Application of multivariate analysis and vibrational spectroscopy in classification of biological systems

A. Salman, E. Shufan, I. Lapidot, L. Tsror, L. Zeiri, R. K. Sahu, R. Moreh, S. Mordechai, M. Huleihel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fourier Transform Infrared (FTIR) and Raman spectroscopies have emerged as powerful tools for chemical analysis. This is due to their ability to provide detailed information about the spatial distribution of chemical composition at the molecular level. A biological sample, i.e. bacteria or fungi, has a typical spectrum. This spectral fingerprint, characterizes the sample and can therefore be used for differentiating between biology samples which belong to different groups, i.e., several different isolates of a given fungi. When the spectral differences between the groups are minute, multivariate analysis should be used to provide a good differentiation. We hereby review several results which demonstrate the differentiation success obtained by combining spectroscopy measurements and multivariate analysis.

Original languageEnglish
Title of host publicationInternational Conference of Computational Methods in Sciences and Engineering 2015, ICCMSE 2015
EditorsZacharoula Kalogiratou, Theodore E. Simos, Theodore Monovasilis, Theodore E. Simos, Theodore E. Simos
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735413498
DOIs
StatePublished - 31 Dec 2015
EventInternational Conference of Computational Methods in Sciences and Engineering 2015, ICCMSE 2015 - Athens, Greece
Duration: 20 Mar 201523 Mar 2015

Publication series

NameAIP Conference Proceedings
Volume1702
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Conference of Computational Methods in Sciences and Engineering 2015, ICCMSE 2015
Country/TerritoryGreece
CityAthens
Period20/03/1523/03/15

Keywords

  • Machine learning
  • Vibrational spectroscopy
  • biological samples
  • multivariate analysis
  • supervised and unsupervised methods

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