Format

Send to

Choose Destination
Evid Based Complement Alternat Med. 2014;2014:425341. doi: 10.1155/2014/425341. Epub 2014 Aug 19.

Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose.

Author information

1
Library, Beijing University of Chinese Medicine, No. 11 Bei San Huan Dong Lu, Chaoyang District, Beijing 100029, China.
2
Beijing University of Posts and Telecommunications, No. 10 Xi Tu Cheng Lu, Haidian District, Beijing 100876, China.
3
School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 6 Wang Jing Zhong Huan Nan Lu, Chaoyang District, Beijing 100102, China.
4
Institute of Pharmaceutical Sciences, University of Graz, Universitaetsplatz 4, 8010 Graz, Austria.

Abstract

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.

Supplemental Content

Full text links

Icon for Hindawi Limited Icon for PubMed Central
Loading ...
Support Center