Tarttelin Hernández Paula, Hailes Stephen M V, Parkin Ivan P
Department of Health & Life Sciences Alison Gingell Building, Whitefriars St Coventry CV1 5FB UK
Department of Computer Science, University College of London 66-72 Gower Street London WC1E 6BT UK.
RSC Adv. 2020 Aug 4;10(47):28464-28477. doi: 10.1039/d0ra03687k. eCollection 2020 Jul 27.
A range of n-type and p-type metal oxide semiconductor gas sensors based on SnO and CrO materials have been modified with zeolites H-ZSM-5, Na-A and H-Y to create a gas sensor array able to successfully detect a cocaine by-product, methyl benzoate, which is commonly targeted by detection dogs. Exposure to vapours was carried out with eleven sensors. Upon data analysis, four of these that offered promising qualities for detection were subsequently selected to understand whether machine learning methods would enable successful and accurate classification of gases. The capability of discrimination of the four sensor array was assessed against nine different vapours of interest; methyl benzoate, ethane, ethanol, nitrogen dioxide, ammonia, acetone, propane, butane, and toluene. When using the polykernel function ( = 200) in the Weka software - and just five seconds into the gas injection - the model was 94.1% accurate in successfully classifying the data. Although further work is necessary to bring the sensors to a standard of detection that is competitive with that of dogs, these results are very encouraging because they show the potential of metal oxide semiconductor sensors to rapidly detect a cocaine by-product in an inexpensive way.
一系列基于SnO和CrO材料的n型和p型金属氧化物半导体气体传感器已用沸石H-ZSM-5、Na-A和H-Y进行了改性，以创建一个能够成功检测可卡因副产物苯甲酸甲酯的气体传感器阵列，苯甲酸甲酯是缉毒犬通常追踪的目标。使用11个传感器进行了蒸汽暴露实验。数据分析后，选择了其中四个具有良好检测性能的传感器，以了解机器学习方法是否能够成功且准确地对气体进行分类。评估了该四传感器阵列对九种不同目标蒸汽的辨别能力；这些蒸汽包括苯甲酸甲酯、乙烷、乙醇、二氧化氮、氨、丙酮、丙烷、丁烷和甲苯。在Weka软件中使用多核函数（=200）时，在注入气体仅五秒后，该模型对数据成功分类的准确率就达到了94.1%。尽管要使这些传感器达到与缉毒犬相媲美的检测标准还需要进一步开展工作，但这些结果非常令人鼓舞，因为它们显示了金属氧化物半导体传感器以低成本快速检测可卡因副产物的潜力。
