Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation - Zhang, Yongjun; Xiao, Xinqing; Feng, Huanhuan; Nikitina, Marina A.; Zhang, Xiaoshuan; Zhao, Qinan (Frontiers in Sustainable Food Systems, 2023)
Noninvasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the live fish’s health status for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly-used methods cannot accurately get the BGL variations owing to the influence of an un-certain glucose exudation rate, ambient effects, and individualized differences. Our study proposes a noninvasive multi-sensor-fusion-based method to evaluate the dynamic BGL variations using the enhanced grey wolf optimized back propagation network (EGWO-BP) to continuously acquire its more accurate trends. Furthermore, the K-means++ (KMPP) algorithm is utilized to further improve the accuracy of BGL acqusition by clustering of 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 and keep alive transportation by acquiring comprehensive biomarkers information from different parts of fish skin mucus, such as fins, body, and tails. The comparison of results indicates the KMPP-EGWO-BP can effectively acquire more accurate BGL variation than traditional grey wolf optimized back propagation network (GWO-BP), particle swarm optimized back propagation network (PSO-BP), back propagation 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 in-dustry.