Fingerprint terahertz spectroscopy combined with machine learning for multicomponent mixture analysis
文献类型：期刊论文
| 作者 | Yan, Hui2,3,4 ; Fan, Wen-Hui1,2,4; Qin, Chong2,4; Jiang, Xiao-Qiang2,4; Zhang, Yu-Ming2,4
|
| 刊名 | Vibrational Spectroscopy
 |
| 出版日期 | 2023-09 |
| 卷号 | 128 |
| ISSN号 | 09242031 |
| 关键词 | Terahertz spectroscopy Machine learning Multicomponent mixtures SMMA SVR |
| DOI | 10.1016/j.vibspec.2023.103581 |
| 产权排序 | 1 |
| 英文摘要 | The distinctive vibrational features in terahertz (THz) spectroscopy characterize a "fingerprint" of the single-component molecular substance. However, due to componential spectral overlapping and baseline drift, the identification and quantification of multicomponent mixtures are quite challenging for THz spectral analysis. A systematic and feasible strategy has been proposed by combining machine learning with THz spectroscopy for both qualitative and quantitative analysis. After the component number was effectively determined by singular value decomposition (SVD), nonnegative matrix factorization (NMF) and self-modeling mixture analysis (SMMA) were applied to extract componential THz spectra. The difficulties of NMF and SMMA encountered in handling ternary mixtures were solved. The results show component spectra extracted by SMMA are highly consistent with the experimental spectra of pure substances after standardization to correct baseline drift, which greatly facilitates rapid identification of compositions in mixtures. Additionally, compared to back-propagation neural network (BPNN), support vector regression (SVR) predict the contents of each individual component with high robustness and the decision coefficient R2 greater than 0.949. Fingerprint terahertz spectroscopy enhanced by machine learning provided an effective strategy for mixture analysis in practical applications. © 2023 Elsevier B.V. |
| 语种 | 英语 |
| 出版者 | Elsevier B.V. |
| 源URL | [http://ir.opt.ac.cn/handle/181661/96728]  |
| 专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
| 作者单位 | 1.Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan; 030006, China 2.University of Chinese Academy of Sciences, Beijing; 100049, China; 3.College of Science, Zhongyuan University of Technology, Zhengzhou Key Laboratory of Low-dimensional Quantum Materials and Devices, Zhengzhou; 450007, China; 4.State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; |
| 推荐引用方式 GB/T 7714 | Yan, Hui,Fan, Wen-Hui,Qin, Chong,et al. Fingerprint terahertz spectroscopy combined with machine learning for multicomponent mixture analysis[J]. Vibrational Spectroscopy,2023,128. |
| APA | Yan, Hui,Fan, Wen-Hui,Qin, Chong,Jiang, Xiao-Qiang,&Zhang, Yu-Ming.(2023).Fingerprint terahertz spectroscopy combined with machine learning for multicomponent mixture analysis.Vibrational Spectroscopy,128. |
| MLA | Yan, Hui,et al."Fingerprint terahertz spectroscopy combined with machine learning for multicomponent mixture analysis".Vibrational Spectroscopy 128(2023). |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。