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http://dx.doi.org/10.5909/JBE.2020.25.3.418

Deep Learning-based Analysis of Meat Freshness Measurement  

Jang, Aera (Kangwon National University, College of Animal Life Science, Dept. of Applied Animal Science)
Kim, Hey-Jin (Kangwon National University, College of Animal Life Science, Dept. of Applied Animal Science)
Kim, Manbae (Kangwon National University, Dept. of Computer & Communications Engineering)
Publication Information
Journal of Broadcast Engineering / v.25, no.3, 2020 , pp. 418-427 More about this Journal
Abstract
The measurement of meat freshness at meat markets is important for the health of consumers. Currently a variety of sensors have been studied for the measurement of the meat freshness. Therefore, the analysis of sensor data is needed for the reduction of measurement errors. In this paper, we analyze the freshness measurement data of ten sensors based on deep learning. The measured data are composed of beef, pork and chicken, whose reliability and noise-robustness are examined by a deep neural network. Further, to search for multiple sensors better than a torrymeter, PCA (principle component analysis) is carried. Then, we validated that the performance of the three sensors outperforms the torrymeter in the experiment.
Keywords
meat freshness; deep learning; multi-sensor; robustness;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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