Reliable Quality Traceability for Tilapia Cold Chain Using Blockchain and Machine Learning Techniques
Year: 2024
Type: article
Abstract: ABSTRACT Tilapia are easily prone to degradation during the cold chain process, which is an urgent need for a transparent, efficient, and trustworthy traceability system. This paper designed and implemented a tilapia‐blockchain IoT traceability system (T‐BITS) based on Hyperledger Fabric. Intelligent sensing device and smart contracts were developed for traceability modeling and consensus optimization. Furthermore, a machine‐learning approach was used to achieve quality grading evaluation for tilapia cold chain. The GWO‐LSTM‐based key parameters prediction and PSO‐SVM‐based quality grading model were established. The results show that the T‐BITS system is more effective to capture and trace the critical ambient and quality information for tilapia cold chain. PSO‐SVM model accuracy for quality coupling grading reaches 93.33%. This work can provide decision‐making reference for tilapia quality control.
DOI: 10.1111/jfpe.70016
Authors: Huanhuan Feng, Jiaxin Fan, Yuxi Ji, Branko Glamuzina, Ruiqin Ma
Institutions: China Agricultural University, University of Dubrovnik
Language: English
Subfield: Food Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4405514145