Predicting concentrations of mixed sugar solutions with a combination of resonant plasmon-enhanced SEIRA and principal component analysis

dc.contributor.authorPfezer, Diana
dc.contributor.authorKarst, Julian
dc.contributor.authorHentschel, Mario
dc.contributor.authorGiessen, Harald
dc.date.accessioned2024-08-08T14:21:26Z
dc.date.available2024-08-08T14:21:26Z
dc.date.issued2022de
dc.date.updated2023-11-14T01:29:28Z
dc.description.abstractThe detection and quantification of glucose concentrations in human blood or in the ocular fluid gain importance due to the increasing number of diabetes patients. A reliable determination of these low concentrations is hindered by the complex aqueous environments in which various biomolecules are present. In this study, we push the detection limit as well as the discriminative power of plasmonic nanoantenna-based sensors towards the physiological limit. We utilize plasmonic surface-enhanced infrared absorption spectroscopy (SEIRA) to study aqueous solutions of mixtures of up to five different physiologically relevant saccharides, namely the monosaccharides glucose, fructose, and galactose, as well as the disaccharides maltose and lactose. Resonantly tuned plasmonic nanoantennas in a reflection flow cell geometry allow us to enhance the specific vibrational fingerprints of the mono- and disaccharides. The obtained spectra are analyzed via principal component analysis (PCA) using a machine learning algorithm. The high performance of the sensor together with the strength of PCA allows us to detect concentrations of aqueous mono- and disaccharides solutions down to the physiological levels of 1 g/L. Furthermore, we demonstrate the reliable discrimination of the saccharide concentrations, as well as compositions in mixed solutions, which contain all five mono- and disaccharides simultaneously. These results underline the excellent discriminative power of plasmonic SEIRA spectroscopy in combination with the PCA. This unique combination and the insights gained will improve the detection of biomolecules in different complex environments.en
dc.description.sponsorshipERC Advanced Grantde
dc.description.sponsorshipBaden-Württemberg Stiftungde
dc.description.sponsorshipCarl-Zeiss-Stiftung, Deutsche Forschungsgemeinschaftde
dc.description.sponsorshipMWK Baden-Württemberg (IQST, ZAQuant)de
dc.description.sponsorshipOpen Access Fund of the University of Stuttgartde
dc.identifier.issn1424-8220
dc.identifier.other1898212007
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-148088de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/14808
dc.identifier.urihttp://dx.doi.org/10.18419/opus-14789
dc.language.isoende
dc.relation.uridoi:10.3390/s22155567de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subject.ddc530de
dc.subject.ddc570de
dc.subject.ddc620de
dc.titlePredicting concentrations of mixed sugar solutions with a combination of resonant plasmon-enhanced SEIRA and principal component analysisen
dc.typearticlede
ubs.fakultaetMathematik und Physikde
ubs.institut4. Physikalisches Institutde
ubs.publikation.seiten16de
ubs.publikation.sourceSensors 22 (2022), No. 5567de
ubs.publikation.typZeitschriftenartikelde

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
sensors-22-05567.pdf
Size:
3.43 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.3 KB
Format:
Item-specific license agreed upon to submission
Description: