CLASSIFICATION OF FOODBORNE PATHOGENS BY FOURIER TRANSFORM INFRARED SPECTROSCOPY AND PATTERN RECOGNITION TECHNIQUES (original) (raw)
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Fourier transform infrared (FT-IR) spectroscopy is a physico-chemical method based on measurement of vibration of a molecule excited by IR radiation at a specific wavelength range. FT-IR spectra of bacterial cells can be used to analyze their total composition, including proteins, fatty acids, carbohydrates, nucleic acids, and lipopolysacharides. FT-IR techniques coupled with different chemometrics analyses of the spectra offer a wide range of applications for food microbiology, including detection, differentiation, quantification, and taxonomic level classification of bacteria from culture broth or food matrices. FT-IR spectroscopy is a reliable, rapid, and economic technique which could be explored as a routine diagnostic tool for bacterial analysis by the food industry, diagnostic laboratories, and public health authorities. This chapter highlights the principles of FT-IR spectroscopic analysis of bacteria, the advantages and disadvantages of FT-IR applied to bacterial analysis, various sampling techniques, spectral manipulation, statistical analysis of spectra, and applications in pathogen detection.
Food microbiology, 2006
Fourier transform infrared spectroscopy (FT-IR) can discriminate Escherichia coli O157:H7 ATCC 35150 from other bacteria: E. coli ATCC 25522, Bacillus cereus ATCC 10876, and Listeria innocua ATCC 51742 inoculated in to apple juice. Spectra of bacterial suspensions (ca. 10 9 cfu/ml in 0.9% NaCl) on Anodisc (aluminum oxide) filters were tested. Unique FT-IR vibrational combination bands from mid-IR active components of bacterial cells are present in the ''fingerprint region'' at wavenumbers between 1500 and 800 cm À1. Principal component analysis (PCA) revealed clear segregations between different bacterial strains.
Journal of Food Protection, 2004
The use of Fourier transform-near infrared (FT-NIR) spectroscopy combined with multivariate pattern recognition techniques was evaluated to address the need for a fast and sensitive method for the detection of bacterial contamination in liquids. The complex cellular composition of bacteria produces FT-NIR vibrational transitions (overtone and combination bands), forming the basis for identification and subtyping. A database including strains of Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Bacillus cereus, and Bacillus thuringiensis was built, with special care taken to optimize sample preparation. The bacterial cells were treated with 70% (vol/vol) ethanol to enhance safe handling of pathogenic strains and then concentrated on an aluminum oxide membrane to obtain a thin bacterial film. This simple membrane filtration procedure generated reproducible FT-NIR spectra that allowed for the rapid discrimination among closely related strains. Principal component analysis and soft independent modeling of class analogy of transformed spectra in the region 5,100 to 4,400 cm Ϫ1 were able to discriminate between bacterial species. Spectroscopic analysis of apple juices inoculated with different strains of E. coli at approximately 10 5 CFU/ml showed that FT-NIR spectral features are consistent with bacterial contamination and soft independent modeling of class analogy correctly predicted the identity of the contaminant as strains of E. coli. FT-NIR in conjunction with multivariate techniques can be used for the rapid and accurate evaluation of potential bacterial contamination in liquids with minimal sample manipulation, and hence limited exposure of the laboratory worker to the agents.
Distinction between mixed genus bacteria using infrared spectroscopy and multivariate analysis
Vibrational Spectroscopy, 2019
Bacterial infections are significant causes of serious human health problems. Different bacterial pathogens might have similar symptoms, and it is important to detect the cause of the infection early in order to enable effective treatment. In many infections, a mixture of different bacteria might exist in the same tested sample. To treat such infections effectively, it is very important to identify the various types of infecting bacteria in the sample. In this pioneering study, we examined the potential of Fourier transform infrared (FTIR) microscopy for accurate identification and differentiation between different bacteria in experimentally mixed samples of bacteria in the genus level in time span of about 30 min. We have measured the FTIR spectra of bacteria in pure form as well as in various mixtures. Principal components analysis (PCA) was applied on the spectra of various classes, followed by linear discriminant analysis (LDA) as a linear classifier. Our results show that it is possible to differentiate between mixed categories of bacteria with high rates of success. The classification rate was higher when the mixed samples included Gram-positive and Gram-negative types. When the mixing range was comparable ([0.5,0.5] and [0.6,0.4]), a classification success rate > 95% was achieved using only the first 20 PCs.
