@inproceedings{bd728df568d34b9eb0ffd7b21afdfbb8,
title = "Mixed vapour identification using partition column-QCMs and Artificial Neural Network",
abstract = "This Paper presents the identification of mixed vapour using electronic nose system composed of Quartz Crystal Microbalance (QCM) sensor array and a partition column of gas chromatography. The polymer coated QCMs produced a specific frequency shift. The data set was processed by an Artificial Neural Network using Backpropagation algorithm as a pattern recognition. The result showed that this equipment was able to identify five types of vapours namely benzene, acetone, isopropyl alcohol, non-polar and polar mixture (i.e. benzene and acetone), and also polar and polar mixture (i.e. isopropyl alcohol and acetone) with the identification rate of 96%.",
keywords = "Artificial Neural Network, Chromatography Column, Electronic Nose, Mixed Vapour, QCM Sensor",
author = "Muhammad Rivai and Achmad Arifin and Agustin, {Eva Inaiyah}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016 ; Conference date: 12-10-2016",
year = "2017",
month = apr,
day = "24",
doi = "10.1109/ICTS.2016.7910294",
language = "English",
series = "Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "172--177",
booktitle = "Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016",
address = "United States",
}