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