1. Full Paper
Tensammetric Analysis of Nonionic Surfactant Mixtures by
Artificial Neural Network
A. Safavi,* F. Sedaghatpour, H. R. Shahbaazi
Department of Chemistry, College of Science, Shiraz University, Shiraz 71454 Iran
*e-mail: safavi@chem.susc.ac.ir
Received: June 16, 2004
Accepted: January 6, 2005
Abstract
An artificial neural network (ANN) model has been developed for tensammetric determination of a series of Brijes
(Brij 30, Brij 35, Brij 56, Brij 96) as nonionic surfactants. The tensammetric method is based on the measurement of
the capacitive current of the mercury electrode after adsorption of surfactants. All Brijes were analyzed in the
concentration range of 1.0 – 100.0 mg mLÀ1
. The proposed method shows good sensitivity and applicability to the
simultaneous determination of mixtures of four Brijes in aqueous solutions.
Keywords: Tensammetric, Nonionic surfactant, Simultaneous determination, Artificial neural network (ANN)
1. Introduction
A large number of synthetic nonionic surfactants can be
found in sewage and surface water. Surfactants are the most
important synthetic substances with respect to carbon
supply. Their flux to surface water is some orders of
magnitude higher than fluxes of pollutants like polychlor-
obiphenyls (PCB) or polyaromatic hydrocarbons (PAH) [1].
Therefore, determination of each of them is important and
difficult with the present state of trace analysis of surfac-
tants. On the other hand, such a target is demanding from
the point of view of routine control of water pollution.
Alcohol etoxylates have emerged as the principal nonionic
surfactants in consumer detergent products. Consistent
qualities, expanding production capacity of relatively inex-
pensive detergents, highly biodegradable alcohols, and
increasing usage in laundry products, particularly heavy-
duty liquids, are the principal factors underlying this growth.
Many surface-active substances exhibit tensammetric
(adsorption/desorption) peaks [2, 3]. Generally, the analyt-
ical results are calculated from the depression of double-
layer capacitance or the height of the desorption peak.
Various types of surfactants, both ionic and nonionic, as well
as some macromolecular compounds are easily determined
in aqueous solutions by means of their adsorption on
suitable electrodes. The adsorption involves displacement
of water molecules by organic molecules which produces a
measurable decrease of the electrode double-layer capacity
[4]. The use of the mercury electrode is advantageous
because of its reproducibly renewable surface. Alternating
current (AC) polarography can be used to measure either
the capacity of the electrode double-layer or the capacitive
current. This simple methodology has been widely applied
in environmental analysis, polymer analysis, process control,
etc. [4]. Direct measurement of surfactants by electro-
chemical methods has proved useful in the determination
and characterization of organic matter in natural and
polluted waters [5, 6]. The height of tensammetric peaks
can be used for quantitative analytical purposes. The
determination of components of surfactant mixtures is a
much more difficult problem than the individual surfactants.
Mixtures of surfactants may undergo complicated mutual
influences of the components both in the bulk solution [7]
and on the electrode surface [4]. It has been shown that the
multivariate calibration technique based on singular value
decomposition and Ho-Kashyp algorithm (SVDHK) can be
applied successfully in cases where interaction between the
components of a mixture influences the analytical signal and
in cases where this signal is nonlinear with the concentration
[8].
The aim of this work is to show how a multivariate
calibration technique based on artificial neural network can
be appliedsuccessfully tothe simultaneousdeterminationof
several nonionic surfactants.
2. Experimental
2.1. Apparatus
Voltammetric measurements were made with a Metrohm
694 (Herisau. Switzerland) VA Stand coupled with a
Metrohm 693 VA Processor. Voltammetric experiments
were carried out with a three-electrode arrangement with an
Ag/AgCl, 3 M KCl reference electrode, platinum wire
counter electrode and a multi-mode mercury drop working
electrode. Dissolved oxygen was removed by purging the
solution under study with argon. pH measurements were
made with a Metrohm 691 pH-meter.