Automated species and strain identification of bacteria in complex matrices using FTIR spectroscopy
SPIE Proceedings, 2008
Fourier Transform Infrared (FTIR) spectroscopy provides a highly selective and reproducible means for the chemicallybased discrimination of intact microbial cells which make the method valuable for large-scale screening of foods. The goals of the present study were to assess the effect of chemical interferents, such as food matrices, different sanitizing compounds and growth media, on the ability of the method to accurately identify and classify L. innocua, L. welshimeri, E. coli, S. cholerasuis, S. subterranea, E. sakazakii, and E. aerogenes. Moreover, the potential of FTIR spectroscopy for discrimination of L. innocua and L. welshimeri of different genotypes and the effect of growth phase on identification accuracy of L. innocua and L. welshimeri were tested. FTIR spectra were collected using two different sample presentation techniques-transmission and attenuated total reflection (ATR), and then analyzed using multivariate discriminant analysis based on the first derivative of the FTIR spectra with the unknown spectra assigned to the species group with the shortest Mahalanobis distance. The results of the study demonstrated 100% correct identification and differentiation of all bacterial strains used in this study in the presence of chemical interferents or food matrices, better than 99% identification rate in presence of media matrices, and 100% correct detection for specific bacteria in mixed flora species. Additionally, FTIR spectroscopy proved to be 100% accurate when differentiating between genotypes of L. innocua and L. welshimeri, with the classification accuracy unaffected by the growth stage. These results suggest that FTIR spectroscopy can be used as a valuable tool for identifying pathogenic bacteria in food and environmental samples.
Automated species and strain identification of bacteria in complex matrices using FTIR spectroscopy
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing IX, 2008
Fourier Transform Infrared (FTIR) spectroscopy provides a highly selective and reproducible means for the chemicallybased discrimination of intact microbial cells which make the method valuable for large-scale screening of foods. The goals of the present study were to assess the effect of chemical interferents, such as food matrices, different sanitizing compounds and growth media, on the ability of the method to accurately identify and classify L. innocua, L. welshimeri, E. coli, S. cholerasuis, S. subterranea, E. sakazakii, and E. aerogenes. Moreover, the potential of FTIR spectroscopy for discrimination of L. innocua and L. welshimeri of different genotypes and the effect of growth phase on identification accuracy of L. innocua and L. welshimeri were tested. FTIR spectra were collected using two different sample presentation techniques-transmission and attenuated total reflection (ATR), and then analyzed using multivariate discriminant analysis based on the first derivative of the FTIR spectra with the unknown spectra assigned to the species group with the shortest Mahalanobis distance. The results of the study demonstrated 100% correct identification and differentiation of all bacterial strains used in this study in the presence of chemical interferents or food matrices, better than 99% identification rate in presence of media matrices, and 100% correct detection for specific bacteria in mixed flora species. Additionally, FTIR spectroscopy proved to be 100% accurate when differentiating between genotypes of L. innocua and L. welshimeri, with the classification accuracy unaffected by the growth stage. These results suggest that FTIR spectroscopy can be used as a valuable tool for identifying pathogenic bacteria in food and environmental samples.
FTIR absorbance spectra of four foodborne pathogens suspended in four common food matrices at three different concentrations were used with artificial neural networks (ANNs) for identification and quantification. The classification accuracy of the ANNs was 93.4% for identification and 95.1% for quantification when validated using a subset of the data set. The accuracy of the ANNs when validated for identification of the pathogens studied at four different concentrations using an independent data set had an accuracy range from 60% to 100% and was strongly influenced by background noise. The pathogens could be identified irrespective of the food matrix in which they were suspended, although the classification accuracy was reduced at lower concentrations. More sophisticated background noise filtration techniques are needed to further improve the predictions.
Sensing and Instrumentation for Food Quality and Safety, 2010
Detection of beef contamination from harmful pathogens will be helpful in protecting the consumer safety and controlling the outbreaks. In this paper, the potential of Fourier transform infrared spectroscopy (FTIR) was investigated to discriminate the Salmonella contaminated packed beef. A suitable headspace sampling system was designed and used to collect the headspace volatiles from the packed meat to the FTIR
Journal of Agricultural and Food Chemistry, 2004
Fourier transform infrared spectroscopy (FT-IR, 4000-600 cm -1 ) was used to discriminate between intact and sonication-injured Listeria monocytogenes ATCC 19114 and to distinguish this strain from other selected Listeria strains (L. innocua ATCC 51742, L. innocua ATCC 33090, and L. monocytogenes ATCC 7644). FT-IR vibrational overtone and combination bands from mid-IR active components of intact and injured bacterial cells produced distinctive "fingerprints" at wavenumbers between 1500 and 800 cm -1 . Spectral data were analyzed by principal component analysis. Clear segregations of different intact and injured strains of Listeria were observed, suggesting that FT-IR can detect biochemical differences between intact and injured bacterial cells. This technique may provide a tool for the rapid assessment of cell viability and thereby the control of foodborne pathogens.
Journal of Agricultural and Food Chemistry, 2008
Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.