**Characterization of Unpleasant Odors in Poultry Houses Using Metal Oxide Semiconductor-Based Gas Sensor Arrays and Pattern Recognition Methods †**

**Mohammed Moufid 1,2, Carlo Tiebe 3, Nezha El Bari 2, Matthias Bartholmai <sup>3</sup> and Benachir Bouchikhi 1,\***


**Abstract:** In this study, the ability of an electronic nose developed to analyze and monitor odor emissions from three poultry farms located in Meknes (Morocco) and Berlin (Germany) was evaluated. Indeed, the potentiality of the electronic nose (e-nose) to differentiate the concentration fractions of hydrogen sulfide, ammonia, and ethanol was investigated. Furthermore, the impact change of relative humidity values (from 15% to 67%) on the responses of the gas sensors was reported and revealed that the effect remained less than 0.6%. Furthermore, the relevant results confirmed that the developed e-nose system was able to perfectly classify and monitor the odorous air of poultry farms.

**Keywords:** poultry odorous air monitoring; electronic nose; gas sensors; pattern recognition methods

#### **1. Introduction**

Unpleasant odors are an inherent part of poultry production. They come from the wastes and emissions of animals. In addition, poultry farms in close proximity to the population generate volatile organic compounds (VOCs) in the air, resulting in a foul odor similar to that of rotten eggs and waste [1]. Moreover, odor nuisance from poultry farms has raised serious concerns about the quality of human life worldwide. Unpleasant smelling air affects the mental and physical health of the population and causes anger [2]. Similarly, the health of hens and farm workers can be threatened by the malodorous chemical compounds (such as hydrogen sulfide and ammonia) emanating from poultry farms [3]. Therefore, appropriate methods and techniques are needed to characterize and monitor odorous air samples, thereby determining the impacts of smelly air on chickens, humans, and the agricultural environment.

Although olfactometric techniques are the most widely used to analyze odorous air based on the perception of a group of human sniffers, these techniques are very expensive and do not provide information on chemical composition [4]. Analytical methods are widely used for the quantitative analysis of odorous air samples to identify unknown organic compounds and their concentration [5]. However, they require a qualified operator, and are expensive, time-consuming, and non-portable. The problems associated with the use of conventional odor air analysis methods could be replaced by the application of faster and cheaper e-nose technology. Electronic noses are devices equipped with an array of gas sensors combined with pattern recognition methods that provide a specific signature of the analyte [6,7]. The last few decades have seen a significant increase in interest in chemical sensors, which is reflected in the growing number of papers and conferences on this topic. For this purpose, these instruments are used in various applications for routine,

**Citation:** Moufid, M.; Tiebe, C.; El Bari, N.; Bartholmai, M.; Bouchikhi, B. Characterization of Unpleasant Odors in Poultry Houses Using Metal Oxide Semiconductor-Based Gas Sensor Arrays and Pattern Recognition Methods. *Chem. Proc.* **2021**, *5*, 52. https://doi.org/ 10.3390/CSAC2021-10481

Academic Editor: Elisabetta Comini

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

rapid, and inexpensive assessment related to the environmental sector [8,9]. Similarly, in recent years, odor emissions from livestock farms have received increased attention due to their large number resulting in the production of high concentrations of hydrogen sulfide and ammonia [10,11]. Electronic noses are also applied in other fields, including biomedical [12,13], pharmaceutical [14], food [15], and security [16].

In this work, the ability of an electronic nose to discriminate malodorous VOCs from three poultry farm sites was investigated. In parallel, the monitoring of malodorous air emissions from a poultry farm as a function of the time and date of collection was carried out. In addition, the effect of relative humidity on the response of the gas sensors was checked. The sensitivity to hydrogen sulfide, ammonia, and ethanol was tested. Pattern recognition methods such as principal components analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) were used for processing the data from gas sensor responses.

#### **2. Materials and Methods**

#### *2.1. Odorous Air Samples Collection*

Odorous air samples were collected using 2L Tedlar bags in three poultry sheds located in the Faculty of Sciences of Meknes (FSM), as well as in the agglomeration of Meknes (Morocco) and in Berlin (Germany). In parallel, to verify the ability of the e-nose to monitor odorous air samples from a poultry farm, odorous air samples were collected from the poultry farm of Meknes at different times and on three different days, with an interval of two days of one week. In total, 126 odorous air samples were collected (14 samples from each time collection performed at 09:00 a.m., 12:00 p.m., and 18:00 p.m.).

