Szulczyński Bartosz, Armiński Krzysztof, Namieśnik Jacek, Gębicki Jacek
Department of Chemical and Process Engineering, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G. Narutowicza Str., 80-233 Gdańsk, Poland.
Department of Control Engineering, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 11/12 G. Narutowicza Str., 80-233 Gdańsk, Poland.
Sensors (Basel). 2018 Feb 8;18(2):519. doi: 10.3390/s18020519.
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 a = 0.03 . 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.
本文介绍了一种由八个传感器组成的电子鼻原型的应用，其中包括五个TGS型传感器、两个电化学传感器和一个PID型传感器，用于识别二元、三元、四元和五元有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研究。使用具有双曲正切激活函数和参数a = 0.03的泄漏整流线性单元（Leaky ReLUs）的选定人工神经网络结构，对预测的气味强度和享乐色调进行了评估。基于电子鼻仪器获得的结果和非线性数据分析，确定了有气味混合物中气味相互作用识别的正确性。在气味强度方面，该值（平均值）为88%，而在享乐色调方面，平均值为74%。在这两种情况下，识别的正确性取决于有气味混合物中存在的成分数量。