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Electroanalysis 2005, 17, No. 12 2005 WILEY-VCH Verlag GmbH Co. KGaA, Weinheim DOI: 10.1002/elan.200403225

2. 2.2. Reagents
A stock solution (1%) of polyoxyethylene (4) lauryl ether
(Brij 30), was prepared by dissolving 1.0000 g Brij 30
(Fluka) in water and diluting to 100 mL in a volumetric
flask. A stock solution (1%) of polyoxyethylene (23) lauryl
ether (Brij 35), was prepared by dissolving 1.0000 g of Brij
35 (Fluka) in water and diluting to 100 mL in a volumetric
flask. A stock solution (0.5%) of polyoxyethylene (10) cetyl
ether (Brij 56), was prepared by dissolving 0.5000 g of Brij
56 (Fluka) in water and diluting to 100 mL in a volumetric
flask. A stock solution (1%) of polyoxyethylene (10) oleyl
ether (Brij 96), was prepared by dissolving 1.0000 g of Brij
96 (Fluka) in water and diluting to 100 mL in a standard
flask. Britton-Robinson (B-R) buffer was prepared by
dissolving boric acid (15g), orthophosphoric acid (8.1 mL)
and glacial acetic acid (6.9 mL) in water and diluting to
500 mL. Appropriate volumes of this solution were adjust-
ed to the required pH value with sodium hydroxide solution
(3 M).
2.3. Procedure
All experiments were performed at room temperature. A
2 mL aliquot of buffer (B – R) solution (pH 8), and appro-
priate volumes of stock solution of each Brij were pipetted
into a 10 mL volumetric flask and diluted to the mark with
water and then transferred to the voltammetric cell.
The solution was purged with argon first for 10 minutes
and then for 20 s before each voltammetric step. Subse-
quently, the stirrer was switched off for 5 s. Finally, a
potential scan was carried out in a negative direction from
À500 to À2000 mV in alternating current mode and the
charging current due to adsorption/desorption of the
surfactants was recorded.
Data were processed by using an artificial neural network
model in order to find the quantity of each component
present in the sample. A back-propagation artificial neural
network having three layers was created using a Visual-
Basic software package.
3. Results and Discussion
3.1. Preliminary Experiments
Preliminary experiments were carried out to identify the
best voltammetric technique for quantitative study. Differ-
ential pulse (DP) polarography and alternating current
(AC) polarography at phase angles 0 and 908 were applied
for different concentrations of Brij 30 (Fig. 1), Brij 35, Brij
56, Brij 96 and tensammetric behavior of each Brij was
studied. In AC polarography at phase angle 0, shift of
tensammetric peak is little. This is an advantageous point for
multivariate analysis because changing position of the peaks
can affect the results. Also at phase angle 0, the tensam-
metric peaks are sharp and the sensitivity is high. Therefore,
AC polarography at phase angle 0 was selected for
quantitative determination.
Representative tensammetric responses for the surfac-
tants investigated are shown in Figures 2 – 5. Brij 30 (Fig. 2)
forms two peaks within the concentration range of 1 – 5.5 mg
mLÀ1
. The less negative peak is wide and the other is sharp.
The increase in the concentration of surfactant causes the
shift of the wide (less negative) peak toward more negative
Fig. 1. a) AC polarogram at phase angle 0; b) AC polarogram at
phase angle 90; c) DP polarogram for different concentrations
(5 – 3400 mg mLÀ1
) of Brij 30 at pH 9.
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3. potentials while the other peak stays in position (approx-
imately À1770 mV). The wide peak disappears at concen-
trations above 5.5 mg mLÀ1
while the sharp peak increases
with the increase of concentration of surfactant up to
3400 mg mLÀ1
. These findings are very similar to what has
been reported previously for other oxyethylated alcohols [9,
10].
The tensammetric behavior of Brij 35 is shown in Figure 3.
This surfactant shows one broad peak in the concentration
range of 0.7 – 2.0 mg mLÀ1
. The potential of this peak shifts
with increase of surfactant concentration toward more
negative potentials. The increase in Brij 35 concentration
over the value of 2.0 mg mLÀ1
causes the appearance of
another peak. The new peak grows with the increase of
surfactant concentration up to 1900 mg mLÀ1
and, simulta-
neously, the first peak decreases and finally disappear with
increase in Brij 35 concentration. This trend is also the same
as what has been reported previously for this surfactant [9,
10].
