Publication
Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network
| dc.contributor.author | Golpour, Iman | |
| dc.contributor.author | Kaveh, Mohammad | |
| dc.contributor.author | Amiri Chayjan, Reza | |
| dc.contributor.author | Guiné, Raquel | |
| dc.date.accessioned | 2020-12-04T10:07:04Z | |
| dc.date.available | 2020-12-04T10:07:04Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | A comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared–con- vective drying of white mulberry. The drying experiments were performed at different air temperatures (40°C, 55°C, and 70°C), air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared radiation power (500, 1000, and 1500 W). RSM focuses on the maximization of effective moisture diffusivity (D eff ) and minimi- zation of specific energy consumption (SEC) in the drying pro- cess. The optimized conditions were encountered for the air temperature of 70°C, the air velocity of 0.4 m/s, and the infrared power level of 1464.57 W. The optimum values of D eff and SEC were 1.77 × 10 −9 m 2 /s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the feed and cascade-forward back-Propagation neural systems with application of Levenberg-Marquardt train- ing algorithm and topologies of 3–20-20-1 and 3–10-10-1 were the best neural models to predict D eff and SEC, respectively. This finding suggests that the ANN as an intelligent method with better performance compared to the RSM can be used to pre- dict the drying parameters of the infrared-convective drying of white mulberry fruit. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1080/15538362.2020.1774474 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.19/6426 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.subject | White mulberry | pt_PT |
| dc.subject | effective moisture diffusivity | pt_PT |
| dc.subject | specific energy consumption | pt_PT |
| dc.subject | response surface methodology | pt_PT |
| dc.subject | artificial neural network | pt_PT |
| dc.title | Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | S1035 | pt_PT |
| oaire.citation.issue | sup2 | pt_PT |
| oaire.citation.startPage | S1015 | pt_PT |
| oaire.citation.title | International Journal of Fruit Science | pt_PT |
| oaire.citation.volume | 20 | pt_PT |
| person.familyName | de Pinho Ferreira Guiné | |
| person.givenName | Raquel | |
| person.identifier | https://scholar.google.pt/citations?user=abFDovIAAAAJ&hl=pt-PT | |
| person.identifier.ciencia-id | 8B13-5492-0F23 | |
| person.identifier.orcid | 0000-0003-0595-6805 | |
| person.identifier.scopus-author-id | 6603138390 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 59580952-77cc-4e4e-ae90-527a8b994f9f | |
| relation.isAuthorOfPublication.latestForDiscovery | 59580952-77cc-4e4e-ae90-527a8b994f9f |
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