# Initial Investigation of Using Planar Interdigital Sensors for Assessment of Quality in Seafood

Authors: A. R. Mohd Syaifudin; K. P. Jayasundera; S. C. Mukhopadhyay
Journal: Journal of Sensors
 (2008)

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## Abstract

A planar interdigital sensor-based sensing system has been fabricated for assessment of seafood quality. Our main objective is to sense the molecule of domoic acid presence in mussels. Three peptide derivatives namely sarcosine, proline, and hydroxylproline were used for the initial studies which are structurally closely related to our target molecule. The proline molecule is arguably the most important amino acid in peptide conformation, containing the basic structural similarity to the domoic acid. Three novel interdigital sensors have been designed and fabricated. The initial result shows that sensors respond very well to the chemicals and it is possible to discriminate the different chemicals from the output of the sensor. Results from the analysis have shown that Sensor_1 has better sensitivity compared to other sensors. Sensor_1 was chosen for further analysis with real mussels. The changes in sensor impedance were then analysed with real mussels before and after adding the proline. The presence of proline on the mussel samples was detected by the sensor. Results also showed a good correlation ofR2 = 0.717 between sensitivity and sample thickness.


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## Body

## 1. Introduction

In late 19th and early 20th century, a large number of
illnesses were linked  with the consumption of raw oysters, claws,  and mussels.
The problem still exists today  and time to time it appears in the society.
There were some incident of deaths in recent times which has made this  problem
is worthy of research. It was found that these illnesses were related to the
ingestion of domoic acid-contaminated mussels which led to amnesic shellfish
poisoning (ASP)
[1–4]. ASP is
characterized according to both gastrointestinal and neurological symptoms,
including severe headache, confusion, and either temporary or permanent memory
loss.Domoic acid (DA) is a naturally
occurring toxin produced by microscopic algae, specifically the diatom speciesPseudo-nitzschia. DA is a chemical that is produced by algae or plankton when it blooms. Shellfish
and filter-feeding fish (anchovies, sardines, krill, etc.) ingest these algae,
where the toxin concentrates. Both can accumulate this toxin without
apparent ill effects. However, in marine mammals and humans, DA is ticarboxylic
acid that acts as a neurotoxin. The toxin is not destroyed by cooking or
freezing. Figure 1 shows the chemical structure of domoic acid.Figure 1
Chemical structure
of domoic acid.The presence of DA in shellfish has
been reported in various regions of the world [1]. There have been numerous reports of toxicity in a variety
of wildlife species indicating that DA moves up the food chain in marine
ecosystems
[1–4].Studies have proven that certain amount of DA can cause health problems to animals and humans
[5–10]. In 1987, DA was identified as the toxin responsible for an outbreak of illness in Prince Edward Island, Canada [11]. It was caused by eating blue mussels. Effects on both the gastrointestinal tract and  the nervous system were observed. It was reported that 107 patients (all adults) met the case definition [2]. Dose-related symptoms included nausea, vomiting, abdominal cramps, diarrhoea, headache, memory loss, and convulsions, and several deaths were attributed to the toxin. As a result of the episode of human illness in Canada, a regulatory level of 20 μg/g of DA in shellfish meat was established.In order to
protect consumers from shellfish poisoning, most countries have a set of
regulatory guidelines. All the products need to be tested for the toxin, which
places a heavy workload on the laboratories and is extremely expensive to the
industry. This  has motivated us toward the development of a sensing system
which can detect the presence of DA and their related analogs without much
difficulty and at a low cost.


## 2. Existing Method

The existing method to detect DA is either based on using HPLC or SPR.


### 2.1. Based on HPLC

High-pressure liquid chromatography (HPLC) has been used to determine DA
[2, 11–13]. Ten samples of commercial blue mussels (Mytilus edulis) from Japan
were analyzed for DA. DA was found in all samples at low concentrations (0.11–1.81 ng/g mussel
tissue) [14]. A
new sensitive determination method of DA using high-performance liquid chromatography
with electrogenerated tris has been reported [15].


