Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 111
IDENTIFICATION OF SOLVENT VAPORS USING NEURAL NETWORK COUPLED SIO
2
RESONATOR ARRAY
Muhammad Rivai
1,2
, Ami Suwandi JS
1
, and Mauridhi Hery Purnomo
2
1
Faculty of Science-Postgraduate Program, Airlangga University, Surabaya, Indonesia
2
Electrical Engineering Department, Institut Teknologi Sepuluh Nopember, Kampus ITS, Keputih, Sukolilo, Surabaya, Indonesia 60111
e-mails: muhammad_rivaiee.its.ac.id, muhammad_rivaiyahoo.com
ABSTRACT
To identify and classify vapors, an array of vapor- sensing elements has been constructed with each
element containing the same crystalline SiO
2
resonator but different polymer as sorption material. The
differing gas-solid partitioning coefficients for the polymers of the sensor array produce a pattern of
frequency changes that can be used to classify vapors by back propagation neural network pattern
recognition. This type of sensor has been shown to resolve common organic solvents, including
molecules of different classes such as aromatic from alcohols as well as those within a particular class e.g.
benzene from toluene and methanol from ethanol.
Keywords: solvent vapor, SiO
2
resonator, neural network.
1. INTRODUCTION
Solvents are volatile organic compounds VOC that often exist as vapors or liquids at room
temperature. The main sources of exposure to solvent vapors are consumer products e.g. room air
deodorants, cosmetics, building materials e.g. paints, adhesives, smoking, chemical plants, petroleum
refineries, and automobiles. Short-term health effects e.g. headache and eye irritation appear to occur in
buildings, particularly new or renovated ones. Long- term health effects may include cancer or human
carcinogen e.g. benzene. It is therefore essential to develop an evaluation method for identification of
solvent vapors.
Conventional approach to chemical sensors has traditionally made use of a ‘lock and key’ design,
wherein specific receptor is synthesized in order to bind strongly and highly selectively to the analyte of
interest. With the approach, selectivity is achieved through precise chemical design of receptor site and
requires the synthesis of a separate, highly selective sensor for each analyte to be detected. An alternative
approach to chemical sensing is closer conceptually to a design widely proposed for the mammalian sense of
olfaction. In such approach, the ‘lock and key’ criterion is abandoned. Instead, an array of different
sensors is used to respond to a number of different chemical vapors. Although in this design,
identification of an analyte cannot be accomplished from the response of a single sensor element, a distinct
pattern of responses produced over the collection of sensors in the array could provide a fingerprint that
would allow classification and identification of the analytes.
Many investigators have tried the vapor identification utilizing various kinds of sensors
including metal oxide semiconductor [1], polymer [2], and quartz resonator [3-7] with satisfactory results.
Authors developed an artificial olfactory using a polymer coated crystalline SiO
2
resonator array and neural network pattern recognition. The results of
identification of solvent vapors are reported here.
2. EXPERIMENTAL
The apparatus in Fig 1 consisted of sensor array coupled with neural network algorithm. Three element
sensors were AT-cut crystalline SiO
2
with fundamental resonance frequency of about 10 MHz.
The resonance frequency decreases when vapor molecules are adsorbed onto the sensor surface, and
the frequency recovers after desorption. The frequency change
∆F is given by
A ∆M
xFx 6
2.3x10 ∆F −
=
1
where F is the fundamental resonant frequency MHz,
∆M total mass of adsorbed vapor molecules g and A the electrode area cm
2
. Three sensors were coated by stationary phases of gas chromatography,
i.e. OV-101, OV-17 and PEG-1540 to increase the adsorption of solvent vapors, due to their geometry
shape and molecular polarities represented by Mc Reynolds constants.
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
