Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR
文献类型： 外文期刊
作者： Li, Lian 1 ; Zuo, Zhi-Tian 1 ; Wang, Yuan-Zhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China
2.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China
关键词： FT-NIR; geographical traceability; PLS-DA; ResNet; Wolfiporia cocos
期刊名称：PHYTOCHEMICAL ANALYSIS （ 影响因子：3.024； 五年影响因子：3.018 ）
ISSN： 0958-0344
年卷期： 2022 年 33 卷 5 期
页码：
收录情况： SCI
摘要： Introduction Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. Objective This research aimed to identify the geographical traceability for different parts of W. cocos. Methods The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm(-1), 6300-5150 cm(-1) and 4450-4050 cm(-1) of original spectra and the second-order derivative (SD) spectra preprocessed. Results From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. Conclusion Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.
- 相关文献
作者其他论文 更多>>
-
Applications of chemical fingerprints and machine learning in plant ecology: Recent progress and future perspectives
作者：Zhong, Chen;Wang, Yuan-Zhong;Zhong, Chen;Li, Li
关键词：Chemical fingerprints; Chemometrics; Plant ecology; Analytical techniques; Machine learning algorithms
-
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：
-
Suitable habitat prediction and identification of origin of Lanxangia tsao-ko
作者：He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词：Medicinal plant; FT-NIR spectroscopy; Machine learning; Suitable habitats; Origin identification
-
Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species
作者：Li, Jie-Qing;Liu, Hong-Gao;Wang, Yuan-Zhong;Liu, Hong-Gao
关键词：boletes species; 2DCOS images; 3DCOS images; Alexnet; Resnet; large-screen visualization
-
Traditional uses, chemical compositions and pharmacological activities of Dendrobium: A review
作者：Li, Pei-Yuan;Wang, Yuan-Zhong;Li, Pei-Yuan;Li, Li
关键词：Dendrobium; Traditional use; Chemical composition; Pharmacological activity
-
A rapid identification based on FT-NIR spectroscopies and machine learning for drying temperatures of Amomum tsao-ko
作者：He, Gang;Lin, Qi;Yang, Shao-Bing;Wang, Yuan-Zhong;He, Gang;Lin, Qi
关键词：Identification research; FT-NIR spectroscopies; Machine learning; Chemometrics; Drying temperatures; Amomum tsao-ko
-
The potential of Amomum tsao-ko as a traditional Chinese medicine: Traditional clinical applications, phytochemistry and pharmacological properties
作者：He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词：Amomum tsao-ko; Chinese herbal medicine; Chemical compounds; Physiological characteristics; Review
