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Choice of a suitable e-nose output variable for the continuous monitoring of an odour in the environment

机译:选择合适的电子鼻输出变量,用于连续监测环境中的气味

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摘要

An array of tin-oxide sensors is used to continuously monitor different odour emissions in the environment. The paper presents some issues aiming at improving the portability and the user-friendliness of the instrument as well as testing what kind of signal may be used to monitor the odour "intensity".Main results are the following.The test of various pre-processing data algorithms pointed out that the use of pure reference air could be avoided, as long as the sensors are allowed to periodically regenerate in the presence of ambient air.Sensor array in static contact with ambient air could be sufficient for the on-line monitoring, but the use of a controlled gas flow system to transfer the odour from the source is better to avoid the influence of air movement on the heated sensors.The control of the temperature and the humidity of the gas and the thermo-regulation of the sensor chamber don't seem essential, even for outdoor operation.When trying to build a regression model linking the odour intensity to the sensor signals, Partial Least Square (PLS) model gives better results with respect to Multi Linear Regression or Principal Component regression models. For an application around a landfill area, where sensor signals are compared to the personal feeling of the operator in the field, PLS gives a percentage of 71% correct intensity prediction.
机译:一系列氧化锡传感器用于连续监测环境中不同的气味排放。本文提出了一些问题,旨在改善仪器的便携性和用户友好性,以及测试可以使用哪种信号来监测气味“强度”。主要结果如下。各种预处理的测试数据算法指出,只要允许传感器在环境空气存在下定期再生,就可以避免使用纯参考空气。与环境空气静态接触的传感器阵列对于在线监测就足够了,但是使用受控的气流系统从源头转移气味更好地避免了空气移动对加热的传感器的影响。气体温度和湿度的控制以及传感器室的温度调节即使试图在室外操作,它也不是必需的。当尝试建立将气味强度与传感器信号联系起来的回归模型时,偏最小二乘(PLS)模型在多元线性回归或主成分回归模型。对于垃圾填埋场附近的应用,传感器信号与现场操作员的个人感觉进行了比较,PLS给出了71%的正确强度预测百分比。

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