Data-driven nanomechanical sensing: specific information extraction from a complex system

Abstract Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realiz...

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Autores principales:	Kota Shiba, Ryo Tamura, Gaku Imamura, Genki Yoshikawa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/caa34293246b47a493b506e5781a093d
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spelling	oai:doaj.org-article:caa34293246b47a493b506e5781a093d2021-12-02T16:06:52ZData-driven nanomechanical sensing: specific information extraction from a complex system10.1038/s41598-017-03875-72045-2322https://doaj.org/article/caa34293246b47a493b506e5781a093d2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03875-7https://doaj.org/toc/2045-2322Abstract Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.Kota ShibaRyo TamuraGaku ImamuraGenki YoshikawaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kota Shiba Ryo Tamura Gaku Imamura Genki Yoshikawa Data-driven nanomechanical sensing: specific information extraction from a complex system
description	Abstract Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.
format	article
author	Kota Shiba Ryo Tamura Gaku Imamura Genki Yoshikawa
author_facet	Kota Shiba Ryo Tamura Gaku Imamura Genki Yoshikawa
author_sort	Kota Shiba
title	Data-driven nanomechanical sensing: specific information extraction from a complex system
title_short	Data-driven nanomechanical sensing: specific information extraction from a complex system
title_full	Data-driven nanomechanical sensing: specific information extraction from a complex system
title_fullStr	Data-driven nanomechanical sensing: specific information extraction from a complex system
title_full_unstemmed	Data-driven nanomechanical sensing: specific information extraction from a complex system
title_sort	data-driven nanomechanical sensing: specific information extraction from a complex system
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/caa34293246b47a493b506e5781a093d
work_keys_str_mv	AT kotashiba datadrivennanomechanicalsensingspecificinformationextractionfromacomplexsystem AT ryotamura datadrivennanomechanicalsensingspecificinformationextractionfromacomplexsystem AT gakuimamura datadrivennanomechanicalsensingspecificinformationextractionfromacomplexsystem AT genkiyoshikawa datadrivennanomechanicalsensingspecificinformationextractionfromacomplexsystem
_version_	1718384868942938112

Data-driven nanomechanical sensing: specific information extraction from a complex system

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