Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques
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...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/1/114 |
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| author | Tiziano Zarra Mark Gino K. Galang Florencio C. Ballesteros Vincenzo Belgiorno Vincenzo Naddeo |
| author_facet | Tiziano Zarra Mark Gino K. Galang Florencio C. Ballesteros Vincenzo Belgiorno Vincenzo Naddeo |
| author_sort | Tiziano Zarra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:44:29Z |
| format | Article |
| id | doaj.art-55021997f693453390af1bd6a9c27288 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:44:29Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-55021997f693453390af1bd6a9c272882023-11-21T02:42:45ZengMDPI AGSensors1424-82202020-12-0121111410.3390/s21010114Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction TechniquesTiziano Zarra0Mark Gino K. Galang1Florencio C. Ballesteros2Vincenzo Belgiorno3Vincenzo Naddeo4Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), ItalyEnvironmental Engineering Program, University of the Philippines, Diliman, Quezon City 1101, PhilippinesEnvironmental Engineering Program, University of the Philippines, Diliman, Quezon City 1101, PhilippinesSanitary Environmental Engineering Division (SEED), Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), ItalySanitary Environmental Engineering Division (SEED), Department of Civil Engineering, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), ItalyInstrumental 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.https://www.mdpi.com/1424-8220/21/1/114artificial neural networkdata extractionelectronic noselinear discriminant analysisodour classification monitoring model |
| spellingShingle | Tiziano Zarra Mark Gino K. Galang Florencio C. Ballesteros Vincenzo Belgiorno Vincenzo Naddeo Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques Sensors artificial neural network data extraction electronic nose linear discriminant analysis odour classification monitoring model |
| title | Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques |
| title_full | Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques |
| title_fullStr | Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques |
| title_full_unstemmed | Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques |
| title_short | Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques |
| title_sort | instrumental odour monitoring system classification performance optimization by analysis of different pattern recognition and feature extraction techniques |
| topic | artificial neural network data extraction electronic nose linear discriminant analysis odour classification monitoring model |
| url | https://www.mdpi.com/1424-8220/21/1/114 |
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