Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines
Authors:
- Juan C. Rodriguez GamboaJGJuan C. Rodriguez Gamboa
- Adenilton J. da SilvaASAdenilton J. da Silva
- Ismael C. S. AraujoIAIsmael C. S. Araujo
- Eva Susana Albarracin E.EEEva Susana Albarracin E.
- Cristhian M. Duran A.Cristhian M. Duran A.
- 1 January 2021
- journal article
- research article
- Published by Elsevier in Sensors and Actuators B: Chemical
- Vol. 327
Abstract
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Funding Information
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
This publication has 11 references indexed in Scilit:
- Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acidLWT, 2019
- Recognition and sensing of organic compounds using analytical methods, chemical sensors, and pattern recognition approachesChemometrics and Intelligent Laboratory Systems, 2019
- Development of a LeNet-5 Gas Identification CNN Structure for Electronic NosesSensors, 2019
- Data Processing for Multiple Electronic Noses Using Sensor Response VisualizationIEEE Sensors Journal, 2018
- Gas Classification Using Deep Convolutional Neural NetworksSensors, 2018
- Calibration transfer and drift counteraction in chemical sensor arrays using Direct StandardizationSensors and Actuators B: Chemical, 2016
- Data set from chemical sensor array exposed to turbulent gas mixturesData in Brief, 2015
- Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass SpectrometrySensors, 2014
- On the performance of gas sensor arrays in open sampling systems using Inhibitory Support Vector MachinesSensors and Actuators B: Chemical, 2013
- Electronic Nose for Quality Control of Colombian Coffee through the Detection of Defects in “Cup Tests”Sensors, 2009