Electrochemical Sensor for Explosives Precursors’ Detection in Water

Abstract

Although all countries are intensifying their efforts against terrorism and increasing their mutual cooperation, terrorist bombing is still one of the greatest threats to society. The discovery of hidden bomb factories is of primary importance in the prevention of terrorism activities. Criminals preparing improvised explosives (IE) use chemical substances called precursors. These compounds are released in the air and in the waste water during IE production. Tracking sources of precursors by analyzing air or wastewater can then be an important clue for bomb factories’ localization. We are reporting here a new multiplex electrochemical sensor dedicated to the on-site simultaneous detection of three explosive precursors, potentially used for improvised explosive device preparation (hereafter referenced as B01, B08, and B15, for security disclosure reasons and to avoid being detrimental to the security of the counter-explosive EU action). The electrochemical sensors were designed to be disposable and to combine ease of use and portability in a screen-printed eight-electrochemical cell array format. The working electrodes were modified with different electrodeposited metals: gold, palladium, and platinum. These different coatings giving selectivity to the multi-sensor through a “fingerprint”-like signal subsequently analyzed using partial least squares-discriminant analysis (PLS-DA). Results are given regarding the detection of the three compounds in a real environment and in the presence of potentially interfering species.

Full text

challenges
Article
Electrochemical Sensor for Explosives Precursors’
Detection in Water
CloéDesmet 1, Agnes Degiuli 1, Carlotta Ferrari 2, Francesco Saverio Romolo 3, Loïc Blum 1
and Christophe Marquette 1, *
1Equipe Génie Enzymatique, Membranes Biomimétiques et Assemblages Supramoléculaires, Univ Lyon,
UniversitéLyon1, 43 Bd du 11 Novembre 1918, 69622 Villeurbanne, France; cloe.desmet@outlook.com (C.D.);
agnes.degiuli@univ-lyon1.fr (A.D.); loic.blum@univ-lyon1.fr (L.B.)
2Institut de Police Scientifique (IPS), Universitéde Lausanne, Dorigny, 1004 Lausanne, Switzerland;
carlotta.ferrari23@libero.it
3Legal Medicine Section-SAIMLAL Department, SAPIENZA University of Rome, Viale Regina Elena, 336,
00161 Roma, Italy; francescosaverio.romolo@uniroma1.it
*Correspondence: christophe.marquette@univ-lyon1.fr
Academic Editor: Palmiro Poltronieri
Received: 9 February 2017; Accepted: 17 March 2017; Published: 22 March 2017
Abstract:
Although all countries are intensifying their efforts against terrorism and increasing their
mutual cooperation, terrorist bombing is still one of the greatest threats to society. The discovery of
hidden bomb factories is of primary importance in the prevention of terrorism activities. Criminals
preparing improvised explosives (IE) use chemical substances called precursors. These compounds
are released in the air and in the waste water during IE production. Tracking sources of precursors by
analyzing air or wastewater can then be an important clue for bomb factories’ localization. We are
reporting here a new multiplex electrochemical sensor dedicated to the on-site simultaneous detection
of three explosive precursors, potentially used for improvised explosive device preparation (hereafter
referenced as B01, B08, and B15, for security disclosure reasons and to avoid being detrimental to
the security of the counter-explosive EU action). The electrochemical sensors were designed to be
disposable and to combine ease of use and portability in a screen-printed eight-electrochemical cell
array format. The working electrodes were modified with different electrodeposited metals: gold,
palladium, and platinum. These different coatings giving selectivity to the multi-sensor through
a “fingerprint”-like signal subsequently analyzed using partial least squares-discriminant analysis
(PLS-DA). Results are given regarding the detection of the three compounds in a real environment
and in the presence of potentially interfering species.
Keywords:
bomb factory; electrochemical array; explosive precursors; improvised explosives; partial
least squares-discriminant analysis
1. Introduction
Although all countries are intensifying their efforts against terrorism and increasing their mutual
cooperation, terrorist bombing is still one of the greatest threats to society. The discovery of hidden
bomb factories is of primary importance in the prevention of terrorism activities. Different explosives
have been widely used in improvised explosive devices (IEDs) by terrorists, due to their relatively
simple preparation protocol. For instance, triacetone triperoxide (TATP), cyclotrimethylenetrinitramine
(RDX), hexamethylene triperoxide diamine (HMTD), and trinitrotoluene (TNT) can be synthesized using
commercially available chemicals following recipes that can be found on the Internet. Commercially
available chemicals used to prepare improvised explosives (IE) are called “precursors” and are regulated
in Europe by the European Parliament and Council Regulation (EU) No. 98/2013 on the marketing and
Challenges 2017,8, 10; doi:10.3390/challe8010010 www.mdpi.com/journal/challenges

Challenges 2017,8, 10 2 of 11
use of explosives precursors, adopted on 15 January 2014 [
1
]. Consequently, growing security concerns
have generated urgent needs for innovative tools for on-field screening of precursors for IE manufacturing.
