Identification Of Foodborne Bacteria Research Articles

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy.

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Rapid identification of a subset of foodborne bacteria in live-cell assays.

The detection and identification of microbial pathogens in meat and fresh produce play an essential role in food safety for reducing foodborne illnesses every year. A new approach based on targeting a specific sequence of the 16S rRNA region for each bacterium is proposed and validated. The probe complex consists of a C60, a conjugated RNA detector which targets a specific 16S rRNA sequence, and a complementary fluorescent reporter. The RNA detectors were designed by integrating NIH nucleotideand Vienna RNA Webservice databases, and their specificities were validated by the RDP database. Probe complexes were synthesized for identifying E. coli K12, E. coli O157: H7, S. enterica, Y. enterocolitica, C. perfringens, and L. monocytogenes. First, under controlled conditions of known bacterial mixtures, the efficiency and crosstalk for identifying the foodborne bacteria were quantified to be above 94% and below 5%, respectively. Second, experiments were designed by inoculating meat products by known numbers of bacteria and measuring the limit of detection. In one experiment, 225g of autoclaved ground chicken was inoculated with 9 E. coli O157:H7, where 6.8 ± 1.2 bacteria with 95% confidence interval were recovered. Third, by positionally printing probe complexes in microwells, specific microorganisms were identified with only one fluorophore. The proposed protocol is a cell-based system, can identify live bacteria in 15min, requires no amplification, and has the potential to open new surveillance opportunities.Key points• The identification of foodborne bacteria is enabled in live-cell assays.• The limit of detection for 100g of fresh chicken breast inoculated with 4 bacteria is 2.7 ± 1.4 with 95% confidence interval.• The identification of five bacteria in a coded microwell chip is enabled with only one fluorophore.

Black phosphorus-Au filter paper-based three-dimensional SERS substrate for rapid detection of foodborne bacteria

Rapid and accurate detection of foodborne bacteria is believed to be of vital importance in assuring food safety. In this work, we explored a novel label-free three-dimensional surface-enhanced Raman scattering (3D-SERS) substrate based on black phosphorus-Au (BP-Au) filter paper for the detection and discrimination of three common foodborne bacteria including Staphylococcus aureus, Listeria monocytogenes and Escherichia coli without complicated sample processing. It was found that the BP-Au filter paper-based 3D-SERS substrate could offer larger surface area as well as more “hotspots” than 2D-SERS substrate for sample detection. Moreover, our results showed that this 3D substrate could efficiently enhance Raman signals from model molecules and exhibit satisfactory SERS activity allowing for specific recognition and discrimination of three types of target bacteria at a concentration of 107 CFU/mL. Subsequently, SERS spectral data from three bacteria were successfully distinguished by principle component analysis (PCA) and linear discriminant analysis (LDA). These results demonstrated that the novel 3D SERS substrate with facile, low-cost, green fabrication process as well as high sensitivity could serve as an excellent alternative to conventional substrates for rapid label-free detection and identification of foodborne bacteria with the aid of statistical discriminant analysis PCA-LDA in food safety application.

Surface-enhanced Raman spectroscopy introduced into the International Standard Organization (ISO) regulations as an alternative method for detection and identification of pathogens in the food industry

We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes, and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290–1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical classification of studied bacteria and to identify the spectrum of an unknown sample. Calculated first and second principal components (PC-1, PC-2) account for 96, 98, and 90% of total variance in the spectra and enable one to identify the Salmonella spp., L. monocytogenes, and Cronobacter spp., respectively. Moreover, the presented study demonstrates the excellent possibility for simultaneous detection of analyzed food-borne bacteria in one sample test (98% of PC-1 and PC-2) with a goal of splitting the data set into three separated clusters corresponding to the three studied bacteria species. The studies described in this paper suggest that SERS represents an alternative to standard microorganism diagnostic procedures.Graphical New approach of the SERS strategy for detection and identification of food-borne bacteria, namely S. enterica, L. monocytogenes, and C. sakazakii in selected food matrices

Identification of foodborne bacteria by infrared spectroscopy using cellular fatty acid methyl esters

Identification of bacterial species by profiling fatty acid methyl esters (FAMEs) has commonly been carried out by using a 20-min capillary gas chromatographic procedure followed by library matching of FAME profiles using commercial MIDI databases and proprietary pattern recognition software. Fast GC (5 min) FAME procedures and mass spectrometric methodologies that require no lipid separation have also been reported. In this study, bacterial identification based on the rapid (2 min) infrared measurement of FAME mixtures was demonstrated. The microorganisms investigated included Gram positive bacteria Staphylococcus aureus, Listeria monocytogenes, Bacillus anthracis, and Bacillus cereus, and Gram negative bacteria from the family Enterobacteriacae: Yersinia enterocolitica, Salmonella typhimurium, Shigella sonnei, and Escherichia coli (four strains of E. coli), and non-Enterobacteriacae: Vibrio cholerae, Vibrio vulnificus, and Vibrio parahemolyticus. Foodborne bacterial mixtures of FAMEs were measured by using an attenuated total reflection (ATR)–Fourier transform infrared (FTIR) spectroscopic procedure and discriminated by multivariate analysis. Results showed that the Enterobacteriacae could be discriminated from the vibrios. The identification was at the level of species (for the Bacillus and Vibrio genera) or strains (for the E. coli species). A series of bacterial FAME test samples were prepared and analyzed for accuracy of identification, and all were correctly identified. Our results suggest that this infrared strategy could be used to identify foodborne pathogens.

Gas Chromatographic Analysis of Cellular Fatty Acids in the Identification of Foodborne Bacteria

The cellular fatty acid composition of about 160 strains of Bacillus, lactic acid bacteria, Enterobacteriaceae, and Staphylococcus was analyzed in order to identify foodborne bacteria. In a first step, the reproducibility of the procedure, from the bacterial growth to the extraction and analysis conditions, was evaluated. The stability of fatty acid composition under controlled conditions was confirmed through high similarity levels (So ≥ 94). Secondly, intraspecies similarity was estimated. The existence of low values (So ≤ 79) was demonstrated within each of the four groups of strains investigated, which stressed the importance of analyzing a high number of reference strains in each species. The intraspecies similarity was maximal under optimal growth conditions. Finally, interspecies similarity was compared to intraspecies similarity. The best results of discrimination between species was obtained for the heterogeneous genus Bacillus, for which the 12 species studied could be separated into 6 clusters at a similarity level of 79. The association of 27 species of lactic acid bacteria into 8 clusters should be useful to confirm results of other characterization methods. Only 3 clusters were formed for the 16 Enterobacteriaceae species studied. The genus Salmonella could not be discriminated. The 10 Staphylococcus species were separated into 5 clusters. Coagulase-positive and -negative strains were not differentiated. Since the fatty acid composition did not usually appear specific to single species, characterization of foodborne bacteria by the analysis of their cellular fatty acids should be used to complement other taxonomic methods.