Title:An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus
Volume: 24
Issue: 7
Author(s): Tianshu Wang, Yanpin Chao, Fangzhou Yin, Xichen Yang, Chenjun Hu and Kongfa Hu*
Affiliation:
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing Jiangsu 210029,China
Keywords:
Electronic nose, Convolutional Neural Network (CNN), feature extraction, deep learning, fructus crataegi, chinese
medicinal materials.
Abstract:
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 LCP1, convolution pooling layer LCP2 and a full connection
layer LFC. 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.