In numerous security applications and scenarios, the reliable multi-parametric detection of these precursors
using a small portable and disposable sensor may be a real asset.
During the last few years, an important increase of research within this area has been observed,
through development of new detection approaches and improvements of existing techniques.
Spectroscopic approaches have been the most widely explored [
2
–
7
], but quartz crystal microbalance
(QCM), ion chromatography [
8
], capillary electrophoresis [
9
], surface-enhanced Raman scattering
(SERS) [
10
], nanotechnology based-methods (using molecularly imprinted polymers [
11
], nanotubes,
or nanoparticles), and sensor techniques [
12
–
16
] have also been largely described. Amongst the
different types of sensors developed to address this need, the electrochemical sensors [
17
–
19
] present
the advantages of being fast, inexpensive, and adapted to miniaturization. Nevertheless, the existing
electrochemical devices rarely enabled a label-free multi-parametric detection. We are reporting
here a new multiplex electrochemical sensor dedicated to the on-site simultaneous detection of three
explosive precursors (hereafter referenced as B01, B08, and B15 for security disclosure reasons and
to avoid being detrimental to the security of the counter-explosive EU action), potentially used for
improvised explosive devices’ preparation. The electrochemical chips were designed to be disposable
and to combine ease of use and portability thanks to a simple and inexpensive screen-printing
fabrication technique. An eight-electrode array was then produced, composed of four different
electrode compositions (gold, platinum, palladium, and carbon). This electrode composition was of
high significance since this is the basis of the electrochemical signature of the different compounds.
This electrochemical signature was subsequently analyzed using partial least squares-discriminant
analysis (PLS-DA) in order to classify and discriminate the different explosive precursors.
2. Materials and Methods
2.1. Materials
Ammonium chloride (NH
4
Cl), calcium chloride (CaCl
2
), chloroplatinic acid hexahydrate
(H
2
PtCl
6
), gold (III) chloride (HAuCl
4
), palladium acetate (Pd(OAc)
2
), hexamethylenetetramine
(hexamine), and whey from bovine milk were purchased from Sigma (Lyon, France).
Magnesium chloride (MgCl
2
), sodium acetate, sodium chloride (NaCl), sodium dihydrogenophosphate
(Na
2
HPO
4
), and hydrochloric acid (HCl) were obtained from Prolabo (Fontenay-sous-Bois, France).
Bacteriological peptone was purchased from Fluka (Saint-Quentin, Fallavier, France).
All solutions were prepared with ultrapure water (18.2 MΩ).
2.2. Methods
2.2.1. Working Electrode Modification
The electrode array (DRP-8x110-U20) and the connector (CAST8X) were purchased from Dropsens
(Llanera, Spain). Each of the eight electrodes is composed of a carbon paste working electrode, a carbon
paste counter electrode, and a silver/silver chloride reference electrode.
Six of the eight working electrodes were modified in order to generate different surfaces for
electrochemical measurement (Figure 1). Three metals were independently electrodeposited on
two working electrodes through galvanostatic chronopotentiometry as previously described [
20
].
A constant current was applied for 10 min, leading to a fixed charge, optimized for each metal
solution. In practice, a drop of 40
µ
L of the solution was deposited on the active area prior to
chronopotentiometry. The gold deposition was carried out using a fixed current of
−
300
µ
A, in the
presence of a solution of 10 mM HAuCl4 in 0.1 M HCl. A fixed current of
−
150
µ
A was applied for the
palladium and platinum deposition in the presence of a solution composed of 10 mM or Pd(OAc)
2
or H
2
PtCl
6
in 0.1 M HCl. Finally, a fixed current of
−
150
µ
A was applied to a bare carbon working

Challenges 2017,8, 10 3 of 11
electrode in a solution of 0.1 M HCl. Following each deposition, the electrode chips were rinsed with
ultrapure water.
Challenges 2017, 8, 10 3 of 12
electrode in a solution of 0.1 M HCl. Following each deposition, the electrode chips were rinsed with
ultrapure water.
Figure 1. The eight-electrode chip with its metal deposition, which will lead to the acquisition of an
electrochemical signature.
2.2.2. Electrochemical Measurements
Sensing of the three precursors has been achieved through their electrochemical oxido-reduction
using four different electrodes. The obtained cyclic voltammograms were then merged to build
specific signatures.
