Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques.
Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques.
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DOI：
10.3390/s21010114
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发表时间：
2020-12-27
期刊：
影响因子：
--
通讯作者：
Naddeo V
中科院分区：
文献类型：
--
作者：
Zarra T;Galang MGK;Ballesteros FC Jr;Belgiorno V;Naddeo V
仪器气味监测系统（IOMS）是智能电子传感工具，其主要应用是产生气味指标，即人类观察者所感知的气味指标。气味传感器信号的质量、采集数据的数学处理以及气味度量相关性的验证是控制的关键问题，以确保测量的鲁棒性和可靠性。该研究提出并讨论了在气味分类监测模型（OCMM）的细化和有效性中使用不同的模式识别和特征提取技术。利用线性判别分析（LDA）和人工神经网络（ANN）作为一种模式识别算法，研究了原始响应曲线的上升、中间和峰值时段的影响。使用先进的智能IOMS对复杂工业工厂收集的真实气味样本进行实验室分析。结果表明，方法的选择对OCMM产品质量的影响。与人工神经网络（ANN）结合的高峰期在高分类率的基础上突出了最佳组合。本文为开发优化IOMS性能的解决方案提供了信息。
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.
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发表时间：
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期刊：
Sensors (Basel, Switzerland)
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