Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV
Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly used methods cannot accurately obtai...
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ftfrontimediafig:oai:figshare.com:article/22809275 2024-09-15T18:34:02+00:00 Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao 2023-05-12T05:43:43Z https://doi.org/10.3389/fsufs.2023.1172522.s002 https://figshare.com/articles/media/Video_1_Stress_fusion_evaluation_modeling_and_verification_based_on_non-invasive_blood_glucose_biosensors_for_live_fish_waterless_transportation_WMV/22809275 unknown doi:10.3389/fsufs.2023.1172522.s002 https://figshare.com/articles/media/Video_1_Stress_fusion_evaluation_modeling_and_verification_based_on_non-invasive_blood_glucose_biosensors_for_live_fish_waterless_transportation_WMV/22809275 CC BY 4.0 Climate Change Processes Food Chemistry and Molecular Gastronomy (excl. Wine) Food Engineering Food Nutritional Balance Food Packaging Preservation and Safety Food Processing Food Sciences not elsewhere classified Manufacturing Safety and Quality Packaging Storage and Transportation (excl. Food and Agricultural Products) stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering Dataset Media 2023 ftfrontimediafig https://doi.org/10.3389/fsufs.2023.1172522.s002 2024-08-19T06:19:56Z Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly used methods cannot accurately obtain the BGL variations owing to the influence of an uncertain glucose exudation rate, ambient effects, and individualized differences. Our study proposes a non-invasive multi-sensor-fusion-based method to evaluate the dynamic BGL variations using the enhanced gray wolf-optimized backpropagation network (EGWO-BP) to continuously acquire more accurate trends. Furthermore, the K-means++ (KMPP) algorithm is utilized to further improve the accuracy of BGL acquisition by clustering fish with full consideration of its size features. In the verification test, turbot (Scophthalmus Maximus) was selected as an experimental subject to perform the continuous BGL monitoring in waterless keep-alive transportation by acquiring comprehensive biomarker information from different parts of fish skin mucus, such as fins, body, and tails. The comparison of results indicates that the KMPP-EGWO-BP can effectively acquire more accurate BGL variation than the traditional gray wolf-optimized backpropagation network (GWO-BP), particle swarm-optimized backpropagation network (PSO-BP), backpropagation network (BP), and support vector regression (SVR) by mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R 2 ). Finally, the proposed BGL fusion evaluation model can precisely acquire the live fish's physiological stress states to substantially reduce the potential mortality for the live fish circulation industry. Dataset Scophthalmus maximus Turbot Frontiers: Figshare |
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Open Polar |
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Frontiers: Figshare |
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ftfrontimediafig |
language |
unknown |
topic |
Climate Change Processes Food Chemistry and Molecular Gastronomy (excl. Wine) Food Engineering Food Nutritional Balance Food Packaging Preservation and Safety Food Processing Food Sciences not elsewhere classified Manufacturing Safety and Quality Packaging Storage and Transportation (excl. Food and Agricultural Products) stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering |
spellingShingle |
Climate Change Processes Food Chemistry and Molecular Gastronomy (excl. Wine) Food Engineering Food Nutritional Balance Food Packaging Preservation and Safety Food Processing Food Sciences not elsewhere classified Manufacturing Safety and Quality Packaging Storage and Transportation (excl. Food and Agricultural Products) stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
topic_facet |
Climate Change Processes Food Chemistry and Molecular Gastronomy (excl. Wine) Food Engineering Food Nutritional Balance Food Packaging Preservation and Safety Food Processing Food Sciences not elsewhere classified Manufacturing Safety and Quality Packaging Storage and Transportation (excl. Food and Agricultural Products) stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering |
description |
Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly used methods cannot accurately obtain the BGL variations owing to the influence of an uncertain glucose exudation rate, ambient effects, and individualized differences. Our study proposes a non-invasive multi-sensor-fusion-based method to evaluate the dynamic BGL variations using the enhanced gray wolf-optimized backpropagation network (EGWO-BP) to continuously acquire more accurate trends. Furthermore, the K-means++ (KMPP) algorithm is utilized to further improve the accuracy of BGL acquisition by clustering fish with full consideration of its size features. In the verification test, turbot (Scophthalmus Maximus) was selected as an experimental subject to perform the continuous BGL monitoring in waterless keep-alive transportation by acquiring comprehensive biomarker information from different parts of fish skin mucus, such as fins, body, and tails. The comparison of results indicates that the KMPP-EGWO-BP can effectively acquire more accurate BGL variation than the traditional gray wolf-optimized backpropagation network (GWO-BP), particle swarm-optimized backpropagation network (PSO-BP), backpropagation network (BP), and support vector regression (SVR) by mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R 2 ). Finally, the proposed BGL fusion evaluation model can precisely acquire the live fish's physiological stress states to substantially reduce the potential mortality for the live fish circulation industry. |
format |
Dataset |
author |
Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao |
author_facet |
Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao |
author_sort |
Yongjun Zhang |
title |
Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
title_short |
Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
title_full |
Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
title_fullStr |
Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
title_full_unstemmed |
Video_1_Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.WMV |
title_sort |
video_1_stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation.wmv |
publishDate |
2023 |
url |
https://doi.org/10.3389/fsufs.2023.1172522.s002 https://figshare.com/articles/media/Video_1_Stress_fusion_evaluation_modeling_and_verification_based_on_non-invasive_blood_glucose_biosensors_for_live_fish_waterless_transportation_WMV/22809275 |
genre |
Scophthalmus maximus Turbot |
genre_facet |
Scophthalmus maximus Turbot |
op_relation |
doi:10.3389/fsufs.2023.1172522.s002 https://figshare.com/articles/media/Video_1_Stress_fusion_evaluation_modeling_and_verification_based_on_non-invasive_blood_glucose_biosensors_for_live_fish_waterless_transportation_WMV/22809275 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.3389/fsufs.2023.1172522.s002 |
_version_ |
1810475760402563072 |