A practical method superior to traditional spectral identification: Two-dimensional correlation spectroscopy combined with deep learning to identify Paris species
文献类型： 外文期刊
作者： Yue, JiaQi 1 ; Huang, HengYu 2 ; Wang, YuanZhong 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 650500, Yunnan, Peoples R China
关键词： Paris; FT-MIR; 2DCOS; Deep learning; ResNet; Species identification
期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：4.821； 五年影响因子：4.364 ）
ISSN： 0026-265X
年卷期： 2021 年 160 卷
页码：
收录情况： SCI
摘要： Spectral analysis has the characteristics of fast and nondestructive. In order to conform to the development of the times, a practical method beyond the traditional spectral analysis was established. For the first time, the two-dimensional correlation spectroscopy (2DCOS) images of Fourier-transform mid-infrared spectroscopy combined with the Residual Neural Network (ResNet) was used for the identification and analysis of 12 Paris species, and the second derivative 2DCOS rarely involved in previous researchers was established. Besides, the fusion strategy of 2DCOS images based on feature bands was first proposed for modeling analysis. From the results, (1) 2DCOS combined with ResNet can successfully identify 12 Paris species. (2) 2DCOS is a powerful tool for identification, whether it is used for image visual analysis or modeling analysis. (3) Compared with asynchronous 2DCOS, synchronous 2DCOS is more suitable for the identification and analysis of complex mixed systems such as traditional Chinese medicine. (4) The modeling based on feature bands fusion strategy of 2DCOS has better model performance and is also suitable for the analysis of small samples. To sum up, what we proposed is an innovative and feasible method with wide applicability, which can not only solve the problem of identifying Paris, provide ideas and methods for the selection of spectral types and feature bands, but also provide practical reference for the research in analytical chemistry and other related fields.
- 相关文献
作者其他论文 更多>>
-
Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Gastrodia elata f. glauca; Fourier transform infrared spectroscopy; Deep learning; Data driven version of soft independent; modeling of class analogy
-
Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics
作者：Zheng, Chuanmao;Li, Jieqing;Zheng, Chuanmao;Wang, Yuanzhong;Liu, Honggao
关键词：Boletus bainiugan; HS-SPME-GC-MS; VOCs; 2DCOS; Chemometrics; Quality estimation
-
The method based on ATR-FTIR spectroscopy combined with feature variable selection for the boletus species and origins identification
作者：Ji, Zhiyi;Li, Jieqing;Ji, Zhiyi;Wang, Yuanzhong;Liu, Honggao
关键词：feature variable selection; food safety; mid-infrared spectroscopy; species identification; traceability; wild boletus
-
Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion
作者：Liu, Hong;Li, Jieqing;Liu, Hong;Wang, Yuanzhong;Liu, Honggao
关键词：2DCOS images; ATR-FTIR; data fusion; FT-NIR; Gastrodia elata Blume; geographical discrimination
-
A fast method for predicting adenosine content in porcini mushrooms using Fourier transform near-infrared spectroscopy combined with regression model
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Fourier transform near-infrared spectroscopy; Porcini mushrooms; Adenosine; Partial least squares regression
-
Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes
作者：Zheng, Chuanmao;Li, Jieqing;Zheng, Chuanmao;Wang, Yuanzhong;Liu, Honggao
关键词：Boletes; Amino acid metabolomics; LC-MS; FT-NIR; ATR-FTIR; 2DCOS
-
The genus Litsea: A comprehensive review of traditional uses, phytochemistry, pharmacological activities and other studies
作者：Li, Guangyao;Li, Guangyao;Li, Zhimin;Wang, Yuanzhong
关键词：L.; traditional uses; chemical components; pharmacological activities