### 2.2. Based on SPR

A portable surface plasmon resonance (SPR) biosensor system has been
developed for the detection of domoic acid [16]. A
rapid and sensitive immuno-based screening method was reported to detect DA
present in extracts of shellfish species using a surface plasmon
resonance-based optical biosensor [17].The existing methods required expensive equipments
and also trained personnel. The procedure to prepare the sample and to conduct
the experiment is tedious and time consuming. Looking at the complexity of the existing
methods, we have designed and fabricated novel planar interdigital sensor-based
sensing system with the purpose of an easy detection of molecules for DA. The
developed sensing system should be reliable and cost effective.


## 2.1. Based on HPLC

High-pressure liquid chromatography (HPLC) has been used to determine DA
[2, 11–13]. Ten samples of commercial blue mussels (Mytilus edulis) from Japan
were analyzed for DA. DA was found in all samples at low concentrations (0.11–1.81 ng/g mussel
tissue) [14]. A
new sensitive determination method of DA using high-performance liquid chromatography
with electrogenerated tris has been reported [15].


## 2.2. Based on SPR

A portable surface plasmon resonance (SPR) biosensor system has been
developed for the detection of domoic acid [16]. A
rapid and sensitive immuno-based screening method was reported to detect DA
present in extracts of shellfish species using a surface plasmon
resonance-based optical biosensor [17].The existing methods required expensive equipments
and also trained personnel. The procedure to prepare the sample and to conduct
the experiment is tedious and time consuming. Looking at the complexity of the existing
methods, we have designed and fabricated novel planar interdigital sensor-based
sensing system with the purpose of an easy detection of molecules for DA. The
developed sensing system should be reliable and cost effective.


## 3. Development of Planar Interdigital Sensors for Assessment of Domoic Acid

Planar interdigital sensor
has been used for estimation of properties of dielectric material such as milk,
saxophone reeds, meat, and leather
[18–22]. The electric field lines
generated by the sensor penetrate into the material under test (MUT) and will
change the impedance of the sensor. The sensor behaves as a capacitor in which
the capacitive reactance becomes a function of system properties. Therefore by
measuring the capacitive reactance the system properties can be evaluated. The
capacitance of parallel plate capacitor can be calculated by
(1)C=ε0εrAd, where C  is the capacitance (in farads), ε0 is the absolute permittivity (8.854×10−12F/m),  εr is the relative permittivity of material under test, A  is the area (in square meters), and d  is the spacing (in meters).


### 3.1. Sensor Design

A novel planar
interdigital  sensor, different from conventional interdigital sensor has been
used. Three types of novel interdigital sensors have been designed and fabricated.
The initial goal is to evaluate which of these three sensors give a good response
and could discriminate between three different chemical samples. Each sensor has the same  effective area of 4.75 mm
by 5.00 mm and having pitches (the distance between adjacent positive and
negative electrode) of 0.25 mm. The positive and negative electrodes have the
same length and width of 4.75 mm and 0.125 mm, respectively. All sensors were
designed to have the same effective number of electrodes (thirteen). Sensor_1
was designed to have 2 positive electrodes at each end separated by 11 negative
electrodes. Sensor_2 and Sensor_3 were designed with the same dimensions but
with different  configurations. Sensor_2 has 3 positive electrodes with 10
negative  electrodes while Sensor_3 has 4 positive electrodes with 9 negative
electrodes. Figures2(a) and 2(b) show the representation of conventional and
novel interdigital sensors. Figure 3 shows Sensor_2 and Sensor_3 with different
configurations.Representation of planar interdigital sensors. (a) Conventional interdigital sensor. (b) Novel interdigital sensor (Sensor_1).
(a)(b)Figure 3
Sensor_2 and
Sensor_3 of different configurations.However, (1)
can be used to calculate the capacitance of a planar type capacitor by
introducing two factors, one for change of distance, “d” and another for change
of area, “A”. Only “d” was changed in the design. It is important during the
sensor design stage, to optimise the number of negative electrodes and to estimate
the equivalent capacitance within sensor geometry. The equivalent circuit to
estimate the equivalent capacitance within sensor  geometry is shown in Figure 4.
The equivalent capacitance can be calculated by (2)C12=12C11;…;C1n=1nC11,C21=12C21;…;C2n=1nC21, where C11=C21.The equivalent
circuit of novel sensors. (a) Sensor_1 with configuration 1-11-1. (b) Sensor_2
with configuration 1-5-1-5-1. (c) Sensor_3 with configuration 1-3-1-3-1-3-1.
(a)(b)(c)Figure5 shows the estimation
of capacitance within sensor geometry. Results from circuit analysis have shown
that Sensor_1 has better uniformity compare to Sensor_2 and Sensor_3. This
uniformity is very important for achieving largest sensitivity of measurement. Figure 6 shows the picture of all fabricated sensors.Figure 5
The estimation
of capacitance of each sensor geometry.Figure 6
The
fabricated interdigital sensors.