#### *2.2. Gas Sensor System*

The developed electronic nose system consists of six MQ-type chemical sensors (MQ-3, MQ-4, MQ-5, MQ-8, MQ-9, and MQ-135) from Winsen Electronics Technology Co., Ltd. (Zhengzhou, China) (Figure 1). The sensor chamber also contains a relative humidity sensor (HIH 4000) and a temperature sensor (LM35) to monitor the environmental conditions during measurements. All the sensors were installed in a Teflon chamber with a volume of 270 cm3. The collected odorous air samples were transferred to the sensor chamber using a Tedlar bag and a micro air pump. The sensor responses were acquired by NI-USB 6212 data acquisition from National Instruments (Austin, TX, USA). It allows signal conversion and preparation for further analysis by changing the analog signal produced by the sensors into its discrete digital representation.

**Figure 1.** Electronic nose system developed for odorous air analysis and monitoring.

#### *2.3. Sensing Measurements*

During analysis, the samples were pumped for 5 min into the sensor chamber at a flow rate of 250 mL/min. The data were acquired every second. After each measurement, synthetic air was injected into the sensor chamber for 5 min to clean the surface of the sensors to return to their baselines.

The concentration fractions of hydrogen sulfide (6 ppm), ammonia (7 ppm), and ethanol (3 ppm) were adjusted with the Gas Mixing System (GMS) of BAM, Berlin, Germany.

The temperature measured inside the sensor chamber during the measurements was approximately 27.5 ± 3.1 ◦C.

#### *2.4. Data Analysis*

2.4.1. Features Extraction

In this study, three features were extracted from the response of each gas sensor:


Eighteen variables were defined the developed system (6 sensors × 3 extracted features).

#### 2.4.2. Pattern Recognition Methods

The extracted features were treated by using pattern recognition methods (PCA, DFA, and SVMs) to estimate the performance of the e-nose to classify and monitor odorous air samples from a poultry farm.

The aim of PCA is the multidimensionality reduction of a dataset by finding new orthogonal directions (principal components) which contain the maximum information. The main advantage of PCA is the ability to present the results on two- or three-dimensional graphs. Groups of points can be visualized on the plots, which makes it possible to assess the contribution of the sample to a particular group. The PCA algorithm generates linear combinations of principal components [17].

DFA is a linear method, but it differs from PCA in that it utilizes the cluster information that was given during the training (supervised method), while the PCA does not care about the relationship of the data points with the specified clusters. The DFA method helps to have the best discrimination by reducing distances between samples (variance within classes) and maximizing distances between clusters (variance between classes) [18].

SVMs objective is to increase the quantity called margin to distinguish clusters with a specific hyperplane. The margin is a distance calculated between the nearest points contained in different groups. SVMs have two techniques. The first is to consider one vs. one or one vs. all and the other is to use all the data in a single formulation. This work applied a 2nd degree polynomial kernel. Indeed, a leave-one-out cross-validation technique was used to determine the prediction accuracy. The second-order of a radial basis function (polynomial) kernel was employed to project the training data to a space that maximizes the margin hyper plane [19].

#### **3. Results and Discussion**

#### *3.1. Sensor Calibration through Relative Humidity Variation*

Since humidity has a great impact on the electrical conductivity of resistive gas sensors, it is necessary to calibrate them using different relative humidity values. Figure 2 represents the normalized conductance of the sensor arrays at three different relative humidity values. We can see from this figure that when the relative humidity values increase from 15% to 67%, the variation of the conductance does not exceed 0.6% for all the sensors. In conclusion, it can be noticed that the calibration of the gas sensors at the considered relative humidity values led to a slight difference in the responses of the gas sensor arrays.

**Figure 2.** Conductance changes of sensor arrays and relative humidity in function of measurement time by adjusting RH values from 15% to 67%.

#### *3.2. Sensing Behavior of Gas Sensor Arrays to Hydrogen Sulfide, Ammonia, and Ethanol at Room Temperature*

Ammonia and hydrogen sulfide are harmful gases generated during the bacterial decomposition of livestock manure [20]. Therefore, it is necessary to test the sensitivity of gas sensors using ammonia and hydrogen sulfide gases. Figure 3 shows a plot of the time dependence of the difference in conductance (G − G0) when the sensors are exposed to hydrogen sulfide (Figure 3a), ammonia (Figure 3b), and ethanol (Figure 3c) at fixed concentration fractions of 6, 7, and 3 ppm, respectively. It can be seen from this figure that all the gas sensors were sensitive to the three tested synthetic gases, except for the MQ-8 sensor. Furthermore, each gas sensor had a different response to the three gases studied, which means that the electronic nose was able to differentiate between these gases.

**Figure 3.** Conductance changes of gas sensor arrays in the presence of (**a**) hydrogen sulfide, (**b**) ammonia, and (**c**) ethanol with concentration fractions of 6, 7, and 3 ppm, respectively.

