Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models
文献类型: 外文期刊
作者: Wang, Ye 1 ; Zuo, Zhi-Tian 2 ; Huang, Heng-Yu 1 ; Wang, Yuan-Zhong 1 ;
作者机构: 1.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China
关键词: Dendrobium; mid-infrared spectroscopy; near-infrared spectroscopy; chemometrics; authentication
期刊名称:ROYAL SOCIETY OPEN SCIENCE ( 影响因子:2.963; 五年影响因子:3.44 )
ISSN: 2054-5703
年卷期: 2019 年 6 卷 5 期
页码:
收录情况: SCI
摘要: Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLSDA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.
- 相关文献
作者其他论文 更多>>
-
Based on metabolomics and fourier transforms near infrared spectroscopy characterization of Lanxangia tsaoko chemical profile differences among fruit types and development of rapid identification and nutrient prediction models
作者:Fu, Deng-Ke;Yang, Wei-Ze;Yang, Mei-Quan;Yang, Tian-Mei;Wang, Yuan-Zhong;Zhang, Jin-Yu;Fu, Deng-Ke
关键词:Lanxangia tsao-ko; Phenotype; Metabolic differences; 2DCOS; ResNet; PLSR
-
FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example of Panax notoginseng
作者:Zuo, Zhi-Tian;Yao, Zeng-Yu;Zuo, Zhi-Tian;Wang, Yuan-Zhong
关键词:
-
Application of ATR-FTIR Spectrum Combined With Ensemble Learning and Deep Learning for Identification of Amomum tsao-ko at Different Drying Temperatures
作者:He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词:Amomum tsao-ko; deep learning; drying temperatures; ensemble learning; machine learning
-
Spatial and temporal distribution characteristics of Paris polyphylla var. yunnanensis and the prediction of steroidal saponins content
作者:Zhong, Chen;Li, Li;Zhong, Chen;Wang, Yuan-Zhong
关键词:Paris polyphylla var. yunnanensis; Habitat suitability; FT-IR spectroscopy; Chemometrics; Steroidal saponins
-
A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition
作者:He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang;Yang, Shao-bing;Wang, Yuan-zhong
关键词:chemometrics; classification; Fourier transform-near infrared spectroscopy; image recognition; Lanxangia tsaoko
-
Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR
作者:He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词:
-
FT-IR spectroscopy coupled with HPLC for qualitative and quantitative analysis of different parts of Gentiana rigescens Franch
作者:He, Gang;Zhu, Xin-yan;Wang, Yuan-zhong;He, Gang;Shen, Tao
关键词:Gentiana rigescens; Total secoiridoids; FT-IR; HPLC; Content prediction