Cyclic voltammetry measurements were achieved simultaneously on the eight electrodes using
a multichannel potentiostat (µ 8000 from Dropsens, Llanera, Spain) at a 300 mV·s−1 scan rate. The
potential range used was +1.0 to −1.0 V for the gold-modified working electrodes and −1.0 to +0.5 V
for the palladium-, platinum-, and non-modified carbon working electrodes. In order to evaluate the
matrix effect, measurements were performed in different aqueous solution: (i) NaCl 0.1 M electrolyte
solution in milliQ water; (ii) tap drinking water; (iii) tap non-drinkable water; (iv) soap water (GEH
dish washing soap 0.1% v/v in tap drinking water); and (iv) artificial sewage water composed of tap
drinking water added of whey 500 mg/L, peptone 100 mg/L, Na2HPO4 54 mg/L, NH4Cl 178.3 mg/L,
sodium acetate 41.7 mg/L, NaCl 58.4 mg/L, CaCl2·2 H2O 14.7 mg/L, MgCl2·6H2O 20.3 mg/L, and KCl
7.4 mg/L.
2.2.3. Electrochemical Data Analysis
Partial least squares-discriminant analysis (PLS-DA) [21] was used as the pattern recognition
technique for the computation of the classification models applied to determine the predicted
probability of the presence of the three targets. The performance of each classification model was
evaluated on the basis of the following parameters:
 Sensitivity (SENS): the percentage of objects of each modelled class correctly accepted by the
class model.
 Specificity (SPEC): the percentage of objects of the other classes correctly rejected by the class
model.
 Efficiency (EFF): the geometric mean of sensitivity and specificity.
Once the optimal model for each target substance selected, the probability of the presence of the
different target substances for any new measurement was computed. All data analysis were
performed using the PLS Toolbox ver. 7.5 and ver. 7.8.2 (Eigenvector Research Inc., Wenatchee, WA,
USA) and all routines were written in MATLAB© platform 7.11 R2010b (The Mathworks Inc., Novi,
MI, USA).
Gold deposition
Platinum deposition
Palladium deposition
Figure 1.
The eight-electrode chip with its metal deposition, which will lead to the acquisition of an
electrochemical signature.
2.2.2. Electrochemical Measurements
Sensing of the three precursors has been achieved through their electrochemical oxido-reduction
using four different electrodes. The obtained cyclic voltammograms were then merged to build
specific signatures.
Cyclic voltammetry measurements were achieved simultaneously on the eight electrodes using
a multichannel potentiostat (
µ
8000 from Dropsens, Llanera, Spain) at a 300 mV
·
s
−1
scan rate.
The potential range used was +1.0 to
−
1.0 V for the gold-modified working electrodes and
−
1.0
to +0.5 V for the palladium-, platinum-, and non-modified carbon working electrodes. In order to
evaluate the matrix effect, measurements were performed in different aqueous solution: (i) NaCl 0.1 M
electrolyte solution in milliQ water; (ii) tap drinking water; (iii) tap non-drinkable water; (iv) soap
water (GEH dish washing soap 0.1% v/v in tap drinking water); and (iv) artificial sewage water
composed of tap drinking water added of whey 500 mg/L, peptone 100 mg/L, Na
2
HPO
4
54 mg/L,
NH
4
Cl 178.3 mg/L, sodium acetate 41.7 mg/L, NaCl 58.4 mg/L, CaCl
2·
2 H
2
O 14.7 mg/L, MgCl
2·
6H
2
O
20.3 mg/L, and KCl 7.4 mg/L.
2.2.3. Electrochemical Data Analysis
Partial least squares-discriminant analysis (PLS-DA) [
21
] was used as the pattern recognition
technique for the computation of the classification models applied to determine the predicted
probability of the presence of the three targets. The performance of each classification model was
evaluated on the basis of the following parameters:
•
Sensitivity (SENS): the percentage of objects of each modelled class correctly accepted by the
class model.
•
Specificity (SPEC): the percentage of objects of the other classes correctly rejected by the
class model.
•Efficiency (EFF): the geometric mean of sensitivity and specificity.
Once the optimal model for each target substance selected, the probability of the presence of the
different target substances for any new measurement was computed. All data analysis were performed
using the PLS Toolbox ver. 7.5 and ver. 7.8.2 (Eigenvector Research Inc., Wenatchee, WA, USA) and all
routines were written in MATLAB© platform 7.11 R2010b (The Mathworks Inc., Novi, MI, USA).

Challenges 2017,8, 10 4 of 11
2.2.4. Operational Setup
Real condition measurements were carried out with the following operational setup composed of:
•
A multichannel potentiostat (Figure 2A) connected through GSM (Global System for Mobile
Communication) to the central control system (using TEKEVER communication tool box [22]).