*3.3. Classification Results of the Odorous Air Samples Collected from Poultry Sheds and Clean Air* 3.3.1. Radar Plots

Figure 4 shows the results of the radar plot corresponding to the odorous air samples collected from three poultry sheds and synthetic air as the control. In this figure, it can be seen that the odorous air pattern (fingerprints) differed from one site to another. Indeed, the smallest one corresponds to the control.

**Figure 4.** Radar plots of poultry odorous air samples and synthetic air (control) expressed as the difference in conductance (ΔG = (G − G0)) extracted from gas sensor responses.

#### 3.3.2. PCA Classification

The PCA method was applied to the database gathered from gas sensor responses upon exposure to odorous air samples collected from the three poultry farms and synthetic air as a control. Figure 5 represents the projections of the experimental results onto a two-dimensional (2D) graph. In this figure, it can be seen that all the groups of odorous air samples are clearly separated, with no overlap with synthetic air (control). In fact, the first two principal components account for 94% of the data variance.

**Figure 5.** PCA plot performed on poultry odorous air samples and synthetic air by using the features (ΔG, area, and slope) extracted from gas sensor responses.

#### *3.4. Classification Results Depending on the Time and Date of Samples Collection* 3.4.1. DFA Classification

DFA was applied to the database gathered from the sensors' responses to verify the ability of the developed e-nose to monitor odorous air samples collected at different dates and times in a poultry farm in Meknes. Figure 6 shows the DFA plot with 89% of the data variance explained by the first two discriminant functions (DFs). It can be seen from this figure that all the clusters are separated from each other. Furthermore, DF1 discriminates odorous air samples based on the date of collection from the poultry shed, while DF2 separates them based on the time of collection. The DFA results prove that the e-nose system was capable of clearly discriminating odorous air samples from a poultry farm according to the date and time of collection.

**Figure 6.** DFA plot performed on odorous air samples collected at different times and dates on a poultry farm (Meknes) using the features (ΔG, area, and slope) extracted from gas sensor array responses.

#### 3.4.2. SVM Classification

An SVM is a supervised learning method. It was applied to the same dataset as DFA to verify the ability of the developed e-nose to monitor odorous air samples in a poultry farm from Meknes city. Table 1 shows the SVM confusion matrix for odorous air samples recognition. In this study, only two misclassified samples were observed in the data matrix. Therefore, a 98.41% accuracy in the recognition of the odorous air samples was achieved. This outcome was in good agreement with the obtained DFA results. This finding confirms that the e-nose system was able to monitor odorous air samples from a poultry farm.

**Table 1.** SVM classification results of odorous air samples collected at different times and dates on a poultry farm from Meknes using the features (ΔG, area, and slope) extracted from gas sensor responses (total score: 98.41%).


#### **4. Conclusions**

The present study demonstrated that the low-cost, portable, and easy-to-use e-nose system was able to distinguish odorous air samples from poultry sheds based on the sampling site, and also depending on the date and time of collection. The effect of relative humidity on gas sensor responses was also investigated and showed that when relative humidity increased from 15% to 67%, there was a slight difference in sensor responses that did not exceed 0.6%. Similarly, the sensitivity of the sensor array to hydrogen sulfide, ammonia, and ethanol was tested and showed that all gas sensors are sensitive to these three synthetic gases, with the exception of the MQ-8 sensor. Radar plots revealed a significant change in odorous air sample patterns based on the sampling sites. In addition, PCA showed that the e-nose system was able to clearly distinguish odorous air samples from three sites of poultry farms without any overlap with the unpolluted air samples (synthetic air) with 94% of the data variance. In order to monitor odorous air samples from a poultry farm according to their date and time of collection, the database was also processed by DFA and SVM. These two pattern recognition methods show a clear discrimination between the studied samples with a success rate of 89% and 98.41%, respectively. We can conclude that the developed e-nose system can be effectively used as a fast, easy-to-use, and inexpensive tool for the analysis and monitoring of odorous air samples from poultry farms.