## 3.1. Sensor Design

A novel planar
interdigital  sensor, different from conventional interdigital sensor has been
used. Three types of novel interdigital sensors have been designed and fabricated.
The initial goal is to evaluate which of these three sensors give a good response
and could discriminate between three different chemical samples. Each sensor has the same  effective area of 4.75 mm
by 5.00 mm and having pitches (the distance between adjacent positive and
negative electrode) of 0.25 mm. The positive and negative electrodes have the
same length and width of 4.75 mm and 0.125 mm, respectively. All sensors were
designed to have the same effective number of electrodes (thirteen). Sensor_1
was designed to have 2 positive electrodes at each end separated by 11 negative
electrodes. Sensor_2 and Sensor_3 were designed with the same dimensions but
with different  configurations. Sensor_2 has 3 positive electrodes with 10
negative  electrodes while Sensor_3 has 4 positive electrodes with 9 negative
electrodes. Figures2(a) and 2(b) show the representation of conventional and
novel interdigital sensors. Figure 3 shows Sensor_2 and Sensor_3 with different
configurations.Representation of planar interdigital sensors. (a) Conventional interdigital sensor. (b) Novel interdigital sensor (Sensor_1).
(a)(b)Figure 3
Sensor_2 and
Sensor_3 of different configurations.However, (1)
can be used to calculate the capacitance of a planar type capacitor by
introducing two factors, one for change of distance, “d” and another for change
of area, “A”. Only “d” was changed in the design. It is important during the
sensor design stage, to optimise the number of negative electrodes and to estimate
the equivalent capacitance within sensor geometry. The equivalent circuit to
estimate the equivalent capacitance within sensor  geometry is shown in Figure 4.
The equivalent capacitance can be calculated by (2)C12=12C11;…;C1n=1nC11,C21=12C21;…;C2n=1nC21, where C11=C21.The equivalent
circuit of novel sensors. (a) Sensor_1 with configuration 1-11-1. (b) Sensor_2
with configuration 1-5-1-5-1. (c) Sensor_3 with configuration 1-3-1-3-1-3-1.
(a)(b)(c)Figure5 shows the estimation
of capacitance within sensor geometry. Results from circuit analysis have shown
that Sensor_1 has better uniformity compare to Sensor_2 and Sensor_3. This
uniformity is very important for achieving largest sensitivity of measurement. Figure 6 shows the picture of all fabricated sensors.Figure 5
The estimation
of capacitance of each sensor geometry.Figure 6
The
fabricated interdigital sensors.