•
A wetting system fitting the sewage system and in which the eight-electrode array is inserted
(Figure 2B). This setup enabled the electrode array to be immersed in flushing water.
Challenges 2017, 8, 10 4 of 12
2.2.4. Operational Setup
Real condition measurements were carried out with the following operational setup composed
of:
 A multichannel potentiostat (Figure 2a) connected through GSM (Global System for Mobile
Communication) to the central control system (using TEKEVER communication tool box [22]).
 A wetting system fitting the sewage system and in which the eight-electrode array is inserted
(Figure 2b). This setup enabled the electrode array to be immersed in flushing water.
Figure 2. Views of the operational electrochemical setup composed by the multichannel potentiostat
(a) and a wetting system (b).
3. Results and Discussion
Electrochemical sensors combined with cyclic voltammetry methods generate a specific signal
from oxidation and reduction of particular molecules. This signal may also vary with the electrode
composition but the differentiation between two chemicals is hardly possible using only one
voltammogram. To solve this issue, a signature of several voltammograms obtained on different
surfaces, used like a compound “fingerprint”, may drastically increase the result’s specificity. In the
present study, an electrochemical chip has been developed with the objective of being able to
discriminate between different chemicals.
3.1. Electrodeposition
Modifying the electrode network in an addressed manner was a key step in the present study to
generate different measurement surfaces useful for signature determination. Three metals, gold,
platinum, and palladium, were chosen for this purpose according to their well-known
electrochemical properties and stability as working electrodes [23]. The electrodeposition of each
metal was then controlled and optimized in order to obtain a homogenous coverage of the working
electrodes (Figure 3A–D.). Each deposition process was validated through the analysis of the I = f(t)
curves, but also thanks to scanning electronic microscopy observations.
A more complete characterization of the modified surfaces was also realized using scanning
electron microscopy observation coupled with energy dispersive X-ray spectroscopy analysis (Figure
3I–L). The surface roughness was found to be really high, leading to potentially high active areas, a
good point when looking at electrochemical signal optimization. Indeed, the higher the specific area,
the higher the current per surface unit recorded for one compound. The surfaces’ observations also
demonstrated the presence of metallic particles’ multilayers on the working electrode area. The
elemental analysis enabled the validation of the full coverage of the surface by the particles, with an
extremely low carbon signal coming from the underlying electrode, proof of the total coverage of the
surface during the electrodeposition process. That point was also a good characteristic since each
electrode will then have a specific signal, only coming from the particular deposited metal, without
any mixed multi-material electrochemical signal.
Figure 2.
Views of the operational electrochemical setup composed by the multichannel potentiostat
(A) and a wetting system (B).
3. Results and Discussion
Electrochemical sensors combined with cyclic voltammetry methods generate a specific signal
from oxidation and reduction of particular molecules. This signal may also vary with the electrode
composition but the differentiation between two chemicals is hardly possible using only one
voltammogram. To solve this issue, a signature of several voltammograms obtained on different
surfaces, used like a compound “fingerprint”, may drastically increase the result’s specificity. In the
present study, an electrochemical chip has been developed with the objective of being able to
discriminate between different chemicals.
3.1. Electrodeposition
Modifying the electrode network in an addressed manner was a key step in the present study
to generate different measurement surfaces useful for signature determination. Three metals, gold,
platinum, and palladium, were chosen for this purpose according to their well-known electrochemical
properties and stability as working electrodes [
23
]. The electrodeposition of each metal was then
controlled and optimized in order to obtain a homogenous coverage of the working electrodes
(Figure 3A–D.). Each deposition process was validated through the analysis of the I = f(t) curves,
but also thanks to scanning electronic microscopy observations.
A more complete characterization of the modified surfaces was also realized using scanning
electron microscopy observation coupled with energy dispersive X-ray spectroscopy analysis
(Figure 3I–L). The surface roughness was found to be really high, leading to potentially high active
areas, a good point when looking at electrochemical signal optimization. Indeed, the higher the
specific area, the higher the current per surface unit recorded for one compound. The surfaces’
observations also demonstrated the presence of metallic particles’ multilayers on the working electrode
area. The elemental analysis enabled the validation of the full coverage of the surface by the particles,
with an extremely low carbon signal coming from the underlying electrode, proof of the total coverage
of the surface during the electrodeposition process. That point was also a good characteristic since each
electrode will then have a specific signal, only coming from the particular deposited metal, without any
mixed multi-material electrochemical signal.

Challenges 2017,8, 10 5 of 11
Challenges 2017, 8, 10 5 of 12
Figure 3. Optical microscopy images of (A) palladium; (B) platinum; (C) gold; and (D) bare carbon
electrodes. SEM observation of the surface of (E) palladium; (F) platinum; (G) gold; and (H) bare
carbon electrodes. Bottom: EDX analysis of the surfaces (I–L).