**Author Contributions:** Investigation, Formal analysis, Methodology, software, Experimentation, Original Draft Preparation: M.M.; design of experiment for gas mixing system to investigate the sensor system in the laboratory, scientific exchange of the time-series data and coordination of the tasks in BAM: C.T.; Review & Editing: N.E.B. and M.B.; Conceptualization, Visualization, Supervision, Validation, Resources, Funding acquisition, Review & Editing: B.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** PMARS N◦2015-87 Moroccan–German scientific research cooperation.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** We would like to thank Moulay Ismaïl University of Meknes for financial support of the project "Research support". This work is funded by the federal Ministry of Education and Research (Germany) and Ministry of Higher Education, Scientific Research and Executive Training (Morocco) under the Framework program of Moroccan–German scientific research cooperation, project under grant agreement n◦ PMARS N◦2015-87. Additionally, we have to thank Michael Hofann and Jihed Ben Majed of BAM, Berlin, Germany for his help and advice.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Abstract* **Europium-Doped Ceria Nanocrystals as Nanozyme Fluorescent Probes for Biosensing †**

**Ali Othman, Akhtar Hayat and Silvana Andreescu \***

Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; aothman@clarkson.edu (A.O.); akhtarhayat@cuilahore.edu.pk (A.H.)

**\*** Correspondence: eandrees@clarkson.edu

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Molecular nanoprobes with intrinsic enzyme-like activity represent a new wave of technology for rapid and sensitive detection of molecular targets. This work reports synthesis and characterization of novel and well-dispersed europium-doped ceria nanocrystals (EuCe NCs) with self-integrated catalytic and fluorescence sensing functions. The NCs have an average size of ∼5 nm and exhibit bright and stable fluorescence for more than 6 months in aqueous media. Their dual cooperative function as both a catalyst and fluorescent probe was explored to develop a universally applicable fluorescence-based biosensing method to monitor enzyme reactions and quantitatively measure clinically relevant molecules. Sensing capabilities are demonstrated for detection of H2O2, glucose/glucose oxidase, lactate/lactate oxidase, phosphatase activity, and the catecholamine neurotransmitter, dopamine. Results indicate that EuCe NCs not only provide high enzyme-mimetic activity, but also impart direct fluorescence sensing ability enabling all-in-one recognition, catalytic amplification, and the detection of biomolecular targets. The EuCe nanozyme offers a stable alternative to the more complex systems based on the combined use of natural enzymes and fluorescent dyes. The high stability and fluorescence detection capabilities demonstrate that EuCe NCs have the potential to be used as a generic platform in chemical and biological sensing and bioimaging applications.

**Keywords:** ceria nanocrystals; europium doping; nanozyme; fluorescent probe; bioanalytical applications

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/CSAC2021-10549/s1.

**Citation:** Othman, A.; Hayat, A.; Andreescu, S. Europium-Doped Ceria Nanocrystals as Nanozyme Fluorescent Probes for Biosensing. *Chem. Proc.* **2021**, *5*, 53. https:// doi.org/10.3390/CSAC2021-10549

Academic Editor: Huangxian Ju

Published: 1 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

### *Proceeding Paper* **Electrode Modified with Tin(IV) Oxide Nanoparticles and Surfactants as Sensitive Sensor for Hesperidin †**

**Elvira Yakupova \* and Guzel Ziyatdinova**

Analytical Chemistry Department, Kazan Federal University, Kremleyevskaya, 18, 420008 Kazan, Russia; Ziyatdinovag@mail.ru

**\*** Correspondence: elviraeakupova96@mail.ru

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Tin(IV) oxide nanoparticles in combination with surfactants were used as a sensitive layer in a sensor for hesperidin. The effect of the surfactant's nature and concentration on the hesperidin response was evaluated. The best parameters were registered in the case of 500 μM cetylpyridinium bromide (CPB) as a dispersive agent. The SEM and electrochemical data confirmed the increase in sensor surface effective area and electron transfer rate. The sensor gave a linear response to hesperidin in the ranges of 0.10–10 and 10–75 μM with a detection limit of 77 nM. The approach was successfully tested on orange juices and validated using ultra-HPLC.

**Keywords:** electrochemical sensors; metal oxide nanoparticles; surfactants; flavonoids; food analysis

#### **1. Introduction**

Hesperidin (Figure 1) is the major flavonoid of *Citrus* L. fruits [1], with a wide spectrum of biological activity that results in its application in medicine [2]. However, it can show a pro-oxidant effect in high concentrations that is typical for phenolic antioxidants [3]. Therefore, simple, sensitive, and selective methods for hesperidin determination are required.

**Figure 1.** Hesperidin structure.

Electrochemical sensors can be successfully used for this purpose due to the ability of hesperidin to be oxidized at the electrode surface. The advantages of electrochemical methods, such as their simplicity, portability, and cost-efficiency in combination with reliability, sensitivity, and sufficient selectivity make them an attractive tool for practical applications. Nevertheless, hesperidin is almost disregarded as an analyte in electroanalysis compared to other natural flavonoids. Thus, the development of electrochemical sensors for hesperidin quantification is of interest from scientific and practical points of view.

