基于金属氧化物半导体气体传感器和紧凑型比色传感器的混合电子鼻系统

Aung Khant Maw, P. Somboon, W. Srituravanich, A. Teeramongkonrasmee
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引用次数: 1

摘要

近年来,商用金属氧化物半导体(MOS)气体传感器作为电子鼻的检测单元被广泛应用于包括疾病诊断在内的各种应用中。然而,由于其选择性差,使用MOS传感器的鼻子只能区分有限数量的气味组。初步研究表明,MOS传感器的选择性可以通过与QCM或电位传感器等其他传感单元的联合集成来提高,但这涉及到复杂的接口电路和测量程序。相比之下,本文提出了一种混合电子鼻,它将来自MOS传感器阵列的嗅觉信息与紧凑的纸质比色传感器阵列结合在一起,更简单,更容易使用。该系统共采用8个MOS传感器,紧凑的纸基比色传感器由苯酚红、甲基红和亚甲基蓝等指示染料制成。使用usb显微镜捕获基于纸张的传感器的颜色配置文件,并监测气体暴露期间染料的变化。通过比较加比色传感器和不加比色传感器对6种挥发性有机化合物(VOC)的分类效果,研究了该系统对VOC分类性能的改善。利用主成分分析(PCA)将两个传感器阵列的测量数据映射到特征空间中进行模式提取。结果表明,基于这两种传感器阵列的数据融合可以改善目标VOCs之间的模式分离。该混合电子鼻系统可用于提高VOC分类性能。
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A Hybrid E-nose System based on Metal Oxide Semiconductor Gas Sensors and Compact Colorimetric Sensors
Commercial metal-oxide semiconductor (MOS) gas sensors have been widely used by recent studies as detection units of electronic noses (e-nose) in various applications including disease diagnosis. However, the enoses employing the MOS sensors can only discriminate a limited number of odor groups due to their poor selectivity. Preliminary studies have shown that the selectivity of the MOS sensors can be enhanced by jointly integrating with other sensory units such as QCM or potentiometric sensors, which, however, involves complex interface circuitry and measurement procedures. In contrast, this paper presents a hybrid electronic nose that combines olfactory information from an MOS sensor array together with a compact paperbased colorimetric sensor array which is simpler and easier to utilize. The proposed system employs total 8 MOS sensors, and the compact paper-based colorimetric sensors are fabricated with indicator dyes such as phenol red, methyl red, and methylene blue. Color profiles of the paper-based sensors are captured using a USB-microscope and the alterations of the dyes during the gas exposure are monitored. The improvement of the system performance in classifying six volatile organic compounds (VOC) are investigated by comparing the classification results of the system with and without the colorimetric sensors. The measurement data from both sensor arrays are mapped to the feature space using principal component analysis (PCA) for pattern extraction. It was confirmed that pattern separation among the target VOCs could be improved based on data fusion of these two sensor arrays. This hybrid e-nose system may be useful for improvement of VOC classification performance.
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