Non-Destructive Detection of Chicken Freshness Based on Electronic Nose Technology and Transfer Learning

Yunwei Xiong, Yuhua Li, Chenyang Wang, Hanqing Shi, Sunyuan Wang, Cheng Yong, Yan Gong, Wentian Zhang and Xiuguo Zou ()
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Yunwei Xiong: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yuhua Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Chenyang Wang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Hanqing Shi: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Sunyuan Wang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Cheng Yong: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yan Gong: College of Engineering, Northeastern University, Boston, MA 02115, USA
Wentian Zhang: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Xiuguo Zou: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 2, 1-19

Abstract: As a non-destructive detection method, an electronic nose can be used to assess the freshness of meats by collecting and analyzing their odor information. Deep learning can automatically extract features and uncover potential patterns in data, minimizing the influence of subjective factors such as selecting features artificially. A transfer-learning-based model was proposed for the electronic nose to detect the freshness of chicken breasts in this study. First, a 3D-printed electronic nose system is used to collect the odor data from chicken breast samples stored at 4 °C for 1–7 d. Then, three conversion to images methods are used to feed the recorded time series data into the convolutional neural network. Finally, the pre-trained AlexNet, GoogLeNet, and ResNet models are retrained in the last three layers while being compared to classic machine learning methods such as K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machines (SVM). The final accuracy of ResNet is 99.70%, which is higher than the 94.33% correct rate of the popular machine learning model SVM. Therefore, the electronic nose combined with conversion to images shows great potential for using deep transfer learning methods for chicken freshness classification.

Keywords: chicken freshness detection; electronic nose; transfer learning; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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