Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose
Identification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method...
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Online Access: | http://dx.doi.org/10.1155/2014/963035 |
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doaj-bb144723292045c29d3216d5e8cf95ef2020-11-24T22:24:24ZengHindawi LimitedEvidence-Based Complementary and Alternative Medicine1741-427X1741-42882014-01-01201410.1155/2014/963035963035Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-NoseLian Peng0Hui-Qin Zou1Rudolf Bauer2Yong Liu3Ou Tao4Su-Rong Yan5Yu Han6Jia-Hui Li7Zhi-Yu Ren8Yong-Hong Yan9School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaLibrary, Beijing University of Chinese Medicine, No. 11 Bei San Huan Dong Lu, Chao Yang District, Beijing 100029, ChinaInstitute of Pharmaceutical Science, University of Graz, Graz 8010, AustriaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaLibrary, Beijing University of Chinese Medicine, No. 11 Bei San Huan Dong Lu, Chao Yang District, Beijing 100029, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chao Yang District, Beijing 100102, ChinaIdentification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method for identification of ten different species of CHMs from Zingiberaceae family based on their response signals from E-nose. Ten Zingiberaceae CHMs were measured and their maximum response values were analyzed by principal component analysis (PCA). Result shows that E Zhu (Curcuma phaeocaulis Val.) and Yi Zhi (Alpinia oxyphylla Miq.) could not be distinguished completely by PCA. Two solutions were proposed: (i) using BestFirst+CfsSubsetEval (BC) method to extract more discriminative features to select sensors with higher contribution rate and remove the redundant signals; (ii) employing a novel cascade classifier with two stages to enhance the distinguishing-positive rate (DPR). Based on these strategies, six features were extracted and used in different stages of the cascade classifier with higher DPRs.http://dx.doi.org/10.1155/2014/963035 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lian Peng Hui-Qin Zou Rudolf Bauer Yong Liu Ou Tao Su-Rong Yan Yu Han Jia-Hui Li Zhi-Yu Ren Yong-Hong Yan |
spellingShingle |
Lian Peng Hui-Qin Zou Rudolf Bauer Yong Liu Ou Tao Su-Rong Yan Yu Han Jia-Hui Li Zhi-Yu Ren Yong-Hong Yan Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose Evidence-Based Complementary and Alternative Medicine |
author_facet |
Lian Peng Hui-Qin Zou Rudolf Bauer Yong Liu Ou Tao Su-Rong Yan Yu Han Jia-Hui Li Zhi-Yu Ren Yong-Hong Yan |
author_sort |
Lian Peng |
title |
Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose |
title_short |
Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose |
title_full |
Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose |
title_fullStr |
Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose |
title_full_unstemmed |
Identification of Chinese Herbal Medicines from Zingiberaceae Family Using Feature Extraction and Cascade Classifier Based on Response Signals from E-Nose |
title_sort |
identification of chinese herbal medicines from zingiberaceae family using feature extraction and cascade classifier based on response signals from e-nose |
publisher |
Hindawi Limited |
series |
Evidence-Based Complementary and Alternative Medicine |
issn |
1741-427X 1741-4288 |
publishDate |
2014-01-01 |
description |
Identification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method for identification of ten different species of CHMs from Zingiberaceae family based on their response signals from E-nose. Ten Zingiberaceae CHMs were measured and their maximum response values were analyzed by principal component analysis (PCA). Result shows that E Zhu (Curcuma phaeocaulis Val.) and Yi Zhi (Alpinia oxyphylla Miq.) could not be distinguished completely by PCA. Two solutions were proposed: (i) using BestFirst+CfsSubsetEval (BC) method to extract more discriminative features to select sensors with higher contribution rate and remove the redundant signals; (ii) employing a novel cascade classifier with two stages to enhance the distinguishing-positive rate (DPR). Based on these strategies, six features were extracted and used in different stages of the cascade classifier with higher DPRs. |
url |
http://dx.doi.org/10.1155/2014/963035 |
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