Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Sustainable Food Systems |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2023.1172522/full |
| _version_ | 1797828891858960384 |
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| author | Yongjun Zhang Yongjun Zhang Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao |
| author_facet | Yongjun Zhang Yongjun Zhang Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao |
| author_sort | Yongjun Zhang |
| collection | DOAJ |
| 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 (R2). 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. |
| first_indexed | 2024-04-09T13:12:45Z |
| format | Article |
| id | doaj.art-c58e281d2cf54179a545bd50f688fb03 |
| institution | Directory Open Access Journal |
| issn | 2571-581X |
| language | English |
| last_indexed | 2024-04-09T13:12:45Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj.art-c58e281d2cf54179a545bd50f688fb032023-05-12T06:38:06ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2023-05-01710.3389/fsufs.2023.11725221172522Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportationYongjun Zhang0Yongjun Zhang1Yongjun Zhang2Xinqing Xiao3Huanhuan Feng4Marina A. Nikitina5Xiaoshuan Zhang6Qinan Zhao7School of Information Engineering, Shandong Youth University of Political Science, Jinan, ChinaSmart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan, ChinaNew Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan, ChinaCollege of Engineering, Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing, ChinaCollege of Engineering, Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing, ChinaV.M. Gorbatov Federal Research Center for Foods Systems of RAS, Moscow, RussiaCollege of Engineering, Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing, ChinaInner Mongolia Academy of Agricultural and Animal Husbandry Science, Hohhot, ChinaNon-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 (R2). 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.https://www.frontiersin.org/articles/10.3389/fsufs.2023.1172522/fullstress measurementnon-invasive blood glucose detectiondata fusion modellive fish waterless transportationclustering |
| spellingShingle | Yongjun Zhang Yongjun Zhang Yongjun Zhang Xinqing Xiao Huanhuan Feng Marina A. Nikitina Xiaoshuan Zhang Qinan Zhao Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation Frontiers in Sustainable Food Systems stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering |
| title | Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation |
| title_full | Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation |
| title_fullStr | Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation |
| title_full_unstemmed | Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation |
| title_short | Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation |
| title_sort | stress fusion evaluation modeling and verification based on non invasive blood glucose biosensors for live fish waterless transportation |
| topic | stress measurement non-invasive blood glucose detection data fusion model live fish waterless transportation clustering |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2023.1172522/full |
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