## 4. Results and Discussions

The sensor is connected
with a series surface mount resistor of 120 kΩ to measure the current through the sensor. A
frequency of 10 kHz with 10 Vpp (voltage peak to peak) is applied to the
exciting coil using agilent function waveform generator. The electric field
will be created by the exciting coil of the sensor in the system under test. The
output signal from the interdigital sensor is small and alternating in nature. Figure7 shows the signal waveform from Sensor_1 and Table 1 shows the relationship
between excitation voltage, sensing voltage, current, and impedance for all
sensors.Table 1
Relationship
between excitation voltage, sensing voltage, current, and impedance.
Sensor typeExcitationSensingImpedance (MΩ)Vin (V)Freq (kHz)Vout (mV)PhaseI (mA)Sensor_110.010.0156.383.01.3037.678Sensor_210.010.0187.583.01.5636.400Sensor_310.010.0192.183.01.6016.247Figure 7
The signal
waveform of Sensor_1.A signal rectification
circuit shown in Figure8 is required to rectify and amplify the alternating
signal. Two LM324 low power quad operational amplifiers were used in the
circuit. A full-wave rectifier will convert the signal waveform to one constant
polarity (+ve and  −ve) at its
output. The amplify signal will pass through a low-pass filter with cut off
frequency of 13 kHz. The output signal from the circuit is fed to the analog
input of microcontroller SiLab C8051F020 to obtain digital value.Figure 8
Circuit
diagram for full-wave signal rectification.


### 4.1. Laboratory Experiments

Before experimenting with
the actual samples from mussels, we have explored the response of the sensors
with three peptide derivatives namely sarcosine, proline, and hydroxylproline
for the initial studies, which are structurally and closely related to our
target molecule. N-methyl glycine represents the simplest structure. The
proline molecule is arguably the most important amino acid in peptide
conformation, containing the basic structural similarity to the domoic acid. The
hydroxyproline containing  hydroxyl group at position 4 represents the susbtituent at C4, which is particularly
crucial for the binding. Figure9 shows the chemical  structure of the three samples
used for the initial studies. These three samples were chosen because their
prices were cheaper compared to the domoic acid. Table 2 shows the prices of
each chemical in the market.Table 2
Prices of
different chemicals in the market.
No.Chemical nameQuantityPrice (USD)1Sarcosine100 g$53.302L-Proline100 g$100.003Hyroxy proline25 g$102.804Domoic acid1 mg$508.005Kaimic acid10 mg$54.80Sarcosine,
proline, and hydroxyproline molecules used for initial studies.A small amount
of samples of sarcosine, proline, and hydroxyproline (1.4 mg) were used for the
experiment. Each chemical was placed on the effective area of the sensor as
shown in Figure10. The electrodes are separated from the sample with the help
of glad wrap. The experimental setup is shown in
Figure 11. Measurement data of
air (Vair) for calibration was
first collected before measuring the chemical samples. The output voltage (Vout) from the rectification
circuit and digital value from microcontroller were collected. Data of sensor
sensitivity was analyzed. The sensitivity can be calculated by (3)Sensitivity=(VOut−VAir)VAir×100, where VAir  is voltage air or voltage without sample and Vout is voltage output with sample.Figure 10
The sample on
the sensor of measurement.Figure 11
The
experimental setup.Figures12 and
13 show the comparative values of the sensor output for three different sensors
for the three samples. It can be said that the sensors respond very well to the
chemicals. It is seen that it is possible to discriminate the different
chemicals from the output of the sensor. This provides an opportunity to
develop a sensing system to detect the presence of DA in raw oysters, claws,
and mussels. Result from initial studies concludes that Sensor_1 shows a good
response and give better  sensitivity to the chemical structure. It can clearly
discriminate between three samples compared to Sensor_2 and Sensor_3.
Therefore, Sensor_1 was chosen for further analysis with real mussels.Figure 12
Comparison of
sensors sensitivity with different sensor configurations.Figure 13
Comparison of
sensors sensitivity with different chemical samples.


