Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 112
NORM ∆F
N
2
Temperature controlled water bath
Flowmeter Valve
acetone benzene
Output layer Hidden layer
Input layer Exhaust
Sensor array Injection
hole Oscillators
methanol
Figure 1. Experimental apparatus.
The sensors were mounted inside a 0.6 L chamber and connected to 5V-CMOS oscillator circuits. To
maximize isolation among the sensors, each oscillator was separately battery powered. The oscillator
frequencies were measured by 32-bit counter circuits and transferred through 89S51 microcontroller to
Personal Computer via RS232. Before sample injection, chamber was flowed by 99.999 N
2
at 1 Lmin
-1
until the frequencies became stable. Seven common organic solvents i.e. benzene, toluene,
chloroform, diethyl ether, ethanol, methanol, and acetone were tested to the apparatus. The sensor
signals were normalized by
∑
= 3
1 j
j i
X X
, where X
i
is the signal of the i-sensor. The normalized values were
fed to the neural network pattern recognition. The three-layer neural network can be taught to recognize
vapors automatically using the back propagation algorithm. The number of input nodes was three
correspond to the number of sensors, and the number of output neurons was seven equal to that of the sort of
solvent. The number of hidden neurons was nine to accelerate and improve the convergence in training
phase. Both training rate and moment constant were empirically determined to be 0.01 to produce the
lowest value of mean squared error for the single hidden layer network [8].
3. RESULT AND DISCUSSION
A few micro liters of a sample solution was injected to the chamber maintained at the temperature
of 32 °C. Vapors are adsorbed in polymer giving rise
to decrease in frequency. Each sensor gave different frequency change i.e. differential value between
before and after injection of sample varied in the audio spectrum depending on the coating materials
and the sorts of vapor. Fig. 2 shows time response of the frequency change of each sensor element exposed
to methanol vapor. As the frequencies become stable, the resonator coated by Ov-101, OV-17 and PEG-
1540 gave the frequency changes of -33.5
±5.2, - 26.6
±1.4, and -101.7±1.3 hertz, respectively, or 0.207, 0.164, 0.628 in normalized values. While N
2
was flowed to the chamber, the frequency of each resonator returned to that of before the sample
injection. This phenomenon describes that the polymer plays role as a perfect adsorption-desorption
mechanism.
Figure 2. Frequency changes of the sensor array exposed to methanol vapor. Liquid sample was injected at 10
th
s and N
2
was injected at 1000
th
s.
The experiment was performed six times each sample and Fig. 3 shows the average of normalized
frequency changes of three kinds of polymer coated crystalline SiO
2
resonator tested by seven sorts of solvent vapor. The histogram clearly displays that the
array gave a unique pattern for each vapor.
Identification of Solvent Vapors Using Neural Network Coupled SIO
2
, Resonator Array – Muhammad Rivai, Ami Suwandi, Mauridhi Hery Purnomo
ISSN 1858-1633 2005 ICTS 113
0.2 0.4
0.6 0.8
Normalized Frequency Change OV-101
OV-17 PEG-1540
acetone methanol
ethanol diethyl ether
chloroform toluene
benzene
Figure 3. Normalized frequency change of 3 sensor elements exposed to 7 vapors.
In training phase, 42 data patterns were transferred to the input layer of neural network. The results shown
in Fig. 4 were obtained after the network was trained by 10
6
epochs with a mean squared error of 1.57 . The activation values of neurons in output layer are
shown in Fig. 4. In running phase, other 21 data patterns were
tested to the trained neural network and the activations of neuron in output layer are shown in Fig. 5. The
histogram clearly shows not only that, the neural network can readily distinguish non-polar from polar
solvents e.g. benzene or toluene from methanol or acetone but also illustrates that such network can
readily distinguish members of a related class of materials e.g. methanol from ethanol or benzene from
toluene. It should be noted that that the apparatus was not designed a priori to have specific responses to any
particular vapor or class of vapors, yet the array could nevertheless separate a board range of chemical
species having relatively subtle differences in their chemicalphysical properties.
0.2 0.4
0.6 0.8
1 1.2
Neuron Activations of the Output Layer
benzene toluene
chloroform diethyl ether
ethanol methanol
acetone acetone
methanol ethanol
diethyl ether chloroform
toluene benzene
Figure 4. Neuron activations of output layer after training phase.
0.2 0.4
0.6 0.8
1 1.2
Neuron Activations of the Output Layer
benzene toluene
chloroform diethyl ether
ethanol methanol
acetone acetone
methanol ethanol
diethyl ether chloroform
toluene benzene
Figure 5. Neuron activations of output layer after running phase
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 114
4. CONCLUSION