Figures 4 and 5 show tensammetric curves for Brij 56 and
Brij 96, respectively, which show similar trends as for other
Brij surfactants.
Fig. 2. AC polarogram at phase angle 0 for different concentrations (1 – 3400 mg mLÀ1
) and a) 1 – 5.5 mg mLÀ1
of Brij 30 at pH 9.
Fig. 3. AC polarogram at phase angle 0 for different concentrations (0.7 – 1900 mg mLÀ1
) and a) 0.7 – 26 mg mLÀ1
of Brij 35 at pH 9.
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4. 3.2 Effect of Operational Parameters
3.2.1. Effect of pH
The influence of pH on the tensammetric peak was
studied in the pH range of 2 to 11 for each Brij (Fig. 6). In
order, to keep the composition of the buffer constant,
when studying the effect of pH, Britton-Robinson buffers
were used. There was no tensammetric peak for any of the
Brijes studied in solutions with pH lower than 6. Accord-
ing to the results obtained, pH 8 was selected as the
optimum pH for analytical purposes. At this pH value, all
four Brijes have sharp and sensitive tensammetric
peaks.
3.2.2. Effect of Scan Rate
The effect of scan rate over the range of 20 – 120 mV/s was
examined on the tensammetric peak currents of Brij 30, Brij
35, Brij 56 and Brij 96.
The highest tensammetric peak currents were obtained
for Brij 35, Brij 56 and Brij 96 at a scan rate of 40 mV sÀ1
.
Therefore, this scan rate was selected as the optimum scan
rate for analytical purposes.
Fig. 4. AC polarogram at phase angle 0 for different concentrations (0.6 – 850 mg mLÀ1
) and a) 0.6 – 5.5 mg mLÀ1
of Brij 56 at pH 9.
Fig. 5. AC polarogram at phase angle 0 for different concentrations (4.5– 3000 mg mLÀ1
) and a) 4.5 – 47.5 mg mLÀ1
of Brij 96 at pH 9.
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5. 3.2.3. Effect of Frequency of the Alternating Voltage
The effect of frequency of alternating voltage was studied
over 30 – 240 Hz range for Brij 30, Brij 35, Brij 56 and Brij 96.
The height of the tensammetric peak increases with
increasing frequency.
Maximum sensitivity was obtained at frequency of 240
Hz. However, this frequency was not selected for quantita-
tive determination, because at this frequency, the determi-
nation range decreases. Therefore, the frequency of 60 Hz
was selected as the optimum frequency of alternating
voltage.
3.2.4. Effect of Accumulation Time
At short accumulation times (up to 30 s), peak current
increased with accumulation time, indicating that before
adsorptive equilibrium is reached, the longer accumulation
time leads to higher adsorption of the surfactant. The peak
current increases upon extending accumulation time to
about 60 s for each Brij and then starts to level off. However,
the determination range decreases with increase in accu-
mulation time. Therefore, in order to have a larger concen-
tration span of the Brijes, the accumulation time of 0 s was
selected for further studies.
3.2.5. Effect of Accumulation Potential
The effect of accumulation potential on the tensammetric
peak current was examined over the potential range of À450
to À1800 mV. As shown in Figure 7, the peak current is
nearly independent of the accumulation potential except for
Brij 30 which decreases with changing potential from À450
to À1800 mV. Therefore, an accumulation potential of
À500 mV was selected as the optimized potential value.
3.3 Multivariate Calibration with ANN
Figure 8 shows the tensammetric peak for each Brij and
their mixtures under optimum experimental conditions.
There are high overlapping tensammetric peaks for the four
Brijes. Therefore, it seems that the use of a multivariate
calibration technique could be helpful for simultaneous
determination.
