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...
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| Veröffentlicht in: | Frontiers in sustainable food systems Jg. 7 |
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12.05.2023
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Xiao, Xinqing Zhao, Qinan Zhang, Xiaoshuan Zhang, Yongjun Feng, Huanhuan Nikitina, Marina A. |
| Author_xml | – sequence: 1 givenname: Yongjun surname: Zhang fullname: Zhang, Yongjun – sequence: 2 givenname: Xinqing surname: Xiao fullname: Xiao, Xinqing – sequence: 3 givenname: Huanhuan surname: Feng fullname: Feng, Huanhuan – sequence: 4 givenname: Marina A. surname: Nikitina fullname: Nikitina, Marina A. – sequence: 5 givenname: Xiaoshuan surname: Zhang fullname: Zhang, Xiaoshuan – sequence: 6 givenname: Qinan surname: Zhao fullname: Zhao, Qinan |
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| Cites_doi | 10.1016/j.aquaculture.2019.734834 10.1016/j.compag.2021.106642 10.3390/info12020059 10.1016/j.jmapro.2022.10.050 10.1373/clinchem.2004.036954 10.1016/j.bios.2014.09.015 10.1016/j.fsi.2016.11.005 10.1016/j.aquaculture.2021.737257 10.1109/TPDS.2014.2306193 10.1016/j.bios.2018.09.042 10.1016/j.compbiolchem.2018.11.017 10.1016/j.inffus.2018.09.007 10.1111/jfb.13904 10.1109/JSSC.2014.2384037 10.3390/s21248179 10.1016/j.mtcomm.2022.103263 10.1177/193229681000400312 10.1016/j.egyr.2020.03.003 10.1007/s11227-019-03015-0 10.1109/TITS.2020.3010296 10.1016/j.aquaeng.2021.102222 10.1007/s00521-017-3272-5 10.1016/j.bspc.2021.102706 10.1016/j.inffus.2020.01.009 10.1016/j.csda.2007.11.008 10.1016/j.bspc.2022.104552 10.3390/en16010132 10.1109/ACCESS.2020.2976509 10.1016/j.aquaculture.2019.734486 10.1016/j.talanta.2019.01.104 10.1016/j.bios.2008.08.038 10.1016/j.foodcont.2020.107809 10.1016/j.trc.2014.02.00602.006 10.1016/j.seta.2021.101029 10.1016/j.aquaculture.2018.09.039 10.1016/j.jelechem.2021.115029 10.1016/j.aqrep.2020.100514 10.3390/s19071518 10.1016/j.aquaculture.2018.03.059 |
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| References | Wang (B27) 2022; 31 Wu (B29) 2015 Gupta (B11) 2021; 68 Song (B20) 2015; 50 Wang (B25) 2021; 122 Faris (B6) 2018; 30 Li (B14) 2022 Zhang (B36) 2023; 16 Du (B4) 2021; 44 Hubert (B13) 2008; 52 Endo (B5) 2009; 24 Wang (B24) 2020; 76 Wang (B23) 2019; 51 Zou (B39) 2020; 60 Tian (B21) 2020; 6 Wu (B31); 130 Harman-Boehm (B12) 2010; 4 Zhao (B38) 2019; 78 Cho (B3) 2004 Wu (B30); 19 Li (B15) 2022; 84 Cai (B2) 2020 Feng (B7) 2022; 193 Zhang (B37) 2020; 8 Yu (B35) 2021; 882 Sadoul (B18) 2019; 94 Wu (B32) 2021; 12 Guardiola (B9) 2016; 59 Wang (B28) 2019; 198 Vanderzwalmen (B22) 2020; 514 Guo (B10) 2014; 43 Xu (B33) 2014; 25 Yang (B34) 2023; 82 Samaras (B19) 2021; 545 Fernández-Alacid (B8) 2019; 499 Lin (B16) 2021; 21 Liu (B17) 2021; 22 (B40) 2020 Aerts (B1) 2018; 492 Wang (B26) 2020 |
| References_xml | – year: 2020 ident: B26 article-title: Effects of waterless live transportation on survivability, physiological responses and flesh quality in Chinese farmed sturgeon (Acipenser schrenckii) publication-title: Aquaculture doi: 10.1016/j.aquaculture.2019.734834 – volume: 193 start-page: 106642 year: 2022 ident: B7 article-title: Modeling and evaluation of quality monitoring based on wireless sensor and blockchain technology for live fish waterless transportation publication-title: Comp. Electron. Agric. doi: 10.1016/j.compag.2021.106642 – year: 2020 ident: B40 publication-title: MathWorks.Inc – volume: 12 start-page: 59 year: 2021 ident: B32 article-title: Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network publication-title: Information doi: 10.3390/info12020059 – volume: 84 start-page: 913 year: 2022 ident: B15 article-title: A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting publication-title: J. Manuf. Process doi: 10.1016/j.jmapro.2022.10.050 – year: 2004 ident: