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Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose
Joint Authors
Zou, Hui-Qin
Li, Shuo
Huang, Ying-Hua
Liu, Yong
Bauer, Rudolf
Peng, Lian
Tao, Ou
Yan, Su-Rong
Yan, Yong-Hong
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-19
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area.
Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers’ safety and efficacy.
In recent decades, electronic nose (E-nose) has been studied as an alternative approach.
In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model.
Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models.
Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data.
It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants.
This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.
American Psychological Association (APA)
Zou, Hui-Qin& Li, Shuo& Huang, Ying-Hua& Liu, Yong& Bauer, Rudolf& Peng, Lian…[et al.]. 2014. Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose. Evidence-Based Complementary and Alternative Medicine،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1018458
Modern Language Association (MLA)
Zou, Hui-Qin…[et al.]. Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose. Evidence-Based Complementary and Alternative Medicine No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1018458
American Medical Association (AMA)
Zou, Hui-Qin& Li, Shuo& Huang, Ying-Hua& Liu, Yong& Bauer, Rudolf& Peng, Lian…[et al.]. Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose. Evidence-Based Complementary and Alternative Medicine. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1018458
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1018458