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Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis.
Appl Microbiol Biotechnol. 2016 Nov; 100(21):9305-9320.AM

Abstract

The potential for near-infrared (NIR) hyperspectral imaging and multivariate data analysis to be used as a rapid non-destructive tool for detection and differentiation of bacteria was investigated. NIR hyperspectral images were collected of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Staphylococcus epidermidis grown on agar for 20 h at 37 °C. Principal component analysis (PCA) was applied to mean-centred data. Standard normal variate (SNV) correction and the Savitzky-Golay technique was applied (2nd derivative, 3rd-order polynomial; 25 point smoothing) to wavelengths in the range of 1103 to 2471 nm. Chemical differences between colonies which appeared similar in colour on growth media (B. cereus, E. coli and S. enteritidis.) were evident in the PCA score plots. It was possible to distinguish B. cereus from E. coli and S. enteritidis along PC1 (59 % sum of squares (SS)) and between E. coli and S. enteritidis in the direction of PC2 (6.85 % SS). S. epidermidis was separated from B. cereus and S. aureus along PC1 (37.5 % SS) and was attributed to variation in amino acid and carbohydrate content. Two clusters were evident in the PC1 vs. PC2 PCA score plot for the images of S. aureus and S. epidermidis, thus permitting distinction between species. Differentiation between genera (similarly coloured on growth media), Gram-positive and Gram-negative bacteria and pathogenic and non-pathogenic species was possible using NIR hyperspectral imaging. Partial least squares discriminant analysis (PLS-DA) models were used to confirm the PCA data. The best predictions were made for B. cereus and Staphylococcus species, where results ranged from 82.0 to 99.96 % correctly predicted pixels.

Authors+Show Affiliations

Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa.Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa.Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa.Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa. pauljw@sun.ac.za.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27624097

Citation

Kammies, Terri-Lee, et al. "Differentiation of Foodborne Bacteria Using NIR Hyperspectral Imaging and Multivariate Data Analysis." Applied Microbiology and Biotechnology, vol. 100, no. 21, 2016, pp. 9305-9320.
Kammies TL, Manley M, Gouws PA, et al. Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis. Appl Microbiol Biotechnol. 2016;100(21):9305-9320.
Kammies, T. L., Manley, M., Gouws, P. A., & Williams, P. J. (2016). Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis. Applied Microbiology and Biotechnology, 100(21), 9305-9320.
Kammies TL, et al. Differentiation of Foodborne Bacteria Using NIR Hyperspectral Imaging and Multivariate Data Analysis. Appl Microbiol Biotechnol. 2016;100(21):9305-9320. PubMed PMID: 27624097.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis. AU - Kammies,Terri-Lee, AU - Manley,Marena, AU - Gouws,Pieter A, AU - Williams,Paul J, Y1 - 2016/09/14/ PY - 2016/03/07/received PY - 2016/08/09/accepted PY - 2016/06/18/revised PY - 2016/9/15/pubmed PY - 2017/1/19/medline PY - 2016/9/15/entrez KW - Foodborne pathogens KW - Microbiology KW - Multivariate data analysis KW - NIR hyperspectral imaging KW - PCA KW - PLS-DA SP - 9305 EP - 9320 JF - Applied microbiology and biotechnology JO - Appl Microbiol Biotechnol VL - 100 IS - 21 N2 - The potential for near-infrared (NIR) hyperspectral imaging and multivariate data analysis to be used as a rapid non-destructive tool for detection and differentiation of bacteria was investigated. NIR hyperspectral images were collected of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Staphylococcus epidermidis grown on agar for 20 h at 37 °C. Principal component analysis (PCA) was applied to mean-centred data. Standard normal variate (SNV) correction and the Savitzky-Golay technique was applied (2nd derivative, 3rd-order polynomial; 25 point smoothing) to wavelengths in the range of 1103 to 2471 nm. Chemical differences between colonies which appeared similar in colour on growth media (B. cereus, E. coli and S. enteritidis.) were evident in the PCA score plots. It was possible to distinguish B. cereus from E. coli and S. enteritidis along PC1 (59 % sum of squares (SS)) and between E. coli and S. enteritidis in the direction of PC2 (6.85 % SS). S. epidermidis was separated from B. cereus and S. aureus along PC1 (37.5 % SS) and was attributed to variation in amino acid and carbohydrate content. Two clusters were evident in the PC1 vs. PC2 PCA score plot for the images of S. aureus and S. epidermidis, thus permitting distinction between species. Differentiation between genera (similarly coloured on growth media), Gram-positive and Gram-negative bacteria and pathogenic and non-pathogenic species was possible using NIR hyperspectral imaging. Partial least squares discriminant analysis (PLS-DA) models were used to confirm the PCA data. The best predictions were made for B. cereus and Staphylococcus species, where results ranged from 82.0 to 99.96 % correctly predicted pixels. SN - 1432-0614 UR - https://www.unboundmedicine.com/medline/citation/27624097/Differentiation_of_foodborne_bacteria_using_NIR_hyperspectral_imaging_and_multivariate_data_analysis_ L2 - https://dx.doi.org/10.1007/s00253-016-7801-4 DB - PRIME DP - Unbound Medicine ER -
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