B3 article-title: Noninvasive measurement of glucose by metabolic heat conformation method publication-title: Clin. Chem. doi: 10.1373/clinchem.2004.036954 – year: 2015 ident: B29 article-title: Fish stress become visible: a new attempt to use biosensor for real-time monitoring fish stress publication-title: Biosens. Bioelectron. doi: 10.1016/j.bios.2014.09.015 – volume: 59 start-page: 323 year: 2016 ident: B9 article-title: Using skin mucus to evaluate stress in gilthead seabream (Sparus aurata L.) publication-title: Fish Shellf. Immunol. doi: 10.1016/j.fsi.2016.11.005 – volume: 545 start-page: 737257 year: 2021 ident: B19 article-title: Cortisol concentration in scales is a valid indicator for the assessment of chronic stress in European sea bass, Dicentrarchus labrax L publication-title: Aquaculture doi: 10.1016/j.aquaculture.2021.737257 – volume: 25 start-page: 3135 year: 2014 ident: B33 article-title: Efficient k-Means++ Approximation with MapReduce publication-title: IEEE Transact. Parallel Distribut. Syst. doi: 10.1109/TPDS.2014.2306193 – volume: 130 start-page: 360 ident: B31 article-title: Real-time fish stress visualization came true:A novel multi-stage color-switching wireless biosensor system publication-title: Biosens. Bioelectron doi: 10.1016/j.bios.2018.09.042 – volume: 78 start-page: 481 year: 2019 ident: B38 article-title: Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2018.11.017 – volume: 51 start-page: 114 year: 2019 ident: B23 article-title: Dynamic identification of coal-rock interface based on adaptive weight optimization and multi-sensor information fusion publication-title: Inf. Fus. doi: 10.1016/j.inffus.2018.09.007 – volume: 94 start-page: 540 year: 2019 ident: B18 article-title: Measuring cortisol, the major stress hormone in fishes publication-title: J. Fish Biol. doi: 10.1111/jfb.13904 – volume: 50 start-page: 1025 year: 2015 ident: B20 article-title: An impedance and multi-wavelength near-infrared spectroscopy IC for noninvasive blood glucose estimation publication-title: IEEE J. Solid State Circ. doi: 10.1109/JSSC.2014.2384037 – volume: 21 start-page: 8179 year: 2021 ident: B16 article-title: An integrated wireless multi-sensor system for monitoring the water quality of aquaculture publication-title: Sensors doi: 10.3390/s21248179 – volume: 31 start-page: 103263 year: 2022 ident: B27 article-title: Inkjet-printed flexible sensors: From function materials, manufacture process, and applications perspective publication-title: Mater. Today Commun. doi: 10.1016/j.mtcomm.2022.103263 – volume: 4 start-page: 583 year: 2010 ident: B12 article-title: Noninvasive glucose monitoring: increasing accuracy by combination of multi-technology and multi-sensors publication-title: J. Diabetes Sci. Technol. doi: 10.1177/193229681000400312 – volume: 6 start-page: 620 year: 2020 ident: B21 article-title: Predictive model of energy consumption for office building by using improved GWO-BP publication-title: Energy Rep. doi: 10.1016/j.egyr.2020.03.003 – volume: 76 start-page: 1642 year: 2020 ident: B24 article-title: Multi-sensor data fusion of motion monitoring system based on BP neural network publication-title: J. Supercomput doi: 10.1007/s11227-019-03015-0 – volume: 22 start-page: 6583 year: 2021 ident: B17 article-title: Data fusion for multi-source sensors using GA-PSO-BP neural network publication-title: IEEE Transact. Intell. Transport. Syst. doi: 10.1109/TITS.2020.3010296 – year: 2022 ident: B14 article-title: Recent advances in intelligent recognition methods for fish stress behavior publication-title: Aquac. Eng doi: 10.1016/j.aquaeng.2021.102222 – volume: 30 start-page: 413 year: 2018 ident: B6 article-title: Grey wolf optimizer: a review of recent variants and applications publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-3272-5 – volume: 68 start-page: 102706 