Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples

Denis Svechkarev, Marat R. Sadykov, Lucas J. Houser, Kenneth W. Bayles, Aaron M. Mohs

Research output: Contribution to journalArticle

Abstract

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.

Original languageEnglish (US)
Article number916
JournalFrontiers in Chemistry
Volume7
DOIs
StatePublished - Jan 15 2020

Keywords

  • 3-hydroxyflavone
  • ESIPT
  • discriminant analysis
  • machine learning
  • multiparametric sensing
  • pathogenic bacteria
  • pattern analysis

ASJC Scopus subject areas

  • Chemistry(all)

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