Tilapia freshness prediction utilizing gas sensor array system combined with convolutional neural network pattern recognition model

Abstract

ABSTRACT Freshness of tilapia stored under cold storage was studied by utilizing gas sensor array combined with convolutional neural network (CNN) pattern recognition model in this paper. Total volatile basic nitrogen (TVBN) index was conducted to supply a freshness mark for tilapia sample. A portable electronic nose was designed and fabricated. The sensor array responses to tilapia sample were recorded. Principal component analysis (PCA) was used for gas sensor array data treatment, and this method could not discriminate all the samples. CNN model was optimized. The structure of CNN model had one input layer, four convolutional layers, two pooling layers, one Dropout layer, one fully connected layer and one output layer. The predicting accuracy of optimized CNN was 92.31%. The method investigated in this paper presented some advantages including rapid, easy operation, high sensitivity, high precision, etc. This method can be promising in aquatic food quality evaluating applications.