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Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks

The effects of temperature, air velocity, and infrared radiation distances on the drying characteristics and quality of apple slices were investigated using infrared-assisted-hot air drying (IRAHAD). Drying temperature and air velocity had remarkable effects on the drying kinetics, color, total phen...

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Detalles Bibliográficos
Autores principales: Huang, Xiao, Li, Yongbin, Zhou, Xiang, Wang, Jun, Zhang, Qian, Yang, Xuhai, Zhu, Lichun, Geng, Zhihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658811/
https://www.ncbi.nlm.nih.gov/pubmed/36360099
http://dx.doi.org/10.3390/foods11213486
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author Huang, Xiao
Li, Yongbin
Zhou, Xiang
Wang, Jun
Zhang, Qian
Yang, Xuhai
Zhu, Lichun
Geng, Zhihua
author_facet Huang, Xiao
Li, Yongbin
Zhou, Xiang
Wang, Jun
Zhang, Qian
Yang, Xuhai
Zhu, Lichun
Geng, Zhihua
author_sort Huang, Xiao
collection PubMed
description The effects of temperature, air velocity, and infrared radiation distances on the drying characteristics and quality of apple slices were investigated using infrared-assisted-hot air drying (IRAHAD). Drying temperature and air velocity had remarkable effects on the drying kinetics, color, total phenol content, total flavonoid content, and vitamin C content (VCC) of apple slices. Infrared radiation distance demonstrated similar results, other than for VCC and color. The shortest drying time was obtained at 70 °C, air velocity of 3 m/s and infrared radiation distance of 10 cm. A deep neural network (DNN) was developed, based on 4526 groups of apple slice drying data, and was applied to predict changes in moisture ratio (MR) and dry basis moisture content (DBMC) of apple slices during drying. DNN predicted that the coefficient of determination (R(2)) was 0.9975 and 1.0000, and the mean absolute error (MAE) was 0.001100 and 0.000127, for MR and DBMC, respectively. Furthermore, DNN obtained the highest R(2) and lowest MAE values when compared with multilayer perceptron (MLP) and support vector regression (SVR). Therefore, DNN can provide new ideas for the rapid detection of apple moisture and guide apple processing in order to improve quality and intelligent control in the drying process.
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spelling pubmed-96588112022-11-15 Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks Huang, Xiao Li, Yongbin Zhou, Xiang Wang, Jun Zhang, Qian Yang, Xuhai Zhu, Lichun Geng, Zhihua Foods Article The effects of temperature, air velocity, and infrared radiation distances on the drying characteristics and quality of apple slices were investigated using infrared-assisted-hot air drying (IRAHAD). Drying temperature and air velocity had remarkable effects on the drying kinetics, color, total phenol content, total flavonoid content, and vitamin C content (VCC) of apple slices. Infrared radiation distance demonstrated similar results, other than for VCC and color. The shortest drying time was obtained at 70 °C, air velocity of 3 m/s and infrared radiation distance of 10 cm. A deep neural network (DNN) was developed, based on 4526 groups of apple slice drying data, and was applied to predict changes in moisture ratio (MR) and dry basis moisture content (DBMC) of apple slices during drying. DNN predicted that the coefficient of determination (R(2)) was 0.9975 and 1.0000, and the mean absolute error (MAE) was 0.001100 and 0.000127, for MR and DBMC, respectively. Furthermore, DNN obtained the highest R(2) and lowest MAE values when compared with multilayer perceptron (MLP) and support vector regression (SVR). Therefore, DNN can provide new ideas for the rapid detection of apple moisture and guide apple processing in order to improve quality and intelligent control in the drying process. MDPI 2022-11-02 /pmc/articles/PMC9658811/ /pubmed/36360099 http://dx.doi.org/10.3390/foods11213486 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Xiao
Li, Yongbin
Zhou, Xiang
Wang, Jun
Zhang, Qian
Yang, Xuhai
Zhu, Lichun
Geng, Zhihua
Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title_full Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title_fullStr Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title_full_unstemmed Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title_short Prediction of Apple Slices Drying Kinetic during Infrared-Assisted-Hot Air Drying by Deep Neural Networks
title_sort prediction of apple slices drying kinetic during infrared-assisted-hot air drying by deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658811/
https://www.ncbi.nlm.nih.gov/pubmed/36360099
http://dx.doi.org/10.3390/foods11213486
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