Fernandes Daniel L A, Gomes M Teresa S R
CESAM & Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal.
Talanta. 2008 Oct 19;77(1):77-83. doi: 10.1016/j.talanta.2008.05.042. Epub 2008 Jun 5.
A new electronic nose was developed to identify the chemical compound released when a 2.5-L flask was broken inside a 3 m x 3 m x 2.5 m store-room. Flasks of 10 different hazardous compounds were initially present in the room: ammonia, propanone, hexane, acetic acid, toluene, methanol, tetrachloromethane, chloroform, ethanol and dichloromethane. Besides identification, quantification of the compound present in the air was also performed by the electronic nose, in order to evaluate the risk level for room cleaning. An array of six sensors based on coated piezoelectric quartz crystals was used. Although none of the individual sensors was specific for a single compound, an artificial neural network made it possible to identify and quantify the released vapour, among a series of 10 compounds, with six sensors. The neural network could be simplified, and the number of neurons reduced, provided it was used just for the identification task. Quantification could be performed later using the individual calibration of the sensor most sensitive to the identified compound.
研发了一种新型电子鼻,用于识别在一个3米×3米×2.5米的储藏室内打破一个2.5升烧瓶时释放出的化合物。室内最初存有10种不同的危险化合物烧瓶:氨、丙酮、己烷、乙酸、甲苯、甲醇、四氯化碳、氯仿、乙醇和二氯甲烷。除了识别之外,电子鼻还对空气中存在的化合物进行了定量分析,以评估房间清洁的风险水平。使用了一个基于涂覆压电石英晶体的六个传感器阵列。尽管单个传感器都不能对单一化合物具有特异性,但人工神经网络使得用六个传感器在一系列10种化合物中识别和定量释放的蒸汽成为可能。如果仅将神经网络用于识别任务,那么它可以被简化,神经元数量也可以减少。稍后可以使用对已识别化合物最敏感的传感器的单独校准来进行定量分析。