### 4.2. Experiment with Mussels

Assuming all mussels in
the market are good, containing DA level less than 20μg/g of mussel meat. A
set of 8 mussels were randomly selected and were cut from 5 different positions.
Figure 14 shows that how the samples were cut from each mussel. All samples
were placed onto the nonstick paper and left them to be dried at a controlled
laboratory temperature of 22.9°C with humidity of 40%. Each sample thickness
and surface temperature was measured using digital calliper and temperature
tester, respectively. The samples thickness measured were between 1.6 mm to
3.2 mm having the temperature of range between 21.9°C to 22.5°C. The experimental
setup shown in Figure 10 was constructed to test the samples. Each sample was
placed on the sensor as shown in Figure 15.Figure 14
The samples were cut from each mussel.Figure 15
The sample (mussel) on the sensor of
measurement.The output
voltage from the signal rectification circuit and the digital values from
microcontroller were taken. Data of each sample was taken for analysis before
and after a small amount of proline (0.7 mg) were added to the samples. Proline
was used in the experiment because it has a close chemical
structure to domoic acid. Result in Figure16 shows
that there was a significant difference of Sensor_1 sensitivity before and
after adding the proline. It is shown that Sensor_1 was able to detect the
presence of proline on the sample. Graph on Figure 17 shows the average sensor
sensitivity is higher at location 5 and lower at location 1. This is because
the average thickness at location 5 is 2.7 mm, which is thicker compared to
other locations. The sensitivity is lowest at location 1 of having average
thickness of 2.3 mm. The graph in Figure 18 shows a good correlation with R2=0.717, between
sensor sensitivity with sample thickness. Experiment with DA was also
conducted. Small amount of DA of 0.1 mg was diluted in 2.0 mL
(2.0 g) of water
to give 50 ppm of DA. Results in Figure 19 shows the respond of Sensor_1 with small
amount of DA (0.5 μg) injected to the samples.Figure 16
Sensor
sensitivity with sample before and after adding proline.Figure 17
Results of
mussel samples from different location.Figure 18
The
relationship between sensor sensitivity with sample thickness.Figure 19
Sensor sensitivity with DA.


## 4.1. Laboratory Experiments

Before experimenting with
the actual samples from mussels, we have explored the response of the sensors
with three peptide derivatives namely sarcosine, proline, and hydroxylproline
for the initial studies, which are structurally and closely related to our
target molecule. N-methyl glycine represents the simplest structure. The
proline molecule is arguably the most important amino acid in peptide
conformation, containing the basic structural similarity to the domoic acid. The
hydroxyproline containing  hydroxyl group at position 4 represents the susbtituent at C4, which is particularly
crucial for the binding. Figure9 shows the chemical  structure of the three samples
used for the initial studies. These three samples were chosen because their
prices were cheaper compared to the domoic acid. Table 2 shows the prices of
each chemical in the market.Table 2
Prices of
different chemicals in the market.
No.Chemical nameQuantityPrice (USD)1Sarcosine100 g$53.302L-Proline100 g$100.003Hyroxy proline25 g$102.804Domoic acid1 mg$508.005Kaimic acid10 mg$54.80Sarcosine,
proline, and hydroxyproline molecules used for initial studies.A small amount
of samples of sarcosine, proline, and hydroxyproline (1.4 mg) were used for the
experiment. Each chemical was placed on the effective area of the sensor as
shown in Figure10. The electrodes are separated from the sample with the help
of glad wrap. The experimental setup is shown in
Figure 11. Measurement data of
air (Vair) for calibration was
first collected before measuring the chemical samples. The output voltage (Vout) from the rectification
circuit and digital value from microcontroller were collected. Data of sensor
sensitivity was analyzed. The sensitivity can be calculated by (3)Sensitivity=(VOut−VAir)VAir×100, where VAir  is voltage air or voltage without sample and Vout is voltage output with sample.Figure 10
The sample on
the sensor of measurement.Figure 11
The
experimental setup.Figures12 and
13 show the comparative values of the sensor output for three different sensors
for the three samples. It can be said that the sensors respond very well to the
chemicals. It is seen that it is possible to discriminate the different
chemicals from the output of the sensor. This provides an opportunity to
develop a sensing system to detect the presence of DA in raw oysters, claws,
and mussels. Result from initial studies concludes that Sensor_1 shows a good
response and give better  sensitivity to the chemical structure. It can clearly
discriminate between three samples compared to Sensor_2 and Sensor_3.
Therefore, Sensor_1 was chosen for further analysis with real mussels.Figure 12
Comparison of
sensors sensitivity with different sensor configurations.Figure 13
Comparison of
sensors sensitivity with different chemical samples.


