A lamb freshness detection model using a flexible optoelectronic in-situ sensing system and multi-input multi-label...

Area of Science:

• Food Science and Technology
• Sensory Science
• Biomedical Engineering

Background:

• Accurate and real-time meat freshness assessment is crucial for food safety and quality control.
• Traditional methods for meat freshness evaluation are often destructive, time-consuming, and lack real-time capabilities.
• Developing non-destructive, portable, and accurate sensing technologies is a significant research challenge.

Purpose of the Study:

• To develop and validate a novel flexible optoelectronic sensing system for non-destructive, real-time lamb meat freshness detection.
• To integrate the sensing system with an advanced machine learning model for accurate freshness grading.
• To assess the system's performance under various storage conditions and compare it with traditional methods.

Main Methods:

• Development of flexible impedance and optical sensing systems using laser direct writing and transfer technology.
• Evaluation of lamb meat freshness under controlled storage temperatures (0°C, 4°C, and 8°C).
• Statistical analysis including correlation and Granger causality tests to validate sensor data.
• Application of a 1DCNN-BiLSTM-ATT model for freshness grading and performance evaluation.

Main Results:

• The developed flexible optoelectronic sensing system demonstrated portability, stability, and high accuracy.
• A moderately strong correlation (r > 0.86) was observed between physicochemical properties, impedance measurements, and spectral data.
• The Granger causality test confirmed a significant causal relationship between impedance and spectral data (p < 0.05).
• The 1DCNN-BiLSTM-ATT model achieved an outstanding accuracy of 94.57% for lamb freshness grading.

Conclusions:

• The novel flexible optoelectronic sensing system offers an innovative and efficient solution for real-time lamb meat freshness prediction.
• The integrated learning model significantly enhances the accuracy and reliability of freshness assessment.
• This technology holds promise for improving food safety and reducing food waste in the meat industry.