The hanging drop mercury electrode has been used for hesperidin quantification [4,5]. Significant interference effects from a wide range of inorganic and organic compounds of different classes, as well as the toxicity of mercury, make these methods inapplicable in laboratory practice. Boron-doped diamond [6] and pencil graphite [7] electrodes allow

**Citation:** Yakupova, E.; Ziyatdinova, G. Electrode Modified with Tin(IV) Oxide Nanoparticles and Surfactants as Sensitive Sensor for Hesperidin. *Chem. Proc.* **2021**, *5*, 54. https:// doi.org/10.3390/CSAC2021-10615

Academic Editor: Ye Zhou

Published: 6 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

the limitations mentioned above to be partially overcome, although the selectivity of the hesperidin response is still insufficient. At the present time, chemically modified electrodes are applied in hesperidin analysis [8–15] since they provide higher sensitivity and selectivity of response. The analytical characteristics of hesperidin reported for the existing electrochemical sensors are presented in Table 1. Nevertheless, the selectivity of the sensor's response is often not considered or is insufficient, and there is limited applicability to real samples. The narrow linear dynamic range of the sensor response in some cases also complicates the analysis of real samples. Therefore, further development of electrochemical sensors for hesperidin that are free of these limitations is required.


**Table 1.** Figures of merit of electrochemical sensors for hesperidin.

<sup>1</sup> Boron-doped diamond electrode. <sup>2</sup> Adsorptive stripping square-wave voltammetry. <sup>3</sup> Pencil graphite electrode. <sup>4</sup> Differential pulse voltammetry. <sup>5</sup> Adsorptive anodic differential pulse voltammetry. <sup>6</sup> Basal-plane pyrolytic graphite electrode. <sup>7</sup> Glassy carbon electrode. <sup>8</sup> Linear sweep voltammetry. <sup>9</sup> Carbon paste electrode. <sup>10</sup> Amperometry.

Electrochemically inert metal oxide nanoparticles (CeO2, TiO2, ZnO, SnO2, Fe3O4, etc.) are prospective nanomaterials widely used for voltammetric sensor creation [16–22]. The combination of metal oxide nanoparticles with surfactants as dispersive agents has led to significant improvement in the voltammetric response of phenolic antioxidants [16–19], caused by stabilization of the nanoparticle dispersions on the one hand and, on the other hand, preconcentration of the analyte at the sensitive layer of the sensor surface via electrostatic or hydrophobic interactions. Another important aspect to be taken into account is the increase in the sensor conductivity due to the presence of surfactants since the metal oxide nanoparticles mentioned above are semiconductors. This type of electrode surface modifier has been successfully applied in the electroanalysis of natural phenolic antioxidants, particularly eugenol [16], thymol [17], quercetin and rutin [18], vanillin [19], and gallic acid [20,21], and in the simultaneous detection of synapic and syringic acids and rutin [22]. The sensors show high sensitivity and selectivity for the response and are easy to prepare, which is an advantage over other modified electrodes. No electrochemical sensors based on metal oxide nanoparticles for hesperidin determination have been reported to date, although it is of practical interest.

The current study was focused on the creation and application of a novel voltammetric sensor for hesperidin based on a glassy carbon electrode (GCE) modified with tin(IV) oxide nanoparticles and surfactants. Attention was paid to the evaluation of the effect of the surfactant's nature and concentration on the hesperidin voltammetric response. The electrodes under investigation were characterized by scanning electron microscopy (SEM) and electrochemical methods. The analytical aspects of hesperidin detection are discussed.

#### **2. Materials and Methods**

Hesperidin (94% purity) from Sigma (Steinheim, Germany) was used as a standard. A 0.40 mM stock solution was prepared in methanol (cp grade). Naringin (95% purity), 99% ascorbic and 98% caffeic acids, 95% quercetin trihydrate, and 85% morin hydrate from Sigma (Steinheim, Germany), 95% chlorogenic acid from Aldrich (Steinheim, Germany), and 97% rutin trihydrate from Alfa Aesar (Heysham, UK) were used in the interference test. For these, 10 mM stock solutions in methanol were prepared in 5.0 mL flasks. Lessconcentrated solutions were obtained by exact dilution.

Tin(IV) oxide nanoparticles (*D* < 100 nm) were purchased from Aldrich (Steinheim, Germany). Their 1 mg mL−<sup>1</sup> dispersions in water and the surfactants were prepared by sonication for 10 min in a WiseClean WUC-A03H ultrasonic bath (DAIHAN Scientific Co., Ltd., Wonju-si, Korea). Cetylpyridinium bromide (CPB) (98% purity), 97% *N*-lauroylsarcosine sodium salt (LSS), and Triton X-100 from Aldrich (Steinheim, Germany), 99% cetyltrimethylammonium bromide (CTAB) and Brij® 35 from Acros Organics (Geel, Belgium), sodium dodecylsulfate (SDS) (Ph. Eur.) from Panreac (Barcelona, Spain), and cetyltriphenylphosphonium bromide (CTPPB) synthesized in the Department of Organoelement Compounds Chemistry of Kazan Federal University were used as dispersive agents. Their 1.0 mM solutions were prepared in distilled water.

