Cargando…
Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747440/ https://www.ncbi.nlm.nih.gov/pubmed/35009555 http://dx.doi.org/10.3390/s22010007 |
_version_ | 1784630837127413760 |
---|---|
author | Corcione, Emilio Pfezer, Diana Hentschel, Mario Giessen, Harald Tarín, Cristina |
author_facet | Corcione, Emilio Pfezer, Diana Hentschel, Mario Giessen, Harald Tarín, Cristina |
author_sort | Corcione, Emilio |
collection | PubMed |
description | The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing. |
format | Online Article Text |
id | pubmed-8747440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87474402022-01-11 Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing Corcione, Emilio Pfezer, Diana Hentschel, Mario Giessen, Harald Tarín, Cristina Sensors (Basel) Article The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing. MDPI 2021-12-21 /pmc/articles/PMC8747440/ /pubmed/35009555 http://dx.doi.org/10.3390/s22010007 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Corcione, Emilio Pfezer, Diana Hentschel, Mario Giessen, Harald Tarín, Cristina Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title | Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title_full | Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title_fullStr | Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title_full_unstemmed | Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title_short | Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing |
title_sort | machine learning methods of regression for plasmonic nanoantenna glucose sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747440/ https://www.ncbi.nlm.nih.gov/pubmed/35009555 http://dx.doi.org/10.3390/s22010007 |
work_keys_str_mv | AT corcioneemilio machinelearningmethodsofregressionforplasmonicnanoantennaglucosesensing AT pfezerdiana machinelearningmethodsofregressionforplasmonicnanoantennaglucosesensing AT hentschelmario machinelearningmethodsofregressionforplasmonicnanoantennaglucosesensing AT giessenharald machinelearningmethodsofregressionforplasmonicnanoantennaglucosesensing AT tarincristina machinelearningmethodsofregressionforplasmonicnanoantennaglucosesensing |