Russell A. Putnam Rehse Group Department of Physics, University of Windsor

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Chemometric Data Analysis Strategies for Optimizing Pathogen Discrimination and Classification Using Laser-Induced Breakdown Spectroscopy (LIBS) Emission Spectra. Russell A. Putnam Rehse Group Department of Physics, University of Windsor Windsor, Ontario, Canada. Previous paper. 2012.
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Chemometric Data Analysis Strategies for Optimizing Pathogen Discrimination and Classification Using Laser-Induced Breakdown Spectroscopy (LIBS) Emission SpectraRussell A. PutnamRehse GroupDepartment of Physics, University of WindsorWindsor, Ontario, CanadaPrevious paper2012LIBS ON BacteriaDFA on 13 emission linesNew study
  • SAME DATA BUT WITH NEW TECHNIQUES AND NEW MODELS
  • RM0, RM1, and RM2
  • Principle Least Squares Discriminant Analysis (PLSDA) vs Discriminant Function Analysis (DFA)
  • The motivation for this work came from De Lucia et al. (explosives)
  • RM0 vs RM1 vs RM2PLSDA vs DFAThe 3 models; rm0, rm1, and rm23 different down-selected models used as independent variables for our analysis
  • RM0 – (lines) the 13 strong emission lines observed in the bacterial spectra (13 independent variables)
  • RM1 – sums the 5 elements observed and ratios of the sums (24 independent variables)
  • RM2 – the 13 strong emission lines and ratios of the lines (80 independent variables)
  • Whole spectrum analysis not performed
  • Over 54,000 channels (SPSS cannot handle)
  • Presence of Échelle spectral gaps
  • DFA on 3 modelsExternal ValidationComparing PLSDA and DFA
  • PLSDA (Principle Least Squares Discriminant Analysis)
  • 2 class, YES or NO test
  • 1 predictor value
  • Has a NO option
  • DFA (Discriminant Function Analysis)
  • 5 class test
  • N discriminant function scores
  • Must classify each spectrum into a group
  • DFAConclusion
  • Both routines provide effective classification of unknown LIBS spectra shown by the high specificity and sensitivity
  • Both ratio models showed improved classification over the lines model, with RM2 (lines and simple ratios) showing slightly improved classification over RM1 (sums and complex sum ratios)
  • PLSDA proved to be more effective at differentiating highly similar bacterial spectra
  • DFA showed lower rates of false positives and could be the analysis of choice to discriminate between multiple genera of bacteria
  • Future work
  • Exhausted current data
  • In process of obtaining new data with a refined experimental method
  • Possibilities
  • Sequential PLSDA for strain discrimination
  • Multistep combination of PLSDA and DFA
  • DFA Specie Level TestPLSDA Strep TestDFA Genus TestYes, also strep!Data setStrepVerificationIdentificationPLSDA Sequential Specie Level TestChemometric Data Analysis Strategies for Optimizing Pathogen Discrimination and Classification Using Laser-Induced Breakdown Spectroscopy (LIBS) Emission SpectraThank you!Questions?
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