Other reagents were chemical grade purity. Double-distilled water was used for the measurements. The experiments were carried out at laboratory temperature (25 ± 2 ◦C).

Voltammetric measurements were carried out on an Autolab PGSTAT12 potentiostat/galvanostat (Eco Chemie B.V., Utrecht, The Netherlands) with GPES software, version 4.9.005. Electrochemical impedance spectroscopy was performed on an Autolab PGSTAT302N potentiostat/galvanostat with a FRA32M module (Eco Chemie B.V., Utrecht, Netherlands) and NOVA 1.10.1.9 software. A 10 mL glassy electrochemical cell with working GCE with a 7.07 mm<sup>2</sup> geometric surface area (BASi® Inc., West Lafayette, IN, USA) or a modified electrode, a silver–silver-chloride-saturated KCl reference electrode, and a platinum wire as the counter electrode was used.

An Expert-001 pH meter (Econix-Expert Ltd., Moscow, Russian Federation) equipped with the glassy electrode was used for pH measurements.

SEM was carried out on a MerlinTM high-resolution field-emission scanning electron microscope (Carl Zeiss, Oberkochen, Germany) at an accelerating voltage of 5 kV and an emission current of 300 pA.

#### **3. Results and Discussion**

#### *3.1. Voltammetric Characteristics of Hesperidin on Modified Electrodes*

The voltammetric behavior of hesperidin on bare GCE and the modified electrodes was studied in 0.1 M phosphate buffer at pH 7.0. Hesperidin is irreversibly oxidized in two steps. The second step is less pronounced. Therefore, the first oxidation peak was used (Table 2). Modification of the electrode surface with tin(IV) oxide nanoparticles provided an insignificant increase in the hesperidin oxidation currents. Furthermore, these values were still insufficient for sensitive hesperidin quantification. The use of surfactants provided stabilization of the nanoparticle dispersions and preconcentration of the hesperidin on the electrode surface via hydrophobic interaction, leading to an increase in the oxidation currents for all the surfactants under investigation. The effect of surfactant concentration in the range of 10–500 μM on the hesperidin response was evaluated. The oxidation potentials were cathodically shifted. The oxidation currents were statistically

significantly increased. Higher oxidation currents were obtained in the case of cationic surfactants. The best hesperidin response was registered on the sensor based on tin(IV) oxide nanoparticles dispersed in 500 μM cetylpyridinium bromide. This agrees well with literature data for a cerium-dioxide-nanoparticles-based sensor for eugenol [16] and a tin-dioxide-nanoparticles-based sensor for vanillin [19].

**Table 2.** Voltammetric characteristics of hesperidin on GCE and modified electrodes (*n* = 5; *p* = 0.95).


#### *3.2. Electrodes Characterization via SEM and Electrochemical Methods*

SEM shows the presence of spherical and rhomboid structures and their aggregates of size 30–200 nm for SnO2-H2O/GCE, in contrast to the relatively smooth surface of GCE (Figure 2a,b). The application of CPB as dispersive agent provides more uniform coverage consisting of spherical particles of size 20–40 nm forming a porous surface leading to an increase in the electrode surface area (Figure 2c).

**Figure 2.** SEM characterization of bare GCE (**a**), SnO2-H2O/GCE (**b**), and SnO2-CPB/GCE (**c**).

The electroactive surface area of the modified electrode is significantly increased compared with bare GCE (34.7 ± 0.3 mm2 for SnO2-CPB/GCE and 8.9 ± 0.3 mm2 for GCE), as confirmed by cyclic voltammetry (for SnO2-CPB/GCE) and chronoamperometry (for GCE and SnO2-H2O/GCE) using [Fe(CN)6] <sup>4</sup><sup>−</sup> ions as a standard. These data explain the increase in hesperidin oxidation currents on the modified electrode. Electrochemical impedance spectroscopy was performed in the presence of [Fe(CN)6] <sup>4</sup>−/3<sup>−</sup> as a redox probe at 0.23 V. Fitting of the impedance spectra using the Randles equivalent circuit showed 554-fold less charge transfer resistance for the modified electrode in comparison to GCE, indicating a dramatic increase in the electron transfer rate. The constant phase element value for SnO2-CPB/GCE was 4.7-fold higher than for the GCE due to the porous structure of the modified electrode and the increase in the surface total charge due to the presence of positively charged CPB. Thus, the developed sensor can be considered as a candidate for analytical applications.

