Literature DB >> 31274277

Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose.

Lucca Kühner1, Rostyslav Semenyshyn1, Mario Hentschel1, Frank Neubrech1,2, Cristina Tarín3, Harald Giessen1.   

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.

Entities:  

Keywords:  biosensing; fructose; glucose; glucose sensor; optical and noninvasive sensing; principal component analysis; surface-enhanced infrared absorption

Year:  2019        PMID: 31274277     DOI: 10.1021/acssensors.9b00488

Source DB:  PubMed          Journal:  ACS Sens        ISSN: 2379-3694            Impact factor:   7.711


  9 in total

1.  Decoding Optical Data with Machine Learning.

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4.  Fabrication and Characterization of a Metallic-Dielectric Nanorod Array by Nanosphere Lithography for Plasmonic Sensing Application.

Authors:  Yuan-Fong Chou Chau; Kuan-Hung Chen; Hai-Pang Chiang; Chee Ming Lim; Hung Ji Huang; Chih-Hsien Lai; N T R N Kumara
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Review 5.  Non-Invasive Blood Glucose Monitoring Technology: A Review.

Authors:  Liu Tang; Shwu Jen Chang; Ching-Jung Chen; Jen-Tsai Liu
Journal:  Sensors (Basel)       Date:  2020-12-04       Impact factor: 3.576

6.  Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing.

Authors:  Emilio Corcione; Diana Pfezer; Mario Hentschel; Harald Giessen; Cristina Tarín
Journal:  Sensors (Basel)       Date:  2021-12-21       Impact factor: 3.576

7.  Plasmonic Resonant Nanoantennas Induce Changes in the Shape and the Intensity of Infrared Spectra of Phospholipids.

Authors:  Fatima Omeis; Zahia Boubegtiten-Fezoua; Ana Filipa Santos Seica; Romain Bernard; Muhammad Haseeb Iqbal; Nicolas Javahiraly; Robrecht M A Vergauwe; Hicham Majjad; Fouzia Boulmedais; David Moss; Petra Hellwig
Journal:  Molecules       Date:  2021-12-23       Impact factor: 4.411

8.  Predicting Concentrations of Mixed Sugar Solutions with a Combination of Resonant Plasmon-Enhanced SEIRA and Principal Component Analysis.

Authors:  Diana Pfezer; Julian Karst; Mario Hentschel; Harald Giessen
Journal:  Sensors (Basel)       Date:  2022-07-26       Impact factor: 3.847

9.  Infrared Plasmonic Biosensor with Tetrahedral DNA Nanostructure as Carriers for Label-Free and Ultrasensitive Detection of miR-155.

Authors:  Xindan Hui; Cheng Yang; Dongxiao Li; Xianming He; He Huang; Hong Zhou; Ming Chen; Chengkuo Lee; Xiaojing Mu
Journal:  Adv Sci (Weinh)       Date:  2021-06-21       Impact factor: 16.806

  9 in total

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