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

Author(s): Tianshu Wang, Yanpin Chao, Fangzhou Yin, Xichen Yang, Chenjun Hu, Kongfa Hu*

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 24 , Issue 7 , 2021

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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.

Keywords: Electronic nose, Convolutional Neural Network (CNN), feature extraction, deep learning, fructus crataegi, chinese medicinal materials.

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Article Details

Year: 2021
Published on: 15 July, 2020
Page: [921 - 932]
Pages: 12
DOI: 10.2174/1386207323666200715171334
Price: $65

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