#### *3.3. Analytical Characterization of the Sensor*

Hesperidin quantification using the developed sensor was performed in adsorptive differential pulse mode since surface-controlled electro-oxidation has been proved. The highest oxidation currents for hesperidin were obtained in 0.1 M phosphate buffer at pH 7.0. The variation of preconcentration time at the open circuit potential showed the highest oxidation currents for 120 s of accumulation. The evaluation of the effect of the pulse parameters showed that the best response was registered at a pulse amplitude of 100 mV and a pulse time of 50 ms.

The sensor gave a linear response to hesperidin in the ranges of 0.10–10 and 10–75 μM (Figure 3) with a detection limit of 77 nM. The calibration-plot parameters are presented in Table 3.

**Figure 3.** Baseline-corrected differential pulse voltammograms of hesperidin on SnO2-CPB/GCE in 0.1 M phosphate buffer at pH 7.0, with *t*acc = 120 s, Δ*E*pulse = 100 mV, *t*pulse = 50 ms, υ = 10 mV s<sup>−</sup>1.

**Table 3.** Hesperidin calibration-plot parameters (*I* =a+b*c*(M)).


The analytical characteristics obtained were significantly better [10,15] or comparable to other sensors based on the modified electrodes [12–14]. However, the sensor developed is simpler, relatively cheaper, and less tedious to prepare. The accuracy of the hesperidin determination was tested for the model solutions using the added–found method (Table 4). The recovery of 98.4–100% confirmed the high accuracy of the developed sensor. The relative standard deviation was less than 3.5%, indicating the absence of random errors of quantification and the high reproducibility of the sensor response since surface renewal was performed after each measurement.

**Table 4.** Quantification of hesperidin in model solutions (*n* = 5; *p* = 0.95).


The sensor selectivity in the presence of a 1000-fold excess of inorganic ions (K+, Mg2+, Ca2+, NO3 −, Cl−, and SO4 <sup>2</sup>−), glucose, rhamnose, and sucrose, as well as a 1000-fold excess of ascorbic acid was demonstrated. Another important advantage was the high selectivity to hesperidin in the presence of other flavonoids and phenolic acids. A 10-fold excess of naringin, quercetin, rutin, morin, and caffeic and chlorogenic acids, despite the fact that they are electroactive, did not result in an interference effect in the hesperidin response.

#### *3.4. Application to Real Samples*

The sensor's applicability to the analysis of real samples was successfully tested on orange juices. The following sample preparation was applied before the measurements: 6 mL of juice was mixed with 6 mL of methanol, sonicated for 15 min, and filtered through 0.45 μm pore size nylon membrane filters [23].

There is a well-defined oxidation peak of hesperidin on the differential pulse voltammograms of orange juices (commercial and fresh) that was confirmed by the standard addition method (Figure 4). Recovery values of 99–100% indicated the absence of matrix effects in the determination.

**Figure 4.** Typical baseline-corrected differential pulse voltammograms of orange juice at the SnO2- CPB/GCE in 0.1 M phosphate buffer at pH 7.0, with *t*acc = 120 s, Δ*E*pulse = 100 mV, *t*pulse = 50 ms, υ = 10 mV s<sup>−</sup>1.

The results of the hesperidin quantification in orange juices using the developed sensor are presented in Figure 5. Validation with independent ultra-HPLC with massspectrometric detection results was performed (Figure 5). The relative standard deviation for both methods did not exceed 2%, proving the absence of random errors. The *t*-test values (0.290–1.08) were less than the critical value of 2.45, confirming the absence of systematic errors in the determination. Similarly, the *F*-test results (1.17–2.57) were less than critical value of 6.59, indicating the uniform precision of the methods used.

**Figure 5.** Quantification of hesperidin in orange juices using electrochemical sensor and ultra-HPLC.

#### **4. Conclusions**

A sensor based on tin(IV) oxide nanoparticles and CPB provide an improvement in the voltammetric and analytical characteristics of hesperidin. The surfactant provides stabilization of the nanomaterial dispersion and the accumulation of analyte on the sensor surface. The novel voltammetric sensor is highly sensitive, selective, and reliable and can be recommended for the preliminary screening of citrus juices as an alternative to chromatography.

