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Comparative Study of Simulation of Temperature Rise in Ring Main Unit
Published on: July 5, 2024
Dynamic Quality Prediction and Intelligent Classification of Litopenaeus vannamei in Cold Chain Based on
Wei Dong1, Min Niu2, Huan Jiang2
1School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, China.
Foods (Basel, Switzerland)
|August 13, 2026
Summary
Temperature fluctuations degrade shrimp quality in cold chains. A new Tad-Transformer model accurately predicts shrimp quality and grade evolution, improving food safety and value.
Area of Science:
- Food Science and Technology
- Artificial Intelligence in Agriculture
- Cold Chain Logistics
Background:
- Temperature fluctuations in cold chain logistics accelerate the degradation of aquatic products, impacting their value and safety.
- Litopenaeus vannamei quality is compromised by protein degradation, lipid oxidation, and color changes due to temperature variations.
Purpose of the Study:
- To develop a dynamic quality prediction method for Litopenaeus vannamei in cold chain logistics.
- To enhance food safety and product value through intelligent quality monitoring.
Main Methods:
- Collected multi-dimensional physicochemical and texture data under six different cold chain temperature conditions.
- Utilized an improved K-means++ clustering algorithm for quality grading.
- Employed the Tad-Transformer neural network for predicting quality indicators and temporal grade evolution.
Main Results:
- The Tad-Transformer model achieved precision and recall exceeding 89% for high-quality samples.
- Demonstrated significant improvements (4.17-10.77% precision, 2.28-8.37% recall) over Transformer, Informer, and FEDformer models.
- Successfully predicted quality indicators and temporal grade evolution in Litopenaeus vannamei.
Conclusions:
- The proposed Tad-Transformer method offers effective dynamic quality prediction for aquatic products.
- Provides technical support for quality grading control and early warning systems in cold chain logistics.
- Offers a scientific reference for intelligent evaluation and dynamic quality monitoring of seafood.
