An optimized e-nose for efficient volatile sensing and discrimination

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Downloads (Pure)

Abstract

Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.

Original languageEnglish
Title of host publicationBIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
EditorsAna Roque, Ana Fred, Hugo Gamboa
PublisherSciTePress
Pages36-46
Number of pages11
ISBN (Electronic)9789897583537
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Biomedical Electronics and Devices, BIODEVICES 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019 - Prague, Czech Republic
Duration: 22 Feb 201924 Feb 2019

Conference

Conference12th International Conference on Biomedical Electronics and Devices, BIODEVICES 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
CountryCzech Republic
CityPrague
Period22/02/1924/02/19

Fingerprint

Volatile organic compounds
Biomaterials
Food safety
Beverages
Cosmetics
Curve fitting
Environmental protection
Gases
Support vector machines
Learning systems
Feature extraction
Data acquisition
Classifiers
Optical properties
Control systems
Sensors
Industry
Electronic nose

Keywords

  • Biomaterials
  • Electronic Nose
  • Machine Learning
  • Volatile Organic Compounds

Cite this

Santos, G., Alves, C., Pádua, A. C., Palma, S., Gamboa, H., & Roque, A. C. (2019). An optimized e-nose for efficient volatile sensing and discrimination. In A. Roque, A. Fred, & H. Gamboa (Eds.), BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019 (pp. 36-46). SciTePress.
Santos, Gonçalo ; Alves, Cláudia ; Pádua, Ana Carolina ; Palma, Susana ; Gamboa, Hugo ; Roque, Ana Cecília. / An optimized e-nose for efficient volatile sensing and discrimination. BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. editor / Ana Roque ; Ana Fred ; Hugo Gamboa. SciTePress, 2019. pp. 36-46
@inproceedings{a49534596faa4a6f888be09f4ae1a7e6,
title = "An optimized e-nose for efficient volatile sensing and discrimination",
abstract = "Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6{\%} (± 0.9{\%}) accuracy.",
keywords = "Biomaterials, Electronic Nose, Machine Learning, Volatile Organic Compounds",
author = "Gon{\cc}alo Santos and Cl{\'a}udia Alves and P{\'a}dua, {Ana Carolina} and Susana Palma and Hugo Gamboa and Roque, {Ana Cec{\'i}lia}",
note = "info:eu-repo/grantAgreement/FCT/5876/147258/PT# SCENT-ERC-2014-STG-639123. POCI-01-0145-FEDER-007728. PD/BD/105752/2014.",
year = "2019",
month = "1",
day = "1",
language = "English",
pages = "36--46",
editor = "Ana Roque and Ana Fred and Hugo Gamboa",
booktitle = "BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019",
publisher = "SciTePress",

}

Santos, G, Alves, C, Pádua, AC, Palma, S, Gamboa, H & Roque, AC 2019, An optimized e-nose for efficient volatile sensing and discrimination. in A Roque, A Fred & H Gamboa (eds), BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. SciTePress, pp. 36-46, 12th International Conference on Biomedical Electronics and Devices, BIODEVICES 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019, Prague, Czech Republic, 22/02/19.

An optimized e-nose for efficient volatile sensing and discrimination. / Santos, Gonçalo; Alves, Cláudia; Pádua, Ana Carolina; Palma, Susana; Gamboa, Hugo; Roque, Ana Cecília.

BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. ed. / Ana Roque; Ana Fred; Hugo Gamboa. SciTePress, 2019. p. 36-46.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An optimized e-nose for efficient volatile sensing and discrimination

AU - Santos, Gonçalo

AU - Alves, Cláudia

AU - Pádua, Ana Carolina

AU - Palma, Susana

AU - Gamboa, Hugo

AU - Roque, Ana Cecília

N1 - info:eu-repo/grantAgreement/FCT/5876/147258/PT# SCENT-ERC-2014-STG-639123. POCI-01-0145-FEDER-007728. PD/BD/105752/2014.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.

AB - Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.

KW - Biomaterials

KW - Electronic Nose

KW - Machine Learning

KW - Volatile Organic Compounds

UR - http://www.scopus.com/inward/record.url?scp=85064664332&partnerID=8YFLogxK

M3 - Conference contribution

SP - 36

EP - 46

BT - BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

A2 - Roque, Ana

A2 - Fred, Ana

A2 - Gamboa, Hugo

PB - SciTePress

ER -

Santos G, Alves C, Pádua AC, Palma S, Gamboa H, Roque AC. An optimized e-nose for efficient volatile sensing and discrimination. In Roque A, Fred A, Gamboa H, editors, BIODEVICES 2019 - 12th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. SciTePress. 2019. p. 36-46