Publication
Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design
| dc.contributor.author | Farzaneh, Vahid | |
| dc.contributor.author | Ghodsvali, Alireza | |
| dc.contributor.author | Bakhshabadi, Hamid | |
| dc.contributor.author | Ganje, Mohammad | |
| dc.contributor.author | Dolatabadi, Zahra | |
| dc.contributor.author | Carvalho, Isabel S. | |
| dc.date.accessioned | 2019-11-20T15:07:42Z | |
| dc.date.available | 2019-11-20T15:07:42Z | |
| dc.date.issued | 2017-04 | |
| dc.description.abstract | The current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3-31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds. Practical ApplicationToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time.(c) 2016 Wiley Periodicals, Inc | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | |
| dc.identifier.doi | 10.1111/jfpe.12366 | |
| dc.identifier.issn | 0145-8876 | |
| dc.identifier.issn | 1745-4530 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/13171 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Wiley | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | L. Seeds | |
| dc.subject | Food | |
| dc.subject | Extraction | |
| dc.subject | Industry | |
| dc.subject | System | |
| dc.subject | Plant | |
| dc.title | Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design | |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 2 | |
| oaire.citation.startPage | e12366 | |
| oaire.citation.title | Journal of Food Process Engineering | |
| oaire.citation.volume | 40 | |
| person.familyName | Farzaneh | |
| person.familyName | Saraiva de Carvalho | |
| person.givenName | Vahid | |
| person.givenName | Isabel Maria Marques | |
| person.identifier.ciencia-id | 1C1E-A777-D6AC | |
| person.identifier.ciencia-id | 591A-3777-B036 | |
| person.identifier.orcid | 0000-0002-0536-9291 | |
| person.identifier.orcid | 0000-0001-8057-3404 | |
| person.identifier.scopus-author-id | 35995183800 | |
| rcaap.rights | restrictedAccess | |
| rcaap.type | article | |
| relation.isAuthorOfPublication | 61a70e8a-ec7a-4071-8365-da3cab860afd | |
| relation.isAuthorOfPublication | af26e659-578b-4e5f-a649-384cd5e4f8e3 | |
| relation.isAuthorOfPublication.latestForDiscovery | 61a70e8a-ec7a-4071-8365-da3cab860afd |
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