Announcement
Starting on July 4, 2018 the Indonesian Publication Index (IPI) has been acquired by the Ministry of Research Technology and Higher Education (RISTEKDIKTI) called GARUDA Garba Rujukan Digital (http://garuda.ristekdikti.go.id)
For further information email to portalgaruda@gmail.com

Thank you
Logo IPI  
Journal > TELKOMNIKA Telecommunication, Computing, Electronics and Control > Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance

 

Full Text PDF (798 kb)
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol 9, No 2: August 2011
Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance
Rivai, Muhammad ( Institute of Technology Sepuluh Nopember)
Purwanto, Djoko ( Institute of Technology Sepuluh Nopember)
Juwono, Hendro ( Institute of Technology Sepuluh Nopember)
Agus Sujono, Hari ( Institute of Technology Sepuluh Nopember)
Article Info   ABSTRACT
Published date:
29 Apr 2013
 
The conventional electronic nose usually consists of an array of dissimilar chemical sensors such as quartz crystal microbalance (QCM) combined with pattern recognition algorithm such as Neural network. Because of parallel processing, the system needs a huge number of sensors and circuits which may emerge complexity and inter-channel crosstalk problems. In this research, a new type of odor identification which combines between gas chromatography (GC) and electronic nose methods has been developed. The system consists of a GC column and a 10-MHz quartz crystal microbalance sensor producing a unique pattern for an odor in time domain. This method offers advantages of substantially reduced size, interferences and power consumption in comparison to existing odor identification system. Several odors of organic compounds were introduced to evaluate the selectivity of the system. Principle component analysis method was used to visualize the classification of each odor in two-dimensional space. This system could resolve common organic solvents, including molecules of different classes (aromatic from alcohols) as well as those within a particular class (methanol from ethanol) and also fuels (premium from pertamax). The neural network can be taught to recognize the odors tested in the experiment with identification rate of 85 %. It is therefore the system may take the place of human nose, especially for poisonous odor evaluations.
Copyrights © 2013