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 degrees 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.

Detection of infectious agents in the airways by ion mobility spectrometry of exhaled breath

“…The major parameters are summarized elsewhere [2, 3, 21-24, 74,76,79,80]. In this spectrometer a 550 MBq [63]Ni ß-radiation source was applied for the ionization of the carrier gas (air). It is connected to a polar multi-capillary column (MCC, type OV-5, Multichrom Ltd, Novosibirsk, Russia) used as the pre-separation unit.…”

“…More recently, the detection of different VOCs and their relations to medical questions was reported, including ion mobility spectrometry [22, [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. Some of the VOCs were related to bacteria taken from headspace of cultures [58,72,73].…”

Usefulness of pyrolysis coupled to gas chromatography/mass spectrometry for evaluating the reproducibility of commercial samples of Cymbopogon citratus Stapf., Poaceae

“…The range of applicability of the technique is illustrated by its use in the forensic field for the differentiation of paints (Kochanowski & Morgan, 2000;Zieba-Palus et al, 2008), analysis of whole organism in bacterial identification and classification (Barshick et al, 1999;Prasad et al, 2008) and analysis of vegetable material (Ibarra et al, 2007;Kim et al, 2003). The use of this technique in the analysis of herbal products has been very limited (Fang et al, 1990), but its potential is very promising.…”

Supervised semi-automated data analysis software for gas chromatography / differential mobility spectrometry (GC/DMS) metabolomics applications

“…Pyrolysis helps to provide biomarker liberation from bacteria/endospores (Cheung et al 2009; Eiceman et al 2006; Krebs et al 2006b; Prasad et al 2008), which can then be separated and detected by DMS. Gas chromatography allows for a modest pre-separation of a very complex mixture before additional DMS separation and detection (Aksenov et al 2014; Arasaradnam et al 2014a; Arasaradnam et al 2014b; Basanta et al 2010; Covington et al 2013; Rutolo et al 2014; Schivo et al 2013).…”

“…Principal component analysis (PCA) is an unsupervised method to determine if data will cluster according to groups based on orthogonal transformations of the data. It is very useful as an initial look at metabolomic data (Covington et al 2013; Prasad et al 2008), and can be used towards outlier detection. Genetic algorithms have been one way to build and validate whole-spectrum models of GC/DMS responses (Eiceman et al 2006; Krebs et al 2006b; Shnayderman et al 2005), which has the advantage of using an optimization method to determine biomarkers within data sets to achieve high classification rates to separate groups of data.…”