For the rapid traceability detection of Zanthoxylum bungeanum products of different origins,a hybrid one dimension-convolution-al neural networks(1D-CNN)-long short-term memory(LSTM)model based on electronic tongue and electronic nose is proposed. Taking Zanthoxylum bungeanum from five different habitats as the test samples,the electronic tongue and nose are used to collect the taste and smell fingerprint information respectively. According to the signal characteristics,1D-CNN is designed to extract the local spatial features of taste and smell signals,and LSTM is used to capture the time series features. Finally,multi-layer perceptron is used to fuse the two fea-tures and distinguish the categories. The experiment results show that the combination of electronic nose and electronic tongue data can distinguish Zanthoxylum bungeanum of different origins with better accuracy than a single equipment. The proposed model has higher clas-sification accuracy compared to other depth models with the accuracy,precision,recall and F1-score of 99.0％,99.1％,99.0％,0.989 re-spectively. This will provide a new method for the rapid identification of Zanthoxylum bungeanum from different origins,and a new re-search idea for the traceability detection of other agricultural products.
维普
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