Application of Artificial Neural Network in the Baking Process of Salmon
The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron mi...
Published in: | Journal of Food Quality |
---|---|
Main Authors: | , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2022
|
Subjects: | |
Online Access: | https://doi.org/10.1155/2022/3226892 https://doaj.org/article/49ce67330a7149828c810bf0eff21fa0 |
id |
ftdoajarticles:oai:doaj.org/article:49ce67330a7149828c810bf0eff21fa0 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:49ce67330a7149828c810bf0eff21fa0 2024-09-15T17:56:29+00:00 Application of Artificial Neural Network in the Baking Process of Salmon Pengfei Jiang Kaiyue Zhu Shan Shang Wengang Jin Wanying Yu Shuang Li Shen Wang Xiuping Dong 2022-01-01T00:00:00Z https://doi.org/10.1155/2022/3226892 https://doaj.org/article/49ce67330a7149828c810bf0eff21fa0 EN eng Wiley http://dx.doi.org/10.1155/2022/3226892 https://doaj.org/toc/1745-4557 1745-4557 doi:10.1155/2022/3226892 https://doaj.org/article/49ce67330a7149828c810bf0eff21fa0 Journal of Food Quality, Vol 2022 (2022) Nutrition. Foods and food supply TX341-641 article 2022 ftdoajarticles https://doi.org/10.1155/2022/3226892 2024-08-05T17:48:46Z The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method. Article in Journal/Newspaper Atlantic salmon Directory of Open Access Journals: DOAJ Articles Journal of Food Quality 2022 1 12 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Nutrition. Foods and food supply TX341-641 |
spellingShingle |
Nutrition. Foods and food supply TX341-641 Pengfei Jiang Kaiyue Zhu Shan Shang Wengang Jin Wanying Yu Shuang Li Shen Wang Xiuping Dong Application of Artificial Neural Network in the Baking Process of Salmon |
topic_facet |
Nutrition. Foods and food supply TX341-641 |
description |
The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method. |
format |
Article in Journal/Newspaper |
author |
Pengfei Jiang Kaiyue Zhu Shan Shang Wengang Jin Wanying Yu Shuang Li Shen Wang Xiuping Dong |
author_facet |
Pengfei Jiang Kaiyue Zhu Shan Shang Wengang Jin Wanying Yu Shuang Li Shen Wang Xiuping Dong |
author_sort |
Pengfei Jiang |
title |
Application of Artificial Neural Network in the Baking Process of Salmon |
title_short |
Application of Artificial Neural Network in the Baking Process of Salmon |
title_full |
Application of Artificial Neural Network in the Baking Process of Salmon |
title_fullStr |
Application of Artificial Neural Network in the Baking Process of Salmon |
title_full_unstemmed |
Application of Artificial Neural Network in the Baking Process of Salmon |
title_sort |
application of artificial neural network in the baking process of salmon |
publisher |
Wiley |
publishDate |
2022 |
url |
https://doi.org/10.1155/2022/3226892 https://doaj.org/article/49ce67330a7149828c810bf0eff21fa0 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_source |
Journal of Food Quality, Vol 2022 (2022) |
op_relation |
http://dx.doi.org/10.1155/2022/3226892 https://doaj.org/toc/1745-4557 1745-4557 doi:10.1155/2022/3226892 https://doaj.org/article/49ce67330a7149828c810bf0eff21fa0 |
op_doi |
https://doi.org/10.1155/2022/3226892 |
container_title |
Journal of Food Quality |
container_volume |
2022 |
container_start_page |
1 |
op_container_end_page |
12 |
_version_ |
1810432681131900928 |