year: 2021 ident: B11 article-title: Towards noninvasive blood glucose measurement using machine learning: an all-purpose PPG system design publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.102706 – volume: 60 start-page: 1 year: 2020 ident: B39 article-title: Moving horizon estimation meets multi-sensor information fusion: development, opportunities and challenges publication-title: Inf. Fus. doi: 10.1016/j.inffus.2020.01.009 – volume: 52 start-page: 5186 year: 2008 ident: B13 article-title: An adjusted boxplot for skewed distributions publication-title: Comp. Stat. Data Anal doi: 10.1016/j.csda.2007.11.008 – volume: 82 start-page: 104552 year: 2023 ident: B34 article-title: Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.104552 – volume: 16 start-page: 132 year: 2023 ident: B36 article-title: Joint SOH-SOC estimation model for lithium-ion batteries based on GWO-BP publication-title: Neural Netw. Energ. doi: 10.3390/en16010132 – volume: 8 start-page: 40955 year: 2020 ident: B37 article-title: Multi-sensors-based physiological stress monitoring and online survival prediction system for live fish waterless transportation publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2976509 – volume: 514 start-page: 734486 year: 2020 ident: B22 article-title: effect of a water conditioner on ornamental fish behavior during commercial transport publication-title: Aquaculture doi: 10.1016/j.aquaculture.2019.734486 – volume: 198 start-page: 86 year: 2019 ident: B28 article-title: A thin film polyethylene terephthalate (PET). Electrochemical sensor for detection of glucose in sweat publication-title: Talanta doi: 10.1016/j.talanta.2019.01.104 – volume: 24 start-page: 1417 year: 2009 ident: B5 article-title: Wireless enzyme sensor system for real-time monitoring of blood glucose levels in fish publication-title: Biosens. Bioelectron. doi: 10.1016/j.bios.2008.08.038 – volume: 122 start-page: 107809 year: 2021 ident: B25 article-title: Optimization and validation of the knowledge-based traceability system for quality control in fish waterless live transportation publication-title: Food Control doi: 10.1016/j.foodcont.2020.107809 – volume: 43 start-page: 50 year: 2014 ident: B10 article-title: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification publication-title: Transport. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2014.02.00602.006 – volume: 44 start-page: 101029 year: 2021 ident: B4 article-title: Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods publication-title: Sustain. Energy Tchnol. Assess. doi: 10.1016/j.seta.2021.101029 – volume: 499 start-page: 185 year: 2019 ident: B8 article-title: Skin mucus metabolites and cortisol in meagre fed acute stress-attenuating diets: correlations between plasma and mucus publication-title: Aquaculture doi: 10.1016/j.aquaculture.2018.09.039 – volume: 882 start-page: 115029 year: 2021 ident: B35 article-title: Gold nanostructure-programmed flexible electrochemical biosensor for detection of glucose and lactate in sweat publication-title: J. Electroanal. Chem. doi: 10.1016/j.jelechem.2021.115029 – year: 2020 ident: B2 article-title: Study of a noninvasive detection method for the high-temperature stress response of the large yellow croaker (Larimichthys crocea). publication-title: Aquac. Rep doi: 10.1016/j.aqrep.2020.100514 – volume: 19 start-page: 1518 ident: B30 article-title: Development of a novel enhanced biosensor system for real-time monitoring of fish stress using a self-assembled monolayer publication-title: Sensors doi: 10.3390/s19071518 – volume: 492 start-page: 40 year: 2018 ident: B1 article-title: Vibrio lentus as a probiotic candidate lowers glucocorticoid levels in gnotobiotic sea bass larvae publication-title: Aquaculture doi: 10.1016/j.aquaculture.2018.03.059 |
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