The data obtained from the AC polarogram were
processed by ANN, which was trained with the back-
propagation of errors learning algorithm. The reduced AC
polarographic data with principal component analysis
(PCA) were used as the input for ANN. The first step in
the simultaneous determination of Brijes by ANN method-
ology involves constructing the calibration set for the
mixtures containing Brij 30, Brij 35, Brij 56 and Brij 96. In
this study, the calibration and prediction sets were prepared
randomly to reduce correlation between concentrations of
Brijes. Thirty mixtures were selected as the calibration set
and eleven mixtures were selected as the prediction set for
each Brij. The concentrations for the training set of Brij 30,
Brij 35, Brij 56 and Brij 96 were between 1.0 and 100.0 mg
Fig. 6. Effect of pH on tensammetric peak current. Experimen-
tal conditions: Eac ¼ 500 mV, tac ¼ 0 s, 50 mg mLÀ1
Brij 30, 50 mg
mLÀ1
Brij 35, 50 mg mLÀ1
Brij 56, 50 mg mLÀ1
Brij 96.
Fig. 7. Effect of accumulation potential on tensammetric peak
current. Experimental conditions: tac ¼ 0 s, pH ¼ 8, 50 mg mLÀ1
Brij
30, 50 mg mLÀ1
Brij 35, 50 mg mLÀ1
Brij 56, 50 mg mLÀ1
Brij 96.
Fig. 8. AC polarogram for 50 mg mLÀ1
each Brij; 1) Brij 30, 2)
Brij 35, 3) Brij 56, 4) Brij 96, 5) Mixture of Brij 30, Brij 35, Brij 56,
and Brij 96 at pH 8.
1116 A. Safavi et al.
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6. mLÀ1
in the calibration matrix. Also, the number of inputs
was normalized between À10 and þ10. Calibration set was
used for construction of ANN model, and the independent
test set was used to evaluate the quality of the model.
There are several ways to optimize the parameters of a
network [11 – 13]. One of them is constructed on the
minimum error of prediction of testing set [14, 15]. The
overall predictive capacity of the model was compared in
terms of relative standard error, RSE defined as:
RSE %ð Þ ¼ 100 Â
PN
j¼1
^Cj À Cj
PN
j¼1
Cj
À Á2
20
B
B
B
@
1
C
C
C
A
1=2
where Cj is jth
component of the desired target (real sample)
and Cˆ j is the jth
component of output produced by network (
predicted sample). Training and testing of the network is a
process of determination of the networks topology and
optimization of its adjustable parameters, where we seek the
minimum of an error surface in multi-dimensional space.
The optimum learning rate and momentum were evaluated
by obtaining those, which yielded a minimum in error of
prediction (of the test set).
To investigate the prediction ability of ANN, neural
network models for individual component, were also made
with respect to output layer considered as a single node
corresponding to the analyte [16]. The construction of these
ANN models is summarized in Table 1. The structure of the
network was comprised of three layers, an input, a hidden
and an output layer. The addition of an extra parameter,
called bias, to the decision function increases its adaptability
to the decision problem it is designed to solve. In this
structure, þ1 was added as a bias.
The most versatile transfer function that can be used to
model a variety of relationships is the sigmoidal type. In the
present nonlinear system, sigmoid functions were found to
be optimum for hidden and output layers. The optimum
number of input obtained was 5 for all Brijes using principal
component analysis (PCA). The number of nodes in the
hidden layer was determined by training ANN with differ-
ent number of nodes and then comparing the prediction
errors from the test set.
The training was stopped manually when the root mean
square error of the test set remained constant after
successive iteration. The optimum number of iteration
cycles or epochs (one pass of all objects, 30 mixtures,
through the network) for each component was also ob-
tained. Because there are several local minima, the algo-
rithm was run from different starting values of initial weights
to find the best optimum. RSEP for Brij 30, Brij 35, Brij 56
and Brij 96 were 3.5 %, 4.9%, 2.6%, and 1.7%, respectively.
The reasonable relative error for each analyte indicates the
accuracy of the proposed method (Table 2)
For showing the applicability of the model, tap water
samples were spiked with different concentration ratios of
Brijes and these synthetic mixed samples were tested by the
model. The results were all satisfactory (Table 3). This can
validate the ANN model.
