Spectroscopic quantification of bacteria using Artificial Neural Networks
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Title |
Spectroscopic quantification of bacteria using Artificial Neural Networks
Not Available |
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Creator |
Mathala Juliet Gupta
Joseph Irudayaraj Chitrita Debroy |
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Subject |
Foodborne pathogens, Enterococcus faecium, Salmonella Enteritidis, Bacillus cereus, Yersinia enterocolitica, Shigella boydii, rapid detection
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Description |
Not Available
Fourier transform–infrared spectroscopy, in conjunction with artificial neural networks, has been used for identification and classification of selected foodborne pathogens. Five bacterial species (Enterococcus faecium, Salmonella Enteritidis, Bacillus cereus, Yersinia enterocolitica, Shigella boydii) and five Escherichia coli strains (O103, O55, O121, O30, O26) suspended in phosphate-buffered saline were enumerated to provide seven different concentrations ranging from 109 to 103 CFU/ml. The trained artificial neural networks were then validated with an independent subset of samples and compared with the traditional plate count method. It was found that the concentration-based classification of the species was 100% correct and the strain-based classification was 90 to 100% accurate. Not Available |
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Date |
2018-11-09T05:48:03Z
2018-11-09T05:48:03Z 2004-11-01 |
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Type |
Research Paper
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Identifier |
Gupta M.J., Joseph Irudayaraj and Chitrita Debroy. 2004. Spectroscopic quantification of bacteria using Artificial Neural Networks. J Food Prot. 67(11):2550-54.
0362-028X http://krishi.icar.gov.in/jspui/handle/123456789/9850 |
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Language |
English
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Relation |
Not Available;
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Publisher |
International Association for Food Protection
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