Issue 6, 2008

Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis

Abstract

Four bacteria, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus warneri, and Micrococcus luteus, were grown at temperatures of 23, 30, and 37 °C and were characterized by pyrolysis-gas chromatography/differential mobility spectrometry (Py-GC/DMS) providing, with replicates, 120 data sets of retention time, compensation voltage, and ion intensity, each for negative and positive polarity. Principal component analysis (PCA) for 96 of these data sets exhibited clusters by temperature of culture growth and not by genus. Analysis of variance was used to isolate the constituents with dependences on growth temperature. When these were subtracted from the data sets, Fisher ratios with PCA resulted in four clusters according to genus at all temperatures for ions in each polarity. Comparable results were obtained from unsupervised PCA with 24 of the original data sets. The ions with taxonomic features were reconstructed into 3D plots of retention time, compensation voltage, and Fisher ratio and were matched, through GC-mass spectrometry (MS), with chemical standards attributed to the thermal decomposition of proteins and lipid A. Results for negative ions provided simpler data sets than from positive ions, as anticipated from selectivity of gas phase ion-molecule reactions in air at ambient pressure.

Graphical abstract: Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis

Article information

Article type
Paper
Submitted
23 Oct 2007
Accepted
07 Feb 2008
First published
04 Mar 2008

Analyst, 2008,133, 760-767

Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis

S. Prasad, K. M. Pierce, H. Schmidt, J. V. Rao, R. Güth, R. E. Synovec, G. B. Smith and G. A. Eiceman, Analyst, 2008, 133, 760 DOI: 10.1039/B716371A

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