Identification of curcumin content in raw and vinegar-processed rhizomes of Curcuma kwangsiensis based on electronic nose combined with back propagation neural network.
10.19540/j.cnki.cjcmm.20200523.304
- Author:
Zhen-Wei LAN
1
;
De JI
2
;
Shu-Mei WANG
1
;
Tu-Lin LU
2
;
Jiang MENG
1
Author Information
1. Engineering Technology Research Center for Chinese Materia Medica Quality of Universities in Guangdong Province,Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica, State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Guangdong Pharmaceutical University Guangzhou 510006, China.
2. School of Pharmacy, Nanjing University of Chinese Medicine Nanjing 210023, China.
- Publication Type:Journal Article
- Keywords:
BP neural network;
Curcuma kwangsiensis;
content prediction;
curcumin;
electronic nose;
vinegar-processed rhizomes of Curcuma kwangsiensis
- MeSH:
Acetic Acid;
Bayes Theorem;
Curcuma;
Curcumin;
Electronic Nose;
Neural Networks, Computer;
Rhizome
- From:
China Journal of Chinese Materia Medica
2020;45(16):3863-3870
- CountryChina
- Language:Chinese
-
Abstract:
This study aimed to establish a rapid and accurate method for identification of raw and vinegar-processed rhizomes of Curcuma kwangsiensis, in order to predict the content of curcumin compounds for scientific evaluation. A complete set of bionics recognition mode was adopted. The digital odor signal of raw and vinegar-processed rhizomes of Curcuma kwangsiensis were obtained by e-nose, and analyzed by back propagation(BP) neural network algorithm, with the accuracy, the sensitivity and specificity in discriminant model, correlation coefficient as well as the mean square error in regression model as the evaluation indexes. The experimental results showed that the three indexes of the e-nose signal discrimination model established by the neural network algorithm were 100% in training set, correction set and prediction set, which were obviously better than the traditional decision tree, naive bayes, support vector machine, K nearest neighbor and boost classification, and could accurately differentiate the raw and vinegar products. Correlation coefficient and mean square error of the regression model in prediction set were 0.974 8 and 0.117 5 respectively, and could well predict curcumin compounds content in Curcuma kwangsiensis, and demonstrate the superiority of the simulation biometrics model in the analysis of traditional Chinese medicine. By BP neural network algorithm, e-nose odor fingerprint could quickly, conveniently and accurately realize the discrimination and regression, which suggested that more bionics information acquisition and identification patterns could be combined in the field of traditional Chinese medicine, so as to provide ideas and methods for the rapid evaluation and stan-dardization of the quality of traditional Chinese medicine.
