An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus.
Convolutional Neural Network (CNN)
Electronic nose
chinese
medicinal materials.
deep learning
feature extraction
fructus crataegi
Journal
Combinatorial chemistry & high throughput screening
ISSN: 1875-5402
Titre abrégé: Comb Chem High Throughput Screen
Pays: United Arab Emirates
ID NLM: 9810948
Informations de publication
Date de publication:
2021
2021
Historique:
received:
22
01
2020
revised:
05
04
2020
accepted:
20
05
2020
pubmed:
17
7
2020
medline:
16
12
2021
entrez:
17
7
2020
Statut:
ppublish
Résumé
The manual identification of Fructus Crataegi processed products is inefficient and unreliable. Therefore, efficient identification of the Fructus Crataegis' processed products is important. 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. 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 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. The method proposed in this paper is effective for the identification of Fructus Crataegi processed products.
Sections du résumé
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
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.
Identifiants
pubmed: 32669076
pii: CCHTS-EPUB-108193
doi: 10.2174/1386207323666200715171334
doi:
Substances chimiques
Drugs, Chinese Herbal
0
Plant Extracts
0
crataegus extract
6OM09RPY36
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
921-932Informations de copyright
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