Rapid differentiation between E. coli and Salmonella Typhimurium using metal oxide sensors integrated with pattern recognition

Abstract A rapid method to differentiate between E coli and Salmonella Typhimurium was developed. E. coli and S. Typhimurium were separately grown in super broth and incubated at 37 °C. Super broth without inoculation of E. coli or S. Typhimurium was used as control. Numbers of E. coli and S. Typhimurium were followed using a colony counting method. Identification of the volatile metabolites produced by E. coli and S. Typhimurium was determined using solid-phase microextraction coupled with gas chromatography/mass spectrometry. An electronic nose with 12 non-specific metal oxide sensors was used to monitor the volatile profiles produced by E. coli and S. Typhimurium. Principal component analysis (PCA) and back-propagation neural network (BPNN) were used as pattern recognition tools. PCA was used for data exploration and dimensional reduction. PCA could visualize class separation between sample subgroups. The BPNN was shown to be capable of predicting the number of E. coli and S. Typhimurium. Good prediction was possible as measured by a regression coefficient ( R 2  = 0.96) between true and predicted data. Using metal oxide sensors and pattern recognition techniques, it was possible to discriminate between samples containing E. coli from those containing S. Typhimurium.

[1]  A. H. Gómez,et al.  Evaluation of tomato maturity by electronic nose , 2006 .

[2]  Serge Kokot,et al.  Data Interpretation by some Common Chemometrics Methods , 1998 .

[3]  Naresh Magan,et al.  Application of electronic nose technology for the detection of fungal contamination in library paper , 2004 .

[4]  Olivier Lazcka,et al.  Pathogen detection: a perspective of traditional methods and biosensors. , 2007, Biosensors & bioelectronics.

[5]  Miriam M. Ngundi,et al.  Rapid detection of foodborne contaminants using an Array Biosensor , 2006 .

[6]  Ubonrat Siripatrawan,et al.  Self-organizing algorithm for classification of packaged fresh vegetable potentially contaminated with foodborne pathogens , 2008 .

[7]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[8]  P. Cieslak,et al.  Alfalfa Seed Decontamination in Salmonella Outbreak , 2003, Emerging infectious diseases.

[9]  J. Goschnick,et al.  Multicomponent quantification with a novel method applied to gradient gas sensor microarray signal patterns , 2007 .

[10]  T. Roberts,et al.  Assessment of Risks Associated with Foodborne Pathogens: An Overview of a Council for Agricultural Science and Technology Report. , 1996, Journal of food protection.

[11]  Duk-Dong Lee,et al.  Recognition of volatile organic compounds using SnO2 sensor array and pattern recognition analysis , 2001 .

[12]  Tetsuo Aishima,et al.  Correlating sensory attributes to gas chromatography-mass spectrometry profiles and e-nose responses using partial least squares regression analysis. , 2004, Journal of chromatography. A.

[13]  Manuela O’Connell,et al.  A practical approach for fish freshness determinations using a portable electronic nose , 2001 .

[15]  Pascale Chalier,et al.  Coupling gas chromatography and electronic nose for dehydration and desalcoholization of alcoholized beverages: Application to off-flavour detection in wine , 2005 .

[16]  S. Ampuero,et al.  Screening of aroma-producing lactic acid bacteria with an electronic nose , 2004 .

[17]  Ubonrat Siripatrawan,et al.  Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable. , 2007, Analytica chimica acta.

[18]  José Santos,et al.  Application of artificial neural networks to calculate the partial gas concentrations in a mixture , 2001 .

[19]  Hans Sundgren,et al.  Electronic nose for odor classification of grains , 1996 .

[20]  E. Oja Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[21]  M. Martí,et al.  Electronic noses in the quality control of alcoholic beverages , 2005 .

[22]  Bryan A. Chin,et al.  Detection of Salmonella typhimurium in fat free milk using a phage immobilized magnetoelastic sensor , 2007 .