Analysis on Feasibility of Electronic Nose Technology for Rapid Identification of Bletillae Rhizoma and Its Approximate Decoction Pieces
10.13422/j.cnki.syfjx.20221447
- VernacularTitle:电子鼻技术应用于白及及其近似饮片快速辨识的可行性分析
- Author:
Han LI
1
;
Yanli WANG
2
;
Xuehua FAN
1
;
Haiyang LI
1
;
Fuguo HOU
1
;
Xinjing GUI
1
;
Junhan SHI
2
;
Lu ZHANG
2
;
Ruixin LIU
2
;
Xuelin LI
2
Author Information
1. Henan University of Chinese Medicine，Zhengzhou 450046，China
2. The First Affiliated Hospital of Henan University of Chinese Medicine，Zhengzhou 450000，China
- Publication Type:Journal Article
- Keywords:
electronic nose technology;
Bletillae Rhizoma;
identification model;
decoction pieces;
Gastrodiae Rhizoma;
Polygonati Odorati Rhizoma;
Bletillae Ochraceae Rhizoma
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(13):157-165
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo investigate the feasibility of applying electronic nose technology to rapidly identify Bletillae Rhizoma and its approximate decoction pieces. MethodA total of 134 batches of Bletillae Rhizoma and its approximate decoction pieces， including 45 batches of Bletillae Rhizoma， 30 batches of Gastrodiae Rhizoma， 30 batches of Polygonati Odorati Rhizoma and 29 batches of Bletillae Ochraceae Rhizoma， were collected as test samples. The olfactory sensory data of the samples were collected by PEN3 electronic nose as the independent variable（X）. Based on the identification results of the 2020 edition of Chinese Pharmacopoeia and local standards， as well as the high performance liquid chromatography（HPLC） fingerprint and original purchase information of 134 batches of the decoction pieces， the benchmark data Y of the identification model were obtained， and four chemometric methods of principal component analysis-discriminant analysis（PCA-DA）， partial least squares-discriminant analysis（PLS-DA）， least square-support vector machine（LS-SVM） and K-nearest neighbor（KNN） were used to establish the binary identification model for 45 batches of Bletillae Rhizoma and 89 batches of non-Bletillae Rhizoma and the quadratic identification model of the four kinds of decoction pieces， that is， Y=F（X）. ResultAfter leave-one-out cross validation， the positive discrimination rates of the above four models were 97.01%， 97.01%， 98.51% and 97.01% in the binary identification， and 97.76%， 89.55%， 98.51% and 97.01% in the quadratic identification， respectively. The highest positive discrimination rate could reach 98.51% for the binary and quadratic identification models， and LS-SVM algorithm is both the optimal one， the most suitable kernel functions were chosen as radial basis function and linear kernel function， respectively. The optimal models discriminated well with no unclassified samples. ConclusionElectronic nose technology can accurately and rapidly identify Bletillae Rhizoma and its approximate decoction pieces， which can provide new ideas and methods for rapid quality evaluation of other decoction pieces.