**Author Contributions:** Conceptualization, G.Z.; methodology, G.Z. and E.Y.; investigation, E.Y.; writing—original draft preparation, G.Z.; writing—review and editing, G.Z.; visualization, G.Z. and E.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors thank Irina Galkina (Department of High Molecular and Organoelement Compounds, Kazan Federal University) for the synthesis and granting of CTPPB, Rustam Davletshin (Department of High Molecular and Organoelement Compounds, Kazan Federal University) for the chromatographic measurements, and Aleksei Rogov (Laboratory of Scanning Electron Microscopy, Interdisciplinary Center for Analytical Microscopy, Kazan Federal University) for the SEM measurements.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Optical Biosensor for the Detection of Hydrogen Peroxide in Milk †**

**Helena Vasconcelos 1,2,\*, Ana Matias <sup>2</sup> , Pedro Jorge <sup>2</sup> , Cristina Saraiva <sup>1</sup> , João Mendes <sup>2</sup> , João Araújo 2, Bernardo Dias 2, Paulo Santos <sup>2</sup> , José M. M. M. Almeida 2,3 and Luís C. C. Coelho <sup>2</sup>**


**Abstract:** Over the years, the food industry's concern to provide safe food that does not cause harm or illness to consumers has increased. The growing demand for the detection of compounds that can contaminate food is increasingly important. Hydrogen peroxide is frequently used as a substance to control the growth of microorganisms in milk, thus increasing its shelf life. Here, a strategy is presented for the detection of hydrogen peroxide as a milk adulterant, using a single shot membrane sensor. The lowest concentration measured with this technique was 0.002% *w*/*w* of H2O2 in semi-fat milk.

**Keywords:** chemiluminescence; hydrogen peroxide; optical sensor; food safety; food fraud; quality assessment

#### **1. Introduction**

Milk is one of the most complete foods for humans, containing nutrients including carbohydrates, proteins, fats, minerals, and vitamins [1].

Owing to its rich composition, milk becomes a substrate for the growth of undesirable microorganisms that can easily deteriorate the product. To prevent this from happening, prohibited substances are fraudulently added [2]. Hydrogen peroxide (H2O2), hypochlorite, formaldehyde, potassium dichromate, and salicylic acid are examples of substances used as adulterants that need monitoring and quality control as they are toxic to humans [3].

In the case of H2O2, it is widely used in the dairy industry as an antimicrobial agent, thus helping to preserve raw milk in the absence of refrigeration [4]. Despite its conventional use, when added to milk, H2O2 can cause a decrease in the nutritional value of the food due to the destruction of vitamins A and E, which generate reactive and cytotoxic oxygen species, including hydroxyl radicals, that can initiate oxidation and damage nucleic acids, lipids, and proteins. Consequently, when ingested, milk can lead to negative effects on the health of the population, especially in immunocompromised people [2,4].

In the USA, hydrogen peroxide is used in cheese production in concentrations up to 0.05% *w*/*w*, however, in other countries, its addition is prohibited due to its toxic effects. A peroxide concentration > 0.1% *w*/*w* has been proven to induce cancer in the duodenum of mice and present short-term genotoxicity [3].

Here, a study is presented for the detection and quantification of H2O2 using a chemiluminescence technique. A small, low-cost hydroxyethyl cellulose sensitive membrane combined with a high-sensitive photodetector is used to measure H2O2 concentrations in semi-fat milk samples.

**Citation:** Vasconcelos, H.; Matias, A.; Jorge, P.; Saraiva, C.; Mendes, J.; Araújo, J.; Dias, B.; Santos, P.; Almeida, J.M.M.M.; Coelho, L.C.C. Optical Biosensor for the Detection of Hydrogen Peroxide in Milk. *Chem. Proc.* **2021**, *5*, 55. https:// doi.org/10.3390/CSAC2021-10466

Academic Editors: Huangxian Ju and Elena Benito Peña

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **2. Materials and Methods**

The sensing methodology is based on the detection of a luminescence signal from the chemical reaction within a solid membrane produced with hydroxyethyl cellulose (HEC, Sigma Aldrich, Taufkirchen, Germany), luminol, sodium phosphate, cobalt (II) chloride hexahydrate, sodium lauryl sulphate (SLS), and ethylenediaminetetraacetic acid (EDTA).

The procedure established by Omanovic-Miklicanin [5] was refined to establish experimental protocols. For the determination of H2O2 in very low concentrations, the sensor sensitivity should be as high as possible. Therefore, the systematic optimization of the membrane was necessary. Only one constituent was varied at a time, keeping the remaining constituents unchanged. After membrane optimization, the final concentrations of these constituents were set to luminol (0.2 mg), sodium phosphate (8.6 mg), SLS (60 μL, 34.36 mmol/L), cobalt hydroxide (100 μL, 5.0 mmol/L), EDTA (2 μL, 20 μmol/L), and HEC