{"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}
引用次数: 0
Abstract
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.
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.