Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

dc.contributor.authorSrivastava, Sonalien
dc.contributor.authorWang, Weien
dc.contributor.authorZhou, Weien
dc.contributor.authorJin, Mingen
dc.contributor.authorVikesland, Peter J.en
dc.date.accessioned2025-03-04T18:00:01Zen
dc.date.available2025-03-04T18:00:01Zen
dc.date.issued2024-11-13en
dc.description.abstractSurface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.en
dc.description.versionPublished versionen
dc.format.extentPages 20830-20848en
dc.format.extent19 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1021/acs.est.4c06737en
dc.identifier.eissn1520-5851en
dc.identifier.issn0013-936Xen
dc.identifier.issue47en
dc.identifier.orcidVikesland, Peter [0000-0003-2654-5132]en
dc.identifier.orcidZhou, Wei [0000-0002-5257-3885]en
dc.identifier.pmid39537382en
dc.identifier.urihttps://hdl.handle.net/10919/124774en
dc.identifier.volume58en
dc.language.isoenen
dc.publisherAmerican Chemical Societyen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/39537382en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectSurface-Enhanced Raman Spectroscopyen
dc.subjectMachine Learningen
dc.subjectEnvironmental Pollutantsen
dc.subject.meshEnvironmental Pollutantsen
dc.subject.meshSpectrum Analysis, Ramanen
dc.subject.meshMachine Learningen
dc.titleMachine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Reviewen
dc.title.serialEnvironmental Science & Technologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Civil & Environmental Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications A Review.pdf
Size:
12.36 MB
Format:
Adobe Portable Document Format
Description:
Published version
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description: