Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance
Abstract: 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.
Author: Muhammad Rivai, Djoko
Purwanto, Hendro Juwono, Hari Agus Sujono
Journal Code: jptkomputergg110040
