变分模态分解展开极值学习机用于复杂样品的光谱定量分析

IF 4.3 2区化学 Q1 SPECTROSCOPY

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy Pub Date : 2025-05-08 DOI:10.1016/j.saa.2025.126354

Liangliang Shen , Jiajing Zhao , Deyun Wu , Xiaoyao Tan , Xihui Bian

{"title":"变分模态分解展开极值学习机用于复杂样品的光谱定量分析","authors":"Liangliang Shen , Jiajing Zhao , Deyun Wu , Xiaoyao Tan , Xihui Bian","doi":"10.1016/j.saa.2025.126354","DOIUrl":null,"url":null,"abstract":"<div><div>Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples. Firstly, mode components (u<em>k</em>) are obtained by decomposing spectra in VMD. Then the mode components are unfolded into an extended matrix. Ultimately, a quantitative model is built between the matrix and the target values by ELM. Efficiency of VMD-UELM is validated by quantitative analysis of hemoglobin, diaromatics and <em>Panax notoginseng</em> (PN) in blood, fuel oil and adulterated herb datasets. Results show that VMD-UELM model demonstrates better or similar performance compared with partial least squares (PLS) and ELM. Therefore, VMD-UELM is an efficient approach for spectral quantitative analysis.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"340 ","pages":"Article 126354"},"PeriodicalIF":4.3000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Variational mode decomposition unfolded extreme learning machine for spectral quantitative analysis of complex samples\",\"authors\":\"Liangliang Shen , Jiajing Zhao , Deyun Wu , Xiaoyao Tan , Xihui Bian\",\"doi\":\"10.1016/j.saa.2025.126354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples. Firstly, mode components (u<em>k</em>) are obtained by decomposing spectra in VMD. Then the mode components are unfolded into an extended matrix. Ultimately, a quantitative model is built between the matrix and the target values by ELM. Efficiency of VMD-UELM is validated by quantitative analysis of hemoglobin, diaromatics and <em>Panax notoginseng</em> (PN) in blood, fuel oil and adulterated herb datasets. Results show that VMD-UELM model demonstrates better or similar performance compared with partial least squares (PLS) and ELM. Therefore, VMD-UELM is an efficient approach for spectral quantitative analysis.</div></div>\",\"PeriodicalId\":433,\"journal\":{\"name\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"volume\":\"340 \",\"pages\":\"Article 126354\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386142525006602\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386142525006602","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SPECTROSCOPY","Score":null,"Total":0}

引用次数: 0

摘要

考虑到变分模态分解（VMD）在数学分解和极限学习机（ELM）在数据建模方面的优势，提出了一种新的回归模型——变分模态分解展开极限学习机（VMD- uelm），用于复杂样品的光谱定量分析。首先，对VMD中的光谱进行分解，得到模态分量（uk）；然后将模态分量展开成扩展矩阵。最后，利用ELM在矩阵与目标值之间建立定量模型。通过对血液、燃料油和掺假草药数据集中的血红蛋白、二芳化合物和三七（PN）的定量分析，验证了VMD-UELM的有效性。结果表明，VMD-UELM模型与偏最小二乘（PLS）和ELM模型相比，具有更好或相近的性能。因此，VMD-UELM是一种有效的光谱定量分析方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Variational mode decomposition unfolded extreme learning machine for spectral quantitative analysis of complex samples

Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples. Firstly, mode components (uk) are obtained by decomposing spectra in VMD. Then the mode components are unfolded into an extended matrix. Ultimately, a quantitative model is built between the matrix and the target values by ELM. Efficiency of VMD-UELM is validated by quantitative analysis of hemoglobin, diaromatics and Panax notoginseng (PN) in blood, fuel oil and adulterated herb datasets. Results show that VMD-UELM model demonstrates better or similar performance compared with partial least squares (PLS) and ELM. Therefore, VMD-UELM is an efficient approach for spectral quantitative analysis.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 物理-光谱学

CiteScore

8.40

自引率

11.40%

发文量

1364

审稿时长

40 days

期刊介绍： Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science. The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments. Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate. Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to: Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences, Novel experimental techniques or instrumentation for molecular spectroscopy, Novel theoretical and computational methods, Novel applications in photochemistry and photobiology, Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.