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Open AccessArticle

Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

1
Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
2
Environmental Engineering Program, University of the Philippines, Diliman, Quezon City 1101, Philippines
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(1), 114; https://doi.org/10.3390/s21010114
Received: 24 November 2020 / Revised: 14 December 2020 / Accepted: 24 December 2020 / Published: 27 December 2020
(This article belongs to the Special Issue Electronic Noses)
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS. View Full-Text
Keywords: artificial neural network; data extraction; electronic nose; linear discriminant analysis; odour classification monitoring model artificial neural network; data extraction; electronic nose; linear discriminant analysis; odour classification monitoring model
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MDPI and ACS Style

Zarra, T.; Galang, M.G.K.; Ballesteros, F.C., Jr.; Belgiorno, V.; Naddeo, V. Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques. Sensors 2021, 21, 114. https://doi.org/10.3390/s21010114

AMA Style

Zarra T, Galang MGK, Ballesteros FC Jr., Belgiorno V, Naddeo V. Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques. Sensors. 2021; 21(1):114. https://doi.org/10.3390/s21010114

Chicago/Turabian Style

Zarra, Tiziano; Galang, Mark G.K.; Ballesteros, Florencio C., Jr.; Belgiorno, Vincenzo; Naddeo, Vincenzo. 2021. "Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques" Sensors 21, no. 1: 114. https://doi.org/10.3390/s21010114

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