3.2. Signature Definition and Data Analysis
Using the individual signals of the different electrodes for each compound, an approach based
on the creation of a unique signature for each target was applied so as to take full advantage of the
information provided by the different electrodes on the same sample. To this aim, the signals
acquired with the four electrode surfaces were merged together as a unique new signal after a block
of data was scaled to unit variance. This approach led to a signature with equal importance of the
information provided by the different electrodes. A schematic representation of this approach is
shown in Figure 4.
These signatures were acquired to generate an experimental database of target compounds in
multiple aqueous environments, such as distilled water, drinking water, non-drinking water, soapy
water, and artificial waste water. This approach was used to build a stronger database than just a
database produced in pure water and to insert in the model the expected variations related to
potential matrix effects.
In addition to the three main target substances (B01, B08, and B15), data were also acquired on
additional target substances B04, B05, and B11, and on the interfering species Int09, representative of
common cleaning products (once again kept as confidential).
The composition of the dataset used for the calculation of the PLS-DA classification models of
target B01, B08, and B15 is reported in Table 1.
Figure 3.
Optical microscopy images of (
A
) palladium; (
B
) platinum; (
C
) gold; and (
D
) bare carbon
electrodes. SEM observation of the surface of (
E
) palladium; (
F
) platinum; (
G
) gold; and (
H
) bare
carbon electrodes. Bottom: EDX analysis of the surfaces (I–L).
3.2. Signature Definition and Data Analysis
Using the individual signals of the different electrodes for each compound, an approach based
on the creation of a unique signature for each target was applied so as to take full advantage of the
information provided by the different electrodes on the same sample. To this aim, the signals acquired
with the four electrode surfaces were merged together as a unique new signal after a block of data was
scaled to unit variance. This approach led to a signature with equal importance of the information
provided by the different electrodes. A schematic representation of this approach is shown in Figure 4.
These signatures were acquired to generate an experimental database of target compounds
in multiple aqueous environments, such as distilled water, drinking water, non-drinking water,
soapy water, and artificial waste water. This approach was used to build a stronger database than
just a database produced in pure water and to insert in the model the expected variations related to
potential matrix effects.
In addition to the three main target substances (B01, B08, and B15), data were also acquired on
additional target substances B04, B05, and B11, and on the interfering species Int09, representative of
common cleaning products (once again kept as confidential).
The composition of the dataset used for the calculation of the PLS-DA classification models of
target B01, B08, and B15 is reported in Table 1.

Challenges 2017,8, 10 6 of 11
Challenges 2017, 8, 10 6 of 12
Figure 4. Electrochemical data analysis approach. From each set of four voltammograms obtained
from the four different electrodes, voltammograms were unfolded, auto-scaled, and merged to
generate a unique signature.
Table 1. Composition of the training and test sets used to compute and validate the classification
models of the EC sensor.
Target
Class
Number of Spectra
for Training
Number of Spectra
for Test
Total Number
of Spectra
B01
Target
25
11
36
No Target
187
122
309
B08
Target
33
22
55
No Target
179
111
290
B15
Target
33
21
54
No Target
179
112
291
For explanatory purpose, the signatures obtained during the training step for each of the three
target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the
experiments performed in water, soapy water, and artificial waste water. The peak positions and
intensity of each target using each electrode are given in Supplementary Information Table S1.
Figure 4.
Electrochemical data analysis approach. From each set of four voltammograms obtained from
the four different electrodes, voltammograms were unfolded, auto-scaled, and merged to generate a
unique signature.
Table 1.
Composition of the training and test sets used to compute and validate the classification
models of the EC sensor.
Target Class Number of Spectra
for Training
Number of Spectra
for Test
Total Number
of Spectra
B01 Target 25 11 36
No Target 187 122 309
B08 Target 33 22 55
No Target 179 111 290
B15 Target 33 21 54
No Target 179 112 291
For explanatory purpose, the signatures obtained during the training step for each of the three
target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the
experiments performed in water, soapy water, and artificial waste water. The peak positions and
intensity of each target using each electrode are given in Supplementary Materials Table S1.

Challenges 2017,8, 10 7 of 11
Challenges 2017, 8, 10 7 of 12
Figure 5. Signatures obtained during the training step for the three target compounds. Measurements
presented here were performed in water, soapy water, and artificial waste water.
