KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/37337
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dr. Mathala Juliet Gupta | en_US |
dc.contributor.author | Dr. Joseph M. Irudayaraj | en_US |
dc.contributor.author | C. Debroy | en_US |
dc.contributor.author | Z. Schmilovitch | en_US |
dc.contributor.author | A. Mizrach | en_US |
dc.date.accessioned | 2020-06-20T10:43:18Z | - |
dc.date.available | 2020-06-20T10:43:18Z | - |
dc.date.issued | 2005-07-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | 0001−2351 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/37337 | - |
dc.description | Not Available | en_US |
dc.description.abstract | FTIR Absrobance spectra in conjunction with artificaial Neural Networks(ANNs) were used to differentiate selected microorganisms at the generic and serogroup levels. The ANN consisted of three layers with 595 input nodes, 50 nodes at the hidden layer and 5 output nides(one for each microorganism or strain). The replications of each experiment were conducted, and 70% of the data was used for training and 30% for validation of the network. Result indicated that differentiation could be achieved at an accuracy of 80% to 100% at thegeneric level nd 90% to 100% at the serogroup level at 10 * * 3 CFU/ml concentration. | en_US |
dc.description.sponsorship | Not Available | 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 | ANN, Differentiation, Food Pathogens, FTIR Spectroscopy | en_US |
dc.title | Differentiation of Food Pathogens using FTIR and Artificaial Neural Networks | 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 ASAE | en_US |
dc.publication.volumeno | 48(5) | en_US |
dc.publication.pagenumber | 1889-1892 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | Not Available | 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 | Pennsylvania State University, PA, US | en_US |
dc.publication.authorAffiliation | Institute of Agricultural Engineering, ARO, 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 | |
---|---|---|---|---|
asabe technical. note.pdf | 3.76 MB | Adobe PDF | View/Open |
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.