Abstract

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.
Original languageEnglish
Article number100002
JournalMaterials TodayBIO
Volume1
DOIs
Publication statusPublished - 1 Jan 2019

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Volatile Organic Compounds
Gelatin
Film thickness
Gels
Gases
Molecular recognition
Chemical analysis
Pattern recognition
Support vector machines
Brain
Signal processing
Classifiers
Optical properties
Proteins

Keywords

  • Gelatin
  • Ionic liquid
  • Liquid crystal
  • Gas sensor
  • Electronic nose
  • Machine learning

Cite this

@article{abb30d3b51b94c00863ae7f919da6598,
title = "Effect of film thickness in gelatin hybrid gels for artificial olfaction",
abstract = "Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98{\%}), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.",
keywords = "Gelatin, Ionic liquid, Liquid crystal, Gas sensor, Electronic nose, Machine learning",
author = "Carina Esteves and Santos, {Gon{\cc}alo M. C.} and Cl{\'a}udia Alves and Palma, {Susana I. C. J.} and Porteira, {Ana R.} and Jo{\~a}o Filho and Costa, {Henrique M. A.} and Alves, {Vitor D.} and {Morais Faustino}, {Bruno M.} and Isabel Ferreira and Hugo Gamboa and Roque, {Ana C. A.}",
note = "This work was supported by the European Research Council through the grant references SCENT-ERC-2014-STG-639123 and CapTherPV-ERC-2014-CoG-647596 and by the Unidade de Ci{\^e}ncias Biomoleculares Aplicadas (UCIBIO) and Linking Landscape, Environment, Agriculture and Food (LEAF) research unit, which are financed by national funds from Funda{\cc}{\~a}o para a Ci{\^e}ncia e Tecnologia/Minist{\'e}rio da Educa{\cc}{\~a}o e Ci{\^e}ncia (UID/Multi/04378/2013 and Pest-OE/AGR/UI0245/2013) and cofinanced by the European Regional Development Fund under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007728). C.E. acknowledges Funda{\cc}{\~a}o para a Ci{\^e}ncia e a Tecnologia, Portugal, for the PhD grant SFRH/BD/113112/2015.",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.mtbio.2019.100002",
language = "English",
volume = "1",
journal = "Materials TodayBIO",
issn = "2590-0064",

}

TY - JOUR

T1 - Effect of film thickness in gelatin hybrid gels for artificial olfaction

AU - Esteves, Carina

AU - Santos, Gonçalo M. C.

AU - Alves, Cláudia

AU - Palma, Susana I. C. J.

AU - Porteira, Ana R.

AU - Filho, João

AU - Costa, Henrique M. A.

AU - Alves, Vitor D.

AU - Morais Faustino, Bruno M.

AU - Ferreira, Isabel

AU - Gamboa, Hugo

AU - Roque, Ana C. A.

N1 - This work was supported by the European Research Council through the grant references SCENT-ERC-2014-STG-639123 and CapTherPV-ERC-2014-CoG-647596 and by the Unidade de Ciências Biomoleculares Aplicadas (UCIBIO) and Linking Landscape, Environment, Agriculture and Food (LEAF) research unit, which are financed by national funds from Fundação para a Ciência e Tecnologia/Ministério da Educação e Ciência (UID/Multi/04378/2013 and Pest-OE/AGR/UI0245/2013) and cofinanced by the European Regional Development Fund under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007728). C.E. acknowledges Fundação para a Ciência e a Tecnologia, Portugal, for the PhD grant SFRH/BD/113112/2015.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

AB - Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

KW - Gelatin

KW - Ionic liquid

KW - Liquid crystal

KW - Gas sensor

KW - Electronic nose

KW - Machine learning

U2 - 10.1016/j.mtbio.2019.100002

DO - 10.1016/j.mtbio.2019.100002

M3 - Article

VL - 1

JO - Materials TodayBIO

JF - Materials TodayBIO

SN - 2590-0064

M1 - 100002

ER -