Multi-Sensors-Based Physiological Stress Monitoring and Online Survival Prediction System for Live Fish Waterless Transportation

Liu

J Food Process Engineering

et al. 2021

Self Cite

This study puts forward a novel frozen aquatic product traceability system (BIOT‐TS) that based on blockchain and Internet of things (IoT) technology, which can alleviate the drawbacks of current tracing management during cold chain logistic, such as the weak security performance, inefficient centralized data management as well as the easy‐tampered traceability information. The proposed system possesses the advantages of decentralization, safety, reliability, and tamper‐proofing when it compares with traditional tracking technology. It is particularly suitable for multi‐stages tracking management of cold chain logistics from aquaculture, production, logistics, and circulation to sales. In terms of such blockchain‐IoT‐based technology, the system is deployed and evaluated with the case of frozen turbot (Scophthalmus maximus) that is circulated and sold under the background of e‐commerce cold chain logistics. It can significantly improve the quality and safety of the frozen aquatic product by using smart contracts, consensus strategy, and data on‐chain verification technology. The overall involved participants can share more reliable tracing information after adopting this tracing system. Furthermore, the performance of this hybrid tracing technology has been evaluated by the operations of on‐chain process and tracing queries through the real experiments, and has testified to be effective. Therefore, the application of this system is beneficial for the improvement of the aquatic food logistics industry and ensures credit in its ecosystem. Practical Applications The blockchain‐IoT‐based frozen aquatic product traceability management is one of the most promising technologies that possess the advantages of tamper‐proof, transparent, and irrefutable transactions combined with distributed monitoring and tracking functionalities. Through the decentralized data management, smart contract, and mechanism of consensus, the traceability information is partitioned and encrypted storage, which can be used for government supervision and consumers' retroactive queries. This novel frozen aquatic food product tracing technology can fundamentally improve the reliability of quality tracing applications and make the closely related tracing management more transparent, secure, and efficient.

Section: Methodsmentioning

confidence: 99%

“…The participants of the BIoT-TS system management (Xinqing et al, 2017;Zhang, Ning, Zhang, Glamuzina, & Xing, 2020).…”

Section: System Architecture Designmentioning

confidence: 99%

Development and assessment of blockchain‐IoT‐based traceability system for frozen aquatic product

Liu

J Food Process Engineering

et al. 2021

Self Cite

“…Recently, prediction and modelling of the river water quality play a significant role in environmental, ecological and water resource management (Barzegar et al, 2018;Zhang et al, 2020). Also, the quality of water is the major factor for inducing the aquatic disease outbreak and even huge quantities of death (Fusco et al, 2016;Sumit et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Chronological Harris hawks‐based deep LSTM classifier in wireless sensor network for aqua status prediction

Chandanapalli¹,

Rao²,

Srinivas³

et al. 2021

Ecohydrology

Aquaculture becomes very popular in economic where aquatic organisms, like fishes and prawns, are mainly dependent on the quality of water in aquaculture pond. Also, the water quality constraints, which include turbidity, carbon dioxide, temperature, pH level, dissolved oxygen and phosphorus, are considered for achieving better performance. Hence, this paper presents an approach for aqua status prediction based on Deep Long Short‐Term Memory (Deep LSTM) classifier. The sensor nodes are placed in the aqua pond for measuring the parameters of water quality, and then the cell network transformation is done using the Voronoi partition. After that, the Cluster Head (CH) selection is carried out using Piecewise Fuzzy C‐means clustering (piFCM). Once the clusters are selected, the Chronological Harris Hawks (Chronological HH) optimization algorithm is introduced for optimal sink placement where the constraints for enabling the optimal sink placement are the distance and energy of the nodes. Finally, the aqua status is predicted using Deep LSTM. The performance of the Chronological HH‐based Deep LSTM is computed in terms of accuracy, energy and the number of dead nodes. The proposed Chronological HH‐based Deep LSTM outperformed other methods with maximal accuracy of 92.65%, maximal energy of 0.976 and the minimal dead nodes of 32.

“…Sensory evaluation was used to analyze the quality of food [31], while the method based on multi-sensors for dynamic monitoring quality analysis is becoming increasingly popular, because of its high precision, accuracy, reliability and multi-aspect. Multi-sensors technology has been already applied in the cold chain transportation of fresh agricultural products such as peach [32], pear [33], fish [34,35], shellfish [36], and got a good effect on quality management.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensors Enabled Dynamic Monitoring and Quality Assessment System (DMQAS) of Sweet Cherry in Express Logistics

Wang

Xing

et al. 2020

Foods

Self Cite

The market demand for fresh sweet cherries in China has experienced continuous growth due to its rich nutritional value and unique taste. Nonetheless, the characteristics of fruits, transportation conditions and uneven distribution pose a huge obstacle in keeping high quality, especially in express logistics. This paper proposes dynamic monitoring and quality assessment system (DMQAS) to reduce the quality loss of sweet cherries in express logistics. The DMQAS was tested and evaluated in three typical express logistics scenarios with “Meizao” sweet cherries. The results showed that DMQAS could monitor the changes of critical micro-environmental parameters (temperature, relative humidity, O2, CO2 and C2H4) during the express logistics, and the freshness prediction model showed high accuracy (the relative error was controlled within 10%). The proposed DMQAS could provide complete and accurate microenvironment data and can be used to further improve the quality and safety management of sweet cherries during express logistics.