Paper
21 February 2002 Rapid detection of pathogenic bacteria by volatile organic compound (VOC) analysis
Andre G. Senecal, Joshua Magnone, Walter Yeomans, Edmund M. Powers
Author Affiliations +
Abstract
Developments in rapid detection technologies have made countless improvements over the years. However, because of the limited sample that these technologies can process in a single run, the chance of capturing and identifying a small amount of pathogens is difficult. The problem is further magnified by the natural random distribution of pathogens in foods. Methods to simplify pathogenic detection through the identification of bacteria specific VOC were studied. E. coli O157:H7 and Salmonella typhimurium were grown on selected agar medium to model protein, and carbohydrate based foods. Pathogenic and common spoilage bacteria (Pseudomonas and Morexella) were screened for unique VOC production. Bacteria were grown on agar slants in closed vials. Headspace sampling was performed at intervals up to 24 hours using Solid Phase Micro-Extraction (SPME) techniques followed by GC/MS analysis. Development of unique volatiles was followed to establish sensitivity of detection. E. coli produced VOC not found in either Trypticase Soy Yeast (TSY) agar blanks or spoilage organism samples were - indole, 1-decanol, and 2-nonanone. Salmonella specific VOC grown on TSY were 3-methyl-1-butanol, dimethyl sulfide, 2-undecanol, 2-pentadecanol and 1-octanol. Trials on potato dextrose agar (PDA) slants indicated VOC specific for E. coli and Salmonella when compared to PDA blanks and Pseudomonas samples. However, these VOC peaks were similar for both pathogens. Morexella did not grow on PDA slants. Work will continue with model growth mediums at various temperatures, and mixed flora inoculums. As well as, VOC production based on the dynamics of bacterial growth.
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Andre G. Senecal, Joshua Magnone, Walter Yeomans, and Edmund M. Powers "Rapid detection of pathogenic bacteria by volatile organic compound (VOC) analysis", Proc. SPIE 4575, Chemical and Biological Early Warning Monitoring for Water, Food, and Ground, (21 February 2002); https://doi.org/10.1117/12.456915
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Cited by 16 scholarly publications.
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KEYWORDS
Pathogens

Personal digital assistants

Organisms

Bacteria

Microorganisms

Biosensors

Proteins

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