3.3. Explosive Precursors’ Detection Using Pattern Recognition
The above mentioned database was then used to compute PLS-DA classification models for the
three target compounds. In this context, we performed a more comprehensive evaluation of the effect
of several spectra pre-treatment methods in removing the uninformative variation while retaining
the most informative one, so as to optimize model performance. To this aim, several PLS-DA models
have been calculated for each target, considering the different spectra pre-processing methods listed
in Table 2.
Table 2. The evaluated data pre-treatment.
PLS-DA Cycle
Spectra Pre-Treatment Method
1
None
2
Mean centre (MC)
3
Auto-scale (Auto)
4
Standard normal variate (SNV)
5
Standard normal variate (SNV) + MC
6
Standard normal variate (SNV) + Auto
The results obtained from this pre-treatment optimization step (data not shown for brevity) have
indicated that the highest efficiency values in cross-validation and in prediction were achieved by
applying pre-treatment 6: standard normal variate (SNV) followed by auto-scale. The statistical
results of the final models are reported in Table 3.
Table 3. Results of the classification models calculated on the final electrochemical database (i.e., pre-
treatment 6).
Target
Efficiency (%)
Cal.
(Calibration)
CV
(Cross-Validation)
Pred.
(Prediction)
B01
100.00
100.00
95.34
B08
100.00
92.15
99.55
B15
100.00
97.65
100.00
Figure 5.
Signatures obtained during the training step for the three target compounds. Measurements
presented here were performed in water, soapy water, and artificial waste water.
3.3. Explosive Precursors’ Detection Using Pattern Recognition
The above mentioned database was then used to compute PLS-DA classification models for the
three target compounds. In this context, we performed a more comprehensive evaluation of the effect of
several spectra pre-treatment methods in removing the uninformative variation while retaining the most
informative one, so as to optimize model performance. To this aim, several PLS-DA models have been
calculated for each target, considering the different spectra pre-processing methods listed in Table 2.
Table 2. The evaluated data pre-treatment.
PLS-DA Cycle Spectra Pre-Treatment Method
1 None
2Mean centre (MC)
3Auto-scale (Auto)
4Standard normal variate (SNV)
5Standard normal variate (SNV) + MC
6Standard normal variate (SNV) + Auto
The results obtained from this pre-treatment optimization step (data not shown for brevity) have
indicated that the highest efficiency values in cross-validation and in prediction were achieved by
applying pre-treatment 6: standard normal variate (SNV) followed by auto-scale. The statistical results
of the final models are reported in Table 3.
Table 3.
Results of the classification models calculated on the final electrochemical database
(i.e., pre-treatment 6).
Target Efficiency (%)
Cal. (Calibration) CV (Cross-Validation) Pred. (Prediction)
B01 100.00 100.00 95.34
B08 100.00 92.15 99.55
B15 100.00 97.65 100.00
The final classification model performance in discriminating one particular target with respect to
signals of the other targets and interfering species is shown in Figure 6. In particular, the predicted
values obtained by applying the classification models for targets B01, B08, and B15 are reported in

Challenges 2017,8, 10 8 of 11
Figure 6A–C, respectively. As can be seen, each of the targets was clearly classified above its predicting
threshold and with minimum false negative or positive errors. Indeed, only one false negative and one
false positive were observed for B01, while none were observed for the two other targets. Moreover,
different target preparations were also tested for B08 and B15, which led to correct classification of
the compounds. The classification model and data pre-treatment was then proved to be efficient and
robust enough to be tested in realistic conditions using the wetting setup described in Figure 2.
Challenges 2017, 8, 10 9 of 12
Figure 6. Predicted values obtained by applying on the test set the classification model for target B01
(A); target B08 (B); and target B15 (C).
Figure 6.
Predicted values obtained by applying on the test set the classification model for target B01
(A); target B08 (B); and target B15 (C).

Challenges 2017,8, 10 9 of 11
3.4. Realistic Conditions Testing
The final assessment of the capability of the electrochemical sensor to detect improvised explosive
precursors directly in sewage drain water from a simulated bomb factory has been performed using the
experimental setup described in Figure 2. For this setup, the electrochemical measurement is triggered
by the wetting of the electrode array following a flushing of waste water from a sink in which a vessel
used for IE preparation has been washed using local tap water. Here, concentrations up to 100 mM
of each of the compounds are usually found in water discharge. These experiments were performed
during improvised explosive preparation campaigns within the Italian Air Force base in Pratica di
Mare (Italy) and the Swedish Defense Research Agency (FOI) facility in Grindsjön (Sweden).
The results obtained are presented in Table 4. The results are given in terms of predicted
probability of the presence of each compound, but also in terms of a properly raised alarm (color code).
As can be seen, the majority of the tested scenarios was giving correct prediction of the waste water
content and enabled the raising of a proper alarm. Only three tests gave false positive results for B08
in the presence of B01, leading to a rising of the alarm for a wrong set on compounds, i.e., B01 + B08
instead of only B01.
