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Spectroscopic quantification of bacteria using Artificial Neural Networks

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Title Spectroscopic quantification of bacteria using Artificial Neural Networks
Not Available
 
Creator Mathala Juliet Gupta
Joseph Irudayaraj
Chitrita Debroy
 
Subject Foodborne pathogens, Enterococcus faecium, Salmonella Enteritidis, Bacillus cereus, Yersinia enterocolitica, Shigella boydii, rapid detection
 
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
 
Date 2018-11-09T05:48:03Z
2018-11-09T05:48:03Z
2004-11-01
 
Type Research Paper
 
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
 
Language English
 
Relation Not Available;
 
Publisher International Association for Food Protection