3. 4. Effect of Interferences
The selectivity was assessed by studying the influence of
foreign substances on the determination of Brijes. The effect
Table 1. Optimized parameters used for construction of ANN
models.
Parameter Compounds
Brij 30 Brij 35 Brij 56 Brij 96
Input nodes 5 5 5 5
Hidden nodes 5 7 6 4
Output nodes 1 1 1 1
Learning rate 0.02 0.20 0.10 0.40
Momentum 0.125 0.100 0.200 0.200
Hidden layer function sigmoid sigmoid sigmoid sigmoid
Output layer function sigmoid sigmoid sigmoid sigmoid
Number of iterations 1920 2000 1250 700
Table 2. Composition of prediction set and relative standard errors for the determination of analytes.
Actual Found
Brij 30 Brij 35 Brij 56 Brij 96 Brij 30 Brij 35 Brij 56 Brij 96
96.0 100.0 50.0 90.0 95.0 97.7 50.5 86.9
89.0 93.0 17.0 100.0 83.1 99.7 17 99.9
96.5 100.0 40.0 77.5 98.2 97.7 44.6 76.5
100.0 93 99.0 17.5 96 97.8 98.1 17.3
15.0 91.0 75.0 99.0 15.8 97.7 73.8 98.6
100.0 90.0 97.5 100.0 99.3 97.7 99.3 99.2
3.0 99.0 5.5 100.0 3.1 97.7 5.9 99.9
82.0 45.0 9.5 7.5 81.3 46.0 9.6 7.6
98.0 98.5 90.0 26.0 98.2 97.7 89.9 25.5
57.0 7.0 50.0 75.0 61.7 6.5 50.5 73.6
7.5 20.0 17.0 71.0 6.0 19.7 17 73.6
R.S.E. (%) 3.5 4.9 2.6 1.7
1117Tensammetric Analysis of Nonionic Surfactant Mixtures
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7. of several surfactants at different concentrations was
studied on tensammetric peak current values of a solution
containing 50.0 mg mLÀ1
of each Brij. Polyethyleneglycol
tert-octylphenyl ether (Triton X-100 and Triton X-305) as
nonionic surfactant increased the tensammetric peak. While
polyoxyethylene sorbitanmonolaurate (Tween 20) and Span
80 as nonionic surfactant decreased the tensammetric peak.
The effect of N-dodecyl pyridinum chloride (DPC), cetyl-
pyridinum chloride (CPC), cetyltrimethyl ammonium bro-
mide (CTAB) and N-dodecyltrimethylammonium bromide
(DTAB) as cationic surfactants, sodium dodecyl sulfate
(SDS) as an anionic surfactant was studied on tensammetric
peak of each Brij. All of them decreased the tensammetric
peak observed for the Brijes. The tolerance limit , defined as
the concentration above which a change of more than three
times of standard deviation was observed in the signals
obtained for the analytes, was 1.0 mg mLÀ1
for all the above
surfactants.
4. Conclusions
The present study demonstrates that AC polarography
based on adsorption and desorption (tensammetry) of
nonionic surfactants can be used to determine several Brijes
in aqueous samples. However, interferences from other
adsorbed compounds are the main drawback to the devel-
opment of tensammetry as a viable technique for routine
analysis. ANN can be used to determine them simultane-
ously and in the presence of known interference. Therefore,
the above system can be a potential candidate for sensitive
and selective simultaneous determination of nonionic
surfactants.
5. Acknowledgement
The authors wish to acknowledge the support of this work by
Shiraz University Research Council.
6. References
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Table 3. Estimated and actual concentrations of Brijes in synthetic samples.
Actual Found
Brij 30 Brij 35 Brij 56 Brij 96 Brij 30 Brij 35 Brij 56 Brij 96
Sample I 2.5 96.0 86.0 36.0 2.30 Æ 0.06 99.0 Æ 0.06 86.70 Æ 0.01 34.8 Æ 0.15
Sample II 89.0 80.0 7.5 5.5 88.50 Æ 0.01 80.0 Æ 1.0 7.80 Æ 0.40 4.05 Æ 0.05
1118 A. Safavi et al.
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