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

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 degrees 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.

Automated summary

The study proposes a method using conversion to images for an electronic nose to detect the freshness of chicken breasts. Deep learning can automatically extract features and uncover potential patterns in data, minimizing the influence of subjective factors. The retrained ResNet model achieves an accuracy of 99.70%, higher than the 94.33% correct rate of the SVM model. Therefore, the electronic nose combined with conversion to images shows great potential for chicken freshness classification using deep transfer learning methods.