Identification of Curcuma herbs using XGBoost algorithm in electronic nose odor fingerprint.
10.19540/j.cnki.cjcmm.20191101.101
- Author:
Jian-Ting GONG
1
;
Jia-Yu WANG
2
;
Li LI
1
;
Dong XU
3
;
Yue CONG
1
;
Jia-Li GUAN
1
;
Hao-Zhong WU
3
;
Hui-Qin ZOU
3
;
Yong-Hong YAN
3
Author Information
1. Beijing Institute of Chinese Medicine Beijing 100035,China.
2. Changchun Medical College Changchun 130031,China.
3. School of Chinese Pharmacy,Beijing University of Chinese Medicine Beijing 102488,China.
- Publication Type:Journal Article
- Keywords:
Curcuma herbs;
Ezhu;
Jianghuang;
Pianjianghuang;
XGBoost;
Yujin;
electronic nose;
odor fingerprint
- MeSH:
Algorithms;
Curcuma/classification*;
Discriminant Analysis;
Drugs, Chinese Herbal/analysis*;
Electronic Nose;
Medicine, Chinese Traditional;
Odorants/analysis*;
Plants, Medicinal/classification*
- From:
China Journal of Chinese Materia Medica
2019;44(24):5375-5381
- CountryChina
- Language:Chinese
-
Abstract:
This article aims to identify four commonly applied herbs from Curcuma genus of Zingiberaceae family,namely Curcumae Radix( Yujin),Curcumae Rhizoma( Ezhu),Curcumae Longae Rhizoma( Jianghuang) and Wenyujin Rhizoma Concisum( Pianjianghuang). The odor fingerprints of those four herbal medicines were collected by electronic nose,respectively. Meanwhile,XGBoost algorithm was introduced to data analysis and discriminant model establishment,with four indexes for performance evaluation,including accuracy,precision,recall,and F-measure. The discriminant model was established by XGBoost with positive rate of returning to 166 samples in the training set and 69 samples in the test set were 99. 39% and 95. 65%,respectively. The top four of the contribution to the discriminant model were LY2/g CT,P40/1,LY2/Gh and LY2/LG,the least contributing sensor was T70/2. Compared with support vector machine,random forest and artificial neural network,XGBoost algorithms shows better identification capacity with higher recognition efficiency. The accuracy,precision,recall and F-measure of the XGBoost discriminant model forecast set were 95. 65%,95. 25%,93. 07%,93. 75%,respectively. The superiority of XGBoost in the identification of Curcuma herbs was verified. Obviously,this new method could not only be suitable for digitization and objectification of traditional Chinese medicine( TCM) odor indicators,but also achieve the identification of different TCM based on their odor fingerprint in electronic nose system. The introduction of XGBoost algorithm and more excellent algorithms provide more ideas for the application of electronic nose in data mining for TCM studies.
