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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/37330
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mathala Juliet Gupta | en_US |
dc.contributor.author | Joseph Irudayaraj | en_US |
dc.contributor.author | Z. Schmilovitch | en_US |
dc.contributor.author | A. Mizrach | en_US |
dc.date.accessioned | 2020-06-20T10:13:28Z | - |
dc.date.available | 2020-06-20T10:13:28Z | - |
dc.date.issued | 2006-05-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | ISSN 0001−2351 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/37330 | - |
dc.description | Not Available | en_US |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | United States-Israel Binational Agricultural Research and Development Fund (Grant No. US-3296-02) | en_US |
dc.language.iso | English | en_US |
dc.publisher | American Society of Agricultural and Biological Engineers | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | ANNs, Differentiation, Food matrices, Food pathogens, FTIR spectroscopy, Quantification | en_US |
dc.title | IDENTIFICATION AND QUANTIFICATION OF FOODBORNE PATHOGENS IN DIFFERENT FOOD MATRICES USING FTIR SPECTROSCOPY AND ARTIFICIAL NEURAL NETWORK | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Transactions of the ASABE | en_US |
dc.publication.volumeno | 49(4) | en_US |
dc.publication.pagenumber | 1249−1255 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | DOI: 10.13031/issn.2151-0032 | en_US |
dc.publication.authorAffiliation | ICAR::Central Coastal Agricultural Research Institute | en_US |
dc.publication.authorAffiliation | Purdue University, Indiana, US | en_US |
dc.publication.authorAffiliation | The Volcani Center, Bet Dagan, Israel | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
Appears in Collections: | NRM-CCARI-Publication |
Files in This Item:
File | Description | Size | Format | |
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ASABE paper.pdf | 361 kB | Adobe PDF | View/Open |
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