Microwave Antenna Sensor with Machine Learning for Non-Destructive Detection of Fresh Meat

Guoping Hu; Lin He; Guolong Shi; Fanli Meng; Yigang He

doi:10.1016/j.eng.2026.01.028

Engineering ›› :202601028 DOI: 10.1016/j.eng.2026.01.028

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Microwave Antenna Sensor with Machine Learning for Non-Destructive Detection of Fresh Meat

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Abstract

The quality of fresh meat inevitably deteriorates during refrigerated storage, and ammonia is a critical volatile marker of spoilage. Nevertheless, temperature and humidity fluctuations within the cold chain environment can decrease the reliability of ammonia detection. To overcome this limitation and increase the sensing precision, in this work, a microwave ammonia sensor with temperature and humidity compensation is proposed on the basis of a backpropagation (BP) neural network. By analyzing the correlation between the radiation gain of the sensor and the ammonia concentration and integrating a wireless power transmission model, a new wireless microwave ammonia sensing model was established. The sensing system was experimentally validated through real-time monitoring of ammonia released during the spoilage of refrigerated meat. The results indicate that BP neural network-based temperature and humidity (THBP) compensation with Pearson correlation analysis reduced the radio frequency signal zero-point frequency fluctuation by 14 MHz, limited the absolute error to 0.06 parts per million (ppm), and increased the detection accuracy by 31.11%. This work provides a reliable theoretical framework and practical approach for high-sensitivity, non-destructive monitoring of the quality of fresh meat in dynamic cold-chain.

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Fresh meat / Detection of quality deterioration / Temperature and humidity compensation / Backpropagation neural network-Pearson correlation analysis

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Guoping Hu, Lin He, Guolong Shi, Fanli Meng, Yigang He. Microwave Antenna Sensor with Machine Learning for Non-Destructive Detection of Fresh Meat. Engineering 202601028 DOI:10.1016/j.eng.2026.01.028

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