Abstract
An array of quartz crystal sensors modified by chemical materials was implemented for vapor detection. In this study, the vapor sensor array was employed to qualify the fuels. This was done to distinguish some common fuels and also to determine the rate of fuel adulteration. All the measurements were conducted at room temperature. The sensor response consumed only up to one minute for a measurement cycle. After statistical data analysis including Principal Component Analysis and Neural Network methods, it was possible to conclude that the sensor array is able to distinguish the fuel vapors with high reproducibility and to determine the rate of fuel adulteration with linear correlation.
Original language | English |
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Pages (from-to) | 6737-6743 |
Number of pages | 7 |
Journal | ARPN Journal of Engineering and Applied Sciences |
Volume | 10 |
Issue number | 16 |
Publication status | Published - 18 Nov 2015 |
Keywords
- Fuel vapor
- Neural network
- Principal component analysis
- Quartz sensor array