Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning
文献类型:期刊论文
作者 | Yan, Hui2,3,4; Fan, Wenhui1,2,4; Chen, Xu4; Wang, Hanqi2,4; Qin, Chong2,4; Jiang, Xiaoqiang2,4 |
刊名 | Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy |
出版日期 | 2022-04-15 |
卷号 | 271 |
ISSN号 | 13861425 |
关键词 | Preservative mixtures Terahertz spectroscopy Machine learning SVD NMF SVR |
DOI | 10.1016/j.saa.2022.120908 |
产权排序 | 1 |
英文摘要 | Preservatives are universally used in synergistic combination to enhance antimicrobial effect. Identify compositions and quantify components of preservatives are crucial steps in quality monitoring to guarantee merchandise safety. In the work, three most common preservatives, sorbic acid, potassium sorbate and sodium benzoate, are deliberately mixed in pairs with different mass ratios, which are supposed to be the "unknown" multicomponent systems and measured by terahertz (THz) time-domain spectroscopy. Subsequently, three major challenges have been accomplished by machine learning methods in this work. The singular value decomposition (SVD) effectively obtains the number of components in mixed preservatives. Then, the component spectra are successfully extracted by non-negative matrix factorization (NMF) and self-modeling mixture analysis (SMMA), which match well with the measured THz spectra of pure reagents. Moreover, the support vector machine for regression (SVR) designed an underlying model to the target components and simultaneously identify contents of each individual component in validation mixtures with decision coefficient R2 = 0.989. By taking advantages of the fingerprint-based THz technique and machine learning methods, our approach has been demonstrated the great potential to be served as a useful strategy for detecting preservative mixtures in practical applications. © 2022 Elsevier B.V. |
语种 | 英语 |
源URL | [http://ir.opt.ac.cn/handle/181661/95683] |
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 |
通讯作者 | Fan, Wenhui |
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, Wenhui,Chen, Xu,et al. Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning[J]. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy,2022,271. |
APA | Yan, Hui,Fan, Wenhui,Chen, Xu,Wang, Hanqi,Qin, Chong,&Jiang, Xiaoqiang.(2022).Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning.Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy,271. |
MLA | Yan, Hui,et al."Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning".Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 271(2022). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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