Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose
Abstract
Monosaccharides, which include the simple sugars such as glucose and fructose, are among the most important carbohydrates in the human diet. Certain chronic diseases, e.g., diabetes mellitus, are associated with anomalous glucose blood levels. Detecting and measuring the levels of monosaccharides in vivo or in aqueous solutions is thus of the utmost importance in life science, health, and point-of-care applications. Noninvasive sensing would avoid problems such as pain and potential infection hazards. Here, with the help of surface enhanced infrared absorption (SEIRA) spectroscopy, we demonstrate the reliable optical detection in the mid-infrared spectral range of pure glucose and fructose solutions as well as mixtures of both in aqueous solution. We utilize a reflection flow cell geometry with physiologically relevant concentrations as small as 10 g/L. As significant improvement over the standard baseline correction employed in SEIRA applications, we utilize principal component analysis (PCA) as machine learning algorithm, which is ideally suited for the extraction of vibrational data. We anticipate our results as important step in biosensing applications that will stimulate efforts to further improve the employed SEIRA substrates, the noise level of the spectroscopic light source, as well as the flow cell environment en route to significantly higher sensitivities and quantitative analysis, even in tear drops.
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AI algorithms can facilitate the analysis and processing of data by VOC sensors in a prompt, direct, and automated manner.
“AI algorithms can facilitate the analysis and processing of data by VOC sensors in a prompt, direct, and automated manner (John-Herpin et al. 2021; Kuhner et al. 2019).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Kuhner et al. 2019
Dimensionality reduction of spectral data using machine learning algorithms is an invaluable aid to VOC sensors.
“Therefore, dimensionality reduction of spectral data using ML algorithms is an invaluable aid to VOC sensors.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Kuhner et al. 2019
With machine learning, decoupling IR fingerprint absorption and refractive index change has been reported to monitor protein dynamics of secondary structure α-helix and β-sheet and recognition of glucose and fructose.
“With the aid of machine learning (ML), decoupling the IR fingerprint absorption and refractive index change induced by molecules has been reported to monitor protein dynamics of secondary structure α-helix and β-sheet48 and recognition of physiological bio-marker of glucose and fructose49.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [49]
Compared with previous literature that demonstrates the identification of two molecules mixture by monitoring two absorption peaks, this work demonstrated simultaneously monitoring of 15 absorption peaks and used to identify three molecules mixture.
“Compared with previous literature that demonstrates the identification of two molecules mixture by monitoring two absorption peaks49, our work demonstrated simultaneously monitoring of 15 absorption peaks and used to identify three molecules mixture.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [49]
Using the design principle of wavelength multiplexing, the WMHNA can be designed to cover all IR fingerprint ranges by engineering the antenna length for specific molecular monitoring like proteins, sugars, lipids, nucleic acids, and volatile organic compounds (VOCs).
“Using the design principle of wavelength multiplexing, the WMHNA can be designed to cover all IR fingerprint ranges by engineering the antenna length for specific molecular monitoring like proteins48, sugars49, lipids17, nucleic acids57, and volatile organic compounds (VOCs)58.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [49]
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References 46 · Cited by 68
- Authors
- Kühner, L., Semenyshyn, R., Hentschel, M., Neubrech, F., Tarín, C., & Giessen, H.
- Affiliations
- University of Stuttgart (DE)
- Corresponding author
- Giessen, Harald
- ORCID
- Venue
- ACS Sensors
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-07-05
- Volume
- 4
- Issue
- 8
- Pages
- 1973-1979
- ISSN
- 2379-3694 (ISSN-L)
- OpenAlex
- W2954884276
- Cited by
- 68
- References
- 46
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 9 · 2023: 7 · 2022: 14 · 2021: 12 · 2020: 7 · 2019: 3
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Sensors (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium f?r Bildung und Forschung
- Deutsche Forschungsgemeinschaft (SPP1839)
- Ministerium f?r Wissenschaft, Forschung und Kunst Baden-W?rttemberg (IQST, ZAQuant)
- Baden-W?rttemberg Stiftung (PROTEINSENS)
- H2020 European Research Council (COMPLEXPLAS)
- Topics
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Gold and Silver Nanoparticles Synthesis and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Engineering › Biomedical Engineering › Plasmonic and Surface Plasmon Research
- Keywords
- surface-enhanced infrared absorption, glucose detection, fructose detection, monosaccharides, diabetes mellitus, principal component analysis, baseline correction, mid-infrared spectroscopy, tear analysis, point-of-care testing
- MeSH terms
- Fructose, Glucose, Humans, Spectrophotometry, Infrared, Vibration, Biosensing Techniques, Principal Component Analysis, Nanotechnology
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