Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures

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dc.citation.articleNumbere2211406119en_US
dc.citation.issueNumber52en_US
dc.citation.journalTitleProceedings of the National Academy of Sciencesen_US
dc.citation.volumeNumber119en_US
dc.contributor.authorBajomo, Mary M.en_US
dc.contributor.authorJu, Yilongen_US
dc.contributor.authorZhou, Jingyien_US
dc.contributor.authorElefterescu, Siminaen_US
dc.contributor.authorFarr, Corbinen_US
dc.contributor.authorZhao, Yipingen_US
dc.contributor.authorNeumann, Oaraen_US
dc.contributor.authorNordlander, Peteren_US
dc.contributor.authorPatel, Ankiten_US
dc.contributor.authorHalas, Naomi J.en_US
dc.contributor.orgLaboratory for Nanophotonicsen_US
dc.date.accessioned2023-01-27T14:47:30Zen_US
dc.date.available2023-01-27T14:47:30Zen_US
dc.date.issued2022en_US
dc.description.abstractSurface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.en_US
dc.identifier.citationBajomo, Mary M., Ju, Yilong, Zhou, Jingyi, et al.. "Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures." <i>Proceedings of the National Academy of Sciences,</i> 119, no. 52 (2022) PNAS: https://doi.org/10.1073/pnas.2211406119.en_US
dc.identifier.digitalpnas-2211406119en_US
dc.identifier.doihttps://doi.org/10.1073/pnas.2211406119en_US
dc.identifier.urihttps://hdl.handle.net/1911/114282en_US
dc.language.isoengen_US
dc.publisherPNASen_US
dc.rightsThis article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleComputational chromatography: A machine learning strategy for demixing individual chemical components in complex mixturesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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