Detection ofsalmonella typhimurium by hand-held ion mobility spectrometer: A quantitative assessment of response characteristics

Smith, Geoffrey B.; Eiceman, Gary A.; Walsh, M.; Critz, Sue A.; Andazola, Ella; Ortega, E. O.; Cadena, F.

doi:10.1002/(sici)1520-6521(1997)1:4<213::aid-fact4>3.0.co;2-w

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…IMS is well-known for applications like the detection of chemical warfare agents, explosives, and drugs (Eiceman and Karpas 2005; Baumbach and Eiceman 1999; Roehl 1991). New applications in recent years are microbial VOC detections in headspace of microbial cultures (Ruzsanyi et al 2002; Snyder et al 1991; Smith et al 1997; Perl et al 2011) or even over clinical samples directly (Chaim et al 2003). Also differential mobility spectrometry, a closely related technology, has been used for that purpose already (Shnayderman et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria

Jünger

Vautz

Kuhns

et al. 2012

Appl Microbiol Biotechnol

136

128

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Presently, 2 to 4 days elapse between sampling at infection suspicion and result of microbial diagnostics. This delay for the identification of pathogens causes quite often a late and/or inappropriate initiation of therapy for patients suffering from infections. Bad outcome and high hospitalization costs are the consequences of these currently existing limited pathogen identification possibilities. For this reason, we aimed to apply the innovative method multi-capillary column–ion mobility spectrometry (MCC-IMS) for a fast identification of human pathogenic bacteria by determination of their characteristic volatile metabolomes. We determined volatile organic compound (VOC) patterns in headspace of 15 human pathogenic bacteria, which were grown for 24 h on Columbia blood agar plates. Besides MCC-IMS determination, we also used thermal desorption–gas chromatography–mass spectrometry measurements to confirm and evaluate obtained MCC-IMS data and if possible to assign volatile compounds to unknown MCC-IMS signals. Up to 21 specific signals have been determined by MCC-IMS for Proteus mirabilis possessing the most VOCs of all investigated strains. Of particular importance is the result that all investigated strains showed different VOC patterns by MCC-IMS using positive and negative ion mode for every single strain. Thus, the discrimination of investigated bacteria is possible by detection of their volatile organic compounds in the chosen experimental setup with the fast and cost-effective method MCC-IMS. In a hospital routine, this method could enable the identification of pathogens already after 24 h with the consequence that a specific therapy could be initiated significantly earlier.

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Section: Introductionmentioning

confidence: 99%

Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria

Jünger

Vautz

Kuhns

et al. 2012

Appl Microbiol Biotechnol

136

128

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“…First experiences have been made in the detection of microbial VOCs by IMS (Snyder et al 1991; Smith et al 1997; Chaim et al 2003) and the related technology of differential mobility spectrometry (Shnayderman et al 2005). Coupling IMS with a multicapillary column for pre-separation facilitates the analysis of complex and humid gas samples (Ruzsanyi et al 2005), such as human breath.…”

Section: Introductionmentioning

confidence: 99%

Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS)

Kunze

Göpel

Kuhns

et al. 2013

Appl Microbiol Biotechnol

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Headspace analyses over microbial cultures using multi-capillary column-ion mobility spectrometry (MCC-IMS) could lead to a faster, safe and cost-effective method for the identification of pathogens. Recent studies have shown that MCC-IMS allows identification of bacteria and fungi, but no information is available from when on during their growth a differentiation between bacteria is possible. Therefore, we analysed the headspace over human pathogenic reference strains of Escherichia coli and Pseudomonas aeruginosa at four time points during their growth in a complex fluid medium. In order to validate our findings and to answer the question if the results of one bacterial strain can be transferred to other strains of the same species, we also analysed the headspace over cultures from isolates of random clinical origin. We detected 19 different volatile organic compounds (VOCs) that appeared or changed their signal intensity during bacterial growth. These included six VOCs exclusively changing over E. coli cultures and seven exclusively changing over P. aeruginosa cultures. Most changes occurred in the late logarithmic or static growth phases. We did not find differences in timing or trends in signal intensity between VOC patterns of different strains of one species. Our results show that differentiation of human pathogenic bacteria by headspace analyses using MCC-IMS technology is best possible during the late phases of bacterial growth. Our findings also show that VOC patterns of a bacterial strain can be transferred to other strains of the same species.

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“…Reliable quantitative response from GC-DMS analyses is necessary for chemometric processing of measurements and APCI sources are known to exhibit limited linear ranges and source saturation [31]. Results from measurements of standards are shown in Figure 4 and The precision of quantitative response is shown in Table 2 with peak areas, standard deviation of peak area, and percent relative standard deviation (% RSD) of peak area are listed for each correctly predicting the test set's feedstocks fostered some confidence in the PCA and HCA models.…”

Section: Quantitative Performancementioning

confidence: 99%

Classification of biodiesel and fuel blends using gas chromatography – differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering

Pasupuleti

Eiceman

Pierce

2016

Talanta

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Fatty acid alkyl esters (FAAEs) were determined at 10-100mg/L in biodiesel and blends with petrodiesel without sample pre-treatment using gas chromatography with a tandem differential mobility detector. Selectivity was provided through chromatographic separations and atmospheric pressure chemical ionization reactions in the detector with mobility characterization of gas ions. Limits of detection were ~0.5ng with an average of 2.98% RSD for peak area precision, ≤1.3% RSD for retention time precision, and ≤9.2% RSD for compensation voltage precision. Biodiesel blends were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Unsupervised cluster analysis captured 52.72% of variance in a single PC while supervised analysis captured 71.64% of variance using Fisher ratio feature selection. Test set predictions showed successful clustering according to source or feedstock when regressed onto the training set model. Detection of the regulated substance methyl linolenate (C18:3 me) was achieved in 6-10s with a 1m long capillary column using dual ion filtering in the tandem differential mobility detector.

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Detection ofsalmonella typhimurium by hand-held ion mobility spectrometer: A quantitative assessment of response characteristics

Cited by 15 publications

References 14 publications

Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria

Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria

Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS)

Classification of biodiesel and fuel blends using gas chromatography – differential mobility spectrometry with cluster analysis and isolation of C18:3 me by dual ion filtering

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