Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.
Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks.
复制标题
DOI：
10.3390/s18020519
复制
发表时间：
2018-02-08
期刊：
影响因子：
--
通讯作者：
Gębicki J
中科院分区：
文献类型：
--
作者：
Szulczyński B;Armiński K;Namieśnik J;Gębicki J
本文介绍了一个由8个传感器组成的电子鼻原型，其中5个tgs型传感器，2个电化学传感器和1个pid型传感器，用于识别二、三、四、五组分气味混合物中的气味相互作用现象。对城市垃圾填埋场和污水处理厂附近存在的典型化合物，如甲苯、丙酮、三乙胺、α-蒎烯和正丁醇进行了调查。利用激活函数tanh和参数Leaky整流线性单元（Leaky ReLUs）选定的人工神经网络结构对预测的气味强度和享乐音调进行评估。根据电子鼻仪器的结果和非线性数据分析，确定了混合气味中气味相互作用识别的正确性。在气味强度的情况下，这个值（平均值）处于88%的水平，而在快乐音调的情况下，这个平均值处于74%的水平。在这两种情况下，识别的正确性取决于气味混合物中存在的成分的数量。
This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter . Correctness of identification of odour interactions in the odorous mixtures was determined based on the results obtained with the electronic nose instrument and non-linear data analysis. This value (average) was at the level of 88% in the case of odour intensity, whereas the average was at the level of 74% in the case of hedonic tone. In both cases, correctness of identification depended on the number of components present in the odorous mixture.
登录
查看更多内容
影响因子：
3.5
作者：
LAFFORT, P;DRAVNIEKS, A
通讯作者：
DRAVNIEKS, A
DOI：
10.1007/s10470-011-9638-1
发表时间：
2012-01-01
影响因子：
1.4
作者：
Kim, Hyuntae;Konnanath, Bharatan;Bakkaloglu, Bertan
通讯作者：
Bakkaloglu, Bertan
影响因子：
4.2
作者：
Pearce, TC;Gardner, JW
通讯作者：
Gardner, JW
影响因子：
5.5
作者：
Hong, Xuezhen;Wang, Jun
通讯作者：
Wang, Jun
影响因子：
3.3
作者：
Amoore, John E.;Hautala, Earl
通讯作者：
Hautala, Earl