## 4.2. Experiment with Mussels

Assuming all mussels in
the market are good, containing DA level less than 20μg/g of mussel meat. A
set of 8 mussels were randomly selected and were cut from 5 different positions.
Figure 14 shows that how the samples were cut from each mussel. All samples
were placed onto the nonstick paper and left them to be dried at a controlled
laboratory temperature of 22.9°C with humidity of 40%. Each sample thickness
and surface temperature was measured using digital calliper and temperature
tester, respectively. The samples thickness measured were between 1.6 mm to
3.2 mm having the temperature of range between 21.9°C to 22.5°C. The experimental
setup shown in Figure 10 was constructed to test the samples. Each sample was
placed on the sensor as shown in Figure 15.Figure 14
The samples were cut from each mussel.Figure 15
The sample (mussel) on the sensor of
measurement.The output
voltage from the signal rectification circuit and the digital values from
microcontroller were taken. Data of each sample was taken for analysis before
and after a small amount of proline (0.7 mg) were added to the samples. Proline
was used in the experiment because it has a close chemical
structure to domoic acid. Result in Figure16 shows
that there was a significant difference of Sensor_1 sensitivity before and
after adding the proline. It is shown that Sensor_1 was able to detect the
presence of proline on the sample. Graph on Figure 17 shows the average sensor
sensitivity is higher at location 5 and lower at location 1. This is because
the average thickness at location 5 is 2.7 mm, which is thicker compared to
other locations. The sensitivity is lowest at location 1 of having average
thickness of 2.3 mm. The graph in Figure 18 shows a good correlation with R2=0.717, between
sensor sensitivity with sample thickness. Experiment with DA was also
conducted. Small amount of DA of 0.1 mg was diluted in 2.0 mL
(2.0 g) of water
to give 50 ppm of DA. Results in Figure 19 shows the respond of Sensor_1 with small
amount of DA (0.5 μg) injected to the samples.Figure 16
Sensor
sensitivity with sample before and after adding proline.Figure 17
Results of
mussel samples from different location.Figure 18
The
relationship between sensor sensitivity with sample thickness.Figure 19
Sensor sensitivity with DA.


## 5. Conclusion

Initial studies on three
different chemicals proved that the sensors can respond very well to each
chemical and can clearly discriminate between them. Sensor_1 was selected for
further analysis with real mussels because it has better sensitivity. Analysis
of results with real mussels shows that Sensor_1 can detect the presence of
proline on the samples. It can be said that Sensor_1 was able to detect the
small amount of proline which is structurally close to DA chemical structure. Experiment
with DA also shown that Sensor_1 was able to detect the presence of DA in
mussel meat. Therefore, novel planar interdigital sensor can be used to assess
the chemicals structure in mussels. The sensing system can provide a fast and
rapid analysis of DA in shellfish meats. A low cost and reliable sensing system
for commercial use will be developed for future work. The proposed system will
use the microcontroller-based sensing system where the calibration of sensor
and analysis can be done instantaneously. Calibration of air will be taken
first and then measurement data of sample. A set of threshold values (sensor
sensitivity) at different sample thickness need to be set. This threshold
values can be obtained from the experiment with more mussels with injected DA.
A result of pass and fail analysis will be programmed into the microcontroller
according to this threshold values.



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*Source: 150874-2009-01-27.xml*