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/123456789/36997
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | A.K. Sharma | en_US |
dc.contributor.author | N.R. Panjagari | en_US |
dc.contributor.author | A.K. Singh | en_US |
dc.date.accessioned | 2020-06-11T01:32:02Z | - |
dc.date.available | 2020-06-11T01:32:02Z | - |
dc.date.issued | 2018-07-07 | - |
dc.identifier.citation | Sharma A.K., Panjagari N.R., Singh A.K. (2018) Intelligent Modelling of Moisture Sorption Isotherms in Milk Protein-Rich Extruded Snacks Prepared from Composite Flour. In: Sharma R., Mantri A., Dua S. (eds) Computing, Analytics and Networks. ICAN 2017. Communications in Computer and Information Science, vol 805. Springer, Singapore | en_US |
dc.identifier.isbn | 978-981-13-0754-6 (Print) | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/36997 | - |
dc.description | Keynote address (through invitation) presented at International Conference International Conference on Computing, Analytics and Networks: ICAN 2017. | en_US |
dc.description.abstract | In this paper, connectionist models have been investigated empirically to predict adsorption isotherms of milk protein-rich extruded snacks prepared from composite flour, at different temperatures (i.e., 28, 37 and 45 °C) and water activities (i.e., in the range: 0.112–0.971). These models were based upon error back propagation learning algorithm supplemented with Bayesian regularization optimization mechanism as well as with various combinations/settings of network parameters. In all simulation experiments, the connectionist models with single hidden layer were found to fit the best to the adsorption isotherms data. The best configuration of the connectionist models comprised 10 neurons in the hidden layer with tangent-sigmoid transfer function; which attained accuracy in the range of 0.467–0.958 root mean square percent error (%RMS). Also, several conventional mathematical sorption models including two-parameter models, viz., Lewicki-I, Mizrahi and Modified BET; and three- and four-parameter models, i.e., Ferro-Fontan, GAB, Lewicki-II, Modified GAB, Modified Mizrahi and Peleg were developed for the purpose. The Ferro-Fontan and Peleg were the best similar models among the conventional sorption models, with %RMS lying in the ranges: 1.63–1.89 and 1.41–3.33, respectively, for the same temperatures and water activities range. Evidently, the connectionist sorption models developed in this study were found to be superior over conventional sorption models, to efficiently and intelligently predict adsorption isotherms of milk protein-rich extruded snacks prepared from composite flour | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Communications in Computer and Information Science (Springer book series), Springer Nature Switzerland AG. | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Adsorption isotherms | en_US |
dc.subject | Connectionist models | en_US |
dc.subject | Empirical sorption models | en_US |
dc.subject | Extruded snacks | en_US |
dc.subject | Predictive analytics | en_US |
dc.title | Intelligent modelling of moisture sorption isotherms in milk protein-rich extruded snacks prepared from composite flour | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Book chapter | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Communications in Computer and Information Science (Springer book series) | en_US |
dc.publication.volumeno | 805 | en_US |
dc.publication.pagenumber | 124-137 | en_US |
dc.publication.divisionUnit | Dairy Economics, Statistics and Management Division; and Dairy Technology Division | en_US |
dc.publication.sourceUrl | https://doi.org/10.1007/978-981-13-0755-3_10 | en_US |
dc.publication.sourceUrl | https://link.springer.com/chapter/10.1007%2F978-981-13-0755-3_10 | en_US |
dc.publication.authorAffiliation | ICAR::National Dairy Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
Appears in Collections: | AS-NDRI-Publication |
Files in This Item:
There are no files associated with this item.
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.