Intelligent Dynamic Quality Prediction of Chilled Chicken with Integrated IoT Flexible Sensing and Knowledge Rules...

Area of Science:

• Food Science
• Engineering
• Computer Science

Background:

• Increasing consumer demand for food safety necessitates stringent meat quality control.
• Traditional methods for monitoring chilled meat quality are often destructive or lack real-time data.
• The chilled chicken supply chain faces challenges in maintaining optimal quality throughout distribution.

Purpose of the Study:

• To construct an intelligent dynamic prediction model for assessing chilled chicken quality.
• To integrate flexible humidity sensors with Internet of Things (IoT) technology for non-destructive monitoring.
• To provide real-time feedback and dynamic adjustments for the chilled chicken cold chain.

Main Methods:

• Development of an intelligent dynamic prediction model based on knowledge rules.
• Integration of flexible humidity sensors into IoT for non-destructive monitoring.
• Correlation analysis of collected data and extraction of critical cold chain factors for model optimization.

Main Results:

• The prediction model achieved an accuracy exceeding 90.5% in classifying chilled chicken quality levels.
• Regression coefficients were consistently close to 1.00, indicating high model reliability.
• Optimized sensing equipment integrated into packaging effectively addressed abnormal cold chain situations.

Conclusions:

• The developed model offers precise quality evaluation and intelligent prediction for chilled chicken.
• The system enhances cold chain management efficiency and packaging effectiveness.
• The model provides a valuable theoretical reference for e-commerce and timely decision-making in food distribution.