research output: Contribution to journalArticle

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