An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information
To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset w...
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2023
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Online Access: | https://doi.org/10.3390/foods12193616 https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86 |
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ftdoajarticles:oai:doaj.org/article:c9a545516c5449a3a5b04faf15508f86 2023-11-12T04:16:13+01:00 An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information Tao Lu Fanqianhui Yu Baokun Han Jingying Guo Kunhua Liu Shuai He 2023-09-01T00:00:00Z https://doi.org/10.3390/foods12193616 https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86 EN eng MDPI AG https://www.mdpi.com/2304-8158/12/19/3616 https://doaj.org/toc/2304-8158 doi:10.3390/foods12193616 2304-8158 https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86 Foods, Vol 12, Iss 3616, p 3616 (2023) oyster convolutional neural network freshness prediction feature visualization strongest activations Chemical technology TP1-1185 article 2023 ftdoajarticles https://doi.org/10.3390/foods12193616 2023-10-15T00:35:38Z To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence. Article in Journal/Newspaper Crassostrea gigas Pacific oyster Directory of Open Access Journals: DOAJ Articles Pacific Foods 12 19 3616 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
oyster convolutional neural network freshness prediction feature visualization strongest activations Chemical technology TP1-1185 |
spellingShingle |
oyster convolutional neural network freshness prediction feature visualization strongest activations Chemical technology TP1-1185 Tao Lu Fanqianhui Yu Baokun Han Jingying Guo Kunhua Liu Shuai He An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
topic_facet |
oyster convolutional neural network freshness prediction feature visualization strongest activations Chemical technology TP1-1185 |
description |
To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence. |
format |
Article in Journal/Newspaper |
author |
Tao Lu Fanqianhui Yu Baokun Han Jingying Guo Kunhua Liu Shuai He |
author_facet |
Tao Lu Fanqianhui Yu Baokun Han Jingying Guo Kunhua Liu Shuai He |
author_sort |
Tao Lu |
title |
An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
title_short |
An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
title_full |
An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
title_fullStr |
An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
title_full_unstemmed |
An Intelligent Method for Predicting Pacific Oyster ( Crassostrea gigas ) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information |
title_sort |
intelligent method for predicting pacific oyster ( crassostrea gigas ) freshness using deep learning fused with malondialdehyde and total sulfhydryl groups information |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/foods12193616 https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Crassostrea gigas Pacific oyster |
genre_facet |
Crassostrea gigas Pacific oyster |
op_source |
Foods, Vol 12, Iss 3616, p 3616 (2023) |
op_relation |
https://www.mdpi.com/2304-8158/12/19/3616 https://doaj.org/toc/2304-8158 doi:10.3390/foods12193616 2304-8158 https://doaj.org/article/c9a545516c5449a3a5b04faf15508f86 |
op_doi |
https://doi.org/10.3390/foods12193616 |
container_title |
Foods |
container_volume |
12 |
container_issue |
19 |
container_start_page |
3616 |
_version_ |
1782333369198051328 |