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Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation

文献类型： 外文期刊

作者： Zhang, Yongjun 1 ; Xiao, Xinqing 4 ; Feng, Huanhuan 4 ; Nikitina, Marina A. 5 ; Zhang, Xiaoshuan 4 ; Zhao, Qinan 6 ;

作者机构： 1.Shandong Youth Univ Polit Sci, Sch Informat Engn, Jinan, Peoples R China

2.Univ Shandong, Smart Healthcare Big Data Engn & Ubiquitous Comp C, Jinan, Peoples R China

3.Univ Shandong, New Technol Res & Dev Ctr Intelligent Informat Con, Jinan, Peoples R China

4.China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing, Peoples R China

5.RAS, V M Gorbatov Fed Res Ctr Foods Syst, Moscow, Russia

6.Inner Mongolia Acad Agr & Anim Husb Sci, Hohhot, Peoples R China

关键词： stress measurement; non-invasive blood glucose detection; data fusion model; live fish waterless transportation; clustering

期刊名称：FRONTIERS IN SUSTAINABLE FOOD SYSTEMS （ 影响因子：4.7； 五年影响因子：5.4 ）

ISSN：

年卷期： 2023 年 7 卷

页码：

收录情况： SCI

摘要： 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.

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