Table 4.
Results obtained using realistic testing conditions. The green box indicates a correct
classification for alarm triggering of the system. A red box indicates a false positive alarm. An orange
box indicates an unclear classification resulting in a low confidence alarm.
Test Number Target Predicted Probability (%)
B01 B08 B15
1B15 0.00 0.00 100.00
B01 100.00 0.00 0.00
2 B08 0.00 93.84 0.00
3 B01 100.00 100.00 0.00
4 B15 0.00 0.00 100.00
5 B01 100.00 92.44 0.00
6 B15 0.00 0.00 100.00
7 Blank 0.00 0.31 0.00
8 Blank 0.00 0.31 0.00
9 B15 0.00 0.00 100.00
10 B08 28.35 99.88 0.00
11 Blank 0.00 0.16 0.00
12 B15 0.00 0.00 100.00
13 B01 100.00 100.00 0.00
14 Blank 0.00 0.66 0.82
15 B08 0.00 99.66 0.00
16 Blank 0.00 0.67 0.00
17 B08 0.00 93.10 0.00
18 B08 0.00 93.84 0.00
19 Blank 0.00 0.31 0.00
4. Conclusions
The developed electrochemical sensor showed a number of key features, including portability,
battery stand-alone operability, wireless operability, and possibility to be easily hidden in the
sewage system.
At the analytical level, it was successfully trained in the laboratory (data pre-treatment
optimization, model prediction training) and tested under relevant environmental conditions, i.e., in
the presence of interfering species and other potential targets. Its capability to detect the three
target compounds in waste water obtained during improvised explosive preparation has also been
demonstrated. It is finally worth mentioning that the electrochemical sensor was also successfully
tested, integrated in a network of sensors with an “expert system” providing not only detection at the
sensor level, but also data fusion of chemical information provided by the different sensors deployed.

Challenges 2017,8, 10 10 of 11
Supplementary Materials:
The following are available online at www.mdpi.com/2078-1547/8/1/10/s1, Table S1:
Position (potential) and amplitude (current) of the observed peaks for each target on each electrode.
Acknowledgments:
This work has been supported by the European Commission Program STREP-FP7-SEC-
2010-1-Bomb Factory Detection by Networks of Advanced Sensors grant agreement No. 261685. The authors
would like to thank Luigi Cassioli and Silvana Grossi of the Italian Air Force for their support during the tests
at the Pratica di Mare (Italy) base and Pedro Santo Antonio from TEKEVER, Obidos (Portugal), for developing
the software connecting the sensors of the BONAS network. The authors would also like to thank FOI (Swedish
Defence Research Agency) head of research and leading Swedish explosive expert Henrik Östmark.
Author Contributions:
CloéDesmet, Agnes Degiuli, Carlotta Ferrari, and Christophe Marquette conceived
and designed the experiments; CloéDesmet, Agnes Degiuli, and Carlotta Ferrari performed the
experiments; Carlotta Ferrari, Loic Blum, and Christophe Marquette analyzed the data; Carlotta Ferrari,
Francesco Saverio Romolo, CloéDesmet and Christophe Marquette wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Regulation (EU) No 98/2013 of the European Parliament and of the Council of 15 January 2013 on the Marketing
and Use of Explosives Precursors Text with EEA Relevance. Available online: http://eur-lex.europa.eu/legal-
content/EN/TXT/?uri=uriserv:OJ.L_.2013.039.01.0001.01.ENG (accessed on 11 Octorber 2013).
2.
King, J.D.; de los Santos, A. Development and evaluation of magnetic resonance technologies, particularly
NMR, for detection of explosives. Appl. Magn. Reson. 2004,25, 535–565. [CrossRef]
3. Barras, J.; Gaskell, M.J.; Hunt, N.; Jenkinson, R.I.; Mann, K.R.; Pedder, D.A.G.; Shilstone, G.N.; Smith, J.A.S.
Detection of ammonium nitrate inside vehicles by nuclear quadrupole resonance. Appl. Magn. Reson. 2004,
25, 411–437. [CrossRef]
4.
Moros, J.; Lorenzo, J.A.; Lucena, P.; Tobaria, L.M.; Laserna, J.J. Simultaneous raman spectroscopy-laser-
induced breakdown spectroscopy for instant standoff analysis of explosives using a mobile integrated
sensor platform. Anal. Chem. 2010,82, 1389–1400. [CrossRef] [PubMed]
5.
Ramírez, M.L.; Félix-Rivera, H.; Sánchez-Cuprill, R.A.; Hernández-Rivera, S.P. Thermal-spectroscopic
characterization of acetone peroxide and acetone peroxide mixtures with nitrocompounds. J. Therm.
