Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation.
10.19540/j.cnki.cjcmm.20230115.101
- Author:
Shu-Lin YU
1
;
Jian-Ting GONG
2
;
Li LI
2
;
Jia-Li GUAN
2
;
En-Ai ZHAI
1
;
Shao-Qin OUYANG
1
;
Hui-Qin ZOU
1
;
Yong-Hong YAN
1
Author Information
1. School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 102488, China.
2. Beijing Institute of Traditional Chinese Medicine Beijing 100010, China.
- Publication Type:Journal Article
- Keywords:
Pollygonati Rhizoma;
electronic nose;
fast discriminant model;
mildew;
odor;
odor fingerprint
- MeSH:
Electronic Nose;
Odorants/analysis*;
Volatile Organic Compounds/analysis*;
Bayes Theorem;
Drugs, Chinese Herbal/analysis*;
Discriminant Analysis
- From:
China Journal of Chinese Materia Medica
2023;48(7):1833-1839
- CountryChina
- Language:Chinese
-
Abstract:
The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.
