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Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) wer...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080558/ https://www.ncbi.nlm.nih.gov/pubmed/33911111 http://dx.doi.org/10.1038/s41598-021-88270-z |
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author | Rasooli Sharabiani, Vali Kaveh, Mohammad Abdi, Roozbeh Szymanek, Mariusz Tanaś, Wojciech |
author_facet | Rasooli Sharabiani, Vali Kaveh, Mohammad Abdi, Roozbeh Szymanek, Mariusz Tanaś, Wojciech |
author_sort | Rasooli Sharabiani, Vali |
collection | PubMed |
description | Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D(eff)) of moisture in CD drying (1.95 × 10(−7)–4.09 × 10(−7) m(2)/s) was found to be lower than that observed in MD (2.94 × 10(−7)–8.21 × 10(−7) m(2)/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R(2) values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively. |
format | Online Article Text |
id | pubmed-8080558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80805582021-04-28 Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling Rasooli Sharabiani, Vali Kaveh, Mohammad Abdi, Roozbeh Szymanek, Mariusz Tanaś, Wojciech Sci Rep Article Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D(eff)) of moisture in CD drying (1.95 × 10(−7)–4.09 × 10(−7) m(2)/s) was found to be lower than that observed in MD (2.94 × 10(−7)–8.21 × 10(−7) m(2)/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R(2) values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively. Nature Publishing Group UK 2021-04-28 /pmc/articles/PMC8080558/ /pubmed/33911111 http://dx.doi.org/10.1038/s41598-021-88270-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rasooli Sharabiani, Vali Kaveh, Mohammad Abdi, Roozbeh Szymanek, Mariusz Tanaś, Wojciech Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title | Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title_full | Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title_fullStr | Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title_full_unstemmed | Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title_short | Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
title_sort | estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080558/ https://www.ncbi.nlm.nih.gov/pubmed/33911111 http://dx.doi.org/10.1038/s41598-021-88270-z |
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