Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose
المؤلفون المشاركون
Zou, Hui-Qin
Li, Shuo
Huang, Ying-Hua
Liu, Yong
Bauer, Rudolf
Peng, Lian
Tao, Ou
Yan, Su-Rong
Yan, Yong-Hong
المصدر
Evidence-Based Complementary and Alternative Medicine
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-08-19
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1018458
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر