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Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer
Drying characteristics of stevia leaves were investigated in an infrared (IR)‐assisted continuous‐flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802544/ https://www.ncbi.nlm.nih.gov/pubmed/33473314 http://dx.doi.org/10.1002/fsn3.2022 |
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author | Bakhshipour, Adel Zareiforoush, Hemad Bagheri, Iraj |
author_facet | Bakhshipour, Adel Zareiforoush, Hemad Bagheri, Iraj |
author_sort | Bakhshipour, Adel |
collection | PubMed |
description | Drying characteristics of stevia leaves were investigated in an infrared (IR)‐assisted continuous‐flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro‐Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R(2)), root mean squared error (RMSE), and chi‐squared error (χ(2)) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best‐fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous‐flow IR‐assisted hybrid solar dryer. |
format | Online Article Text |
id | pubmed-7802544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78025442021-01-19 Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer Bakhshipour, Adel Zareiforoush, Hemad Bagheri, Iraj Food Sci Nutr Original Research Drying characteristics of stevia leaves were investigated in an infrared (IR)‐assisted continuous‐flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro‐Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R(2)), root mean squared error (RMSE), and chi‐squared error (χ(2)) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best‐fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous‐flow IR‐assisted hybrid solar dryer. John Wiley and Sons Inc. 2020-11-12 /pmc/articles/PMC7802544/ /pubmed/33473314 http://dx.doi.org/10.1002/fsn3.2022 Text en © 2020 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Bakhshipour, Adel Zareiforoush, Hemad Bagheri, Iraj Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title | Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title_full | Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title_fullStr | Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title_full_unstemmed | Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title_short | Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
title_sort | mathematical and intelligent modeling of stevia (stevia rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802544/ https://www.ncbi.nlm.nih.gov/pubmed/33473314 http://dx.doi.org/10.1002/fsn3.2022 |
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