Anal. Calorim. 2010,102, 549–555. [CrossRef]
6.
Cheng, S.; Dou, J.; Wang, W.; Chen, C.; Hua, L.; Zhou, Q.; Hou, K.; Li, J.; Li, H. Dopant-assisted negative
photoionization ion mobility spectrometry for sensitive detection of explosives. Anal. Chem.
2012
,85,
319–326. [CrossRef] [PubMed]
7.
Östmark, H.; Nordberg, M.; Carlsson, T.E. Stand-off detection of explosives particles by multispectral
imaging Raman spectroscopy. Appl. Opt. 2011,50, 5592–5599. [CrossRef] [PubMed]
8.
Johns, C.; Shellie, R.A.; Potter, O.G.; O’Reilly, J.W.; Hutchinson, J.P.; Guijt, R.M.; Breadmore, M.C.; Hilder, E.F.;
Dicinoski, G.W.; Haddad, P.R. Identification of homemade inorganic explosives by ion chromatographic
analysis of post-blast residues. J. Chromatogr. 2008,1182, 205–214.
9.
Hopper, K.G.; LeClair, H.; McCord, B.R. A novel method for analysis of explosives residue by simultaneous
detection of anions and cations via capillary zone electrophoresis. Talanta
2005
,67, 304–312. [CrossRef]
[PubMed]
10.
Hakonen, A.; Andersson, P.O.; Schmidt, M.S.; Rindzevicius, T.; Käll, M. Explosive and chemical threat
detection by surface-enhanced Raman scattering: A review. Anal. Chim. Acta
2015
,893, 1–13. [CrossRef]
[PubMed]
11.
Apodaca, C.D.; Pernites, R.B.; del Mundo, F.R.; Advincula, R.C. Detection of 2,4-dinitrotoluene (DNT) as a
model system for nitroaromatic compounds via molecularly imprinted short-alkyl-chain SAMs. Langmuir
2011,27, 6768–6779. [CrossRef] [PubMed]
12. Wang, J. Microchip devices for detecting terrorist weapons. Anal. Chim. Acta 2004,507, 3–10. [CrossRef]
13.
Singh, S. Sensors—An effective approach for the detection of explosives. J. Hazard. Mater.
2007
,144, 15–28.
[CrossRef] [PubMed]
14.
Liu, J.; Li, Y.; Huang, X.; Zhu, Z. Tin oxide nanorod array-based electrochemical hydrogen peroxide biosensor.
Nanoscale Res. Lett. 2010,5, 1177–1181. [CrossRef] [PubMed]
15.
Bromberg, A.; Mathies, R.A. Homogeneous immunoassay for detection of TNT and its analogues on a
microfabricated capillary electrophoresis chip. Anal. Chem. 2003,75, 1188–1195. [CrossRef] [PubMed]

Challenges 2017,8, 10 11 of 11
16.
Charles, P.T.; Adams, A.A.; Howell, P.B.; Trammell, S.A.; Deschamps, J.R.; Kusterbeck, A.W.
Fluorescence-based sensing of 2,4,6-trinitrotoluene (TNT) using a multi-channeled poly(methyl methacrylate)
(PMMA) microimmunosensor. Sensors 2010,10, 876–889. [CrossRef] [PubMed]
17. Wang, J. Electrochemical sensing of explosives. Electroanalysis 2007,19, 415–423. [CrossRef]
18.
Chen, W.; Cai, S.; Ren, Q.; Wen, W.; Zhao, Y. Recent advances in electrochemical sensing for hydrogen
peroxide: A review. Analyst 2012,137, 49–58. [CrossRef] [PubMed]
19.
Yang, Q.; Liang, Y.; Zhou, T.; Shi, G.; Jin, L. TNT determination based on its degradation by immobilized
HRP with electrochemical sensor. Electrochem. Commun. 2008,10, 1176–1179. [CrossRef]
20.
Corgier, B.P.; Li, F.; Blum, L.J.; A, C. On-chip chemiluminescent signal enhancement using nanostructured
gold-modified carbon microarrays. Langmuir 2007,23, 8619–8623. [CrossRef] [PubMed]
21. Barker, M.; Rayens, W.S. Partial least squares for discrimination. J. Chemom. 2003,17, 166–173. [CrossRef]
22.
Ferrari, C.; Ulrici, A.; Romolo, F. Expert system for bomb factory detection by networks of advance sensors.
Challenges 2017,8, 1. [CrossRef]
23.
Chandler, G.K.; Genders, J.D.; Pletcher, D. Electrodes based on noble metals. Platin. Met. Rev.
1997
,41, 54–63.
©
2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).