An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus.

BACKGROUND

The manual identification of Fructus Crataegi processed products is inefficient and unreliable. Therefore, efficient identification of the Fructus Crataegis' processed products is important.

OBJECTIVE

In order to efficiently identify Fructus Crataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed.

METHODS

First, the original smell of Fructus Crataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer L, convolution pooling layer L and a full connection layer L. Thus, the feature vector of the processed products can be obtained. Then, the recognition model for Fructus Grataegis processed products is constructed, and the model is trained to obtain the optimized parameters: filters F1 and F2, bias vectors B1, B2, B3, and B4, matrices M1 and M2. Finally, the features of the target processed products are extracted through the trained parameters to achieve accurate prediction.

RESULTS

The experimental results show that the proposed method has higher accuracy for the identification of Fructus Crataegis processed products, and is competitive with other machine learning based methods.

CONCLUSION

The method proposed in this paper is effective for the identification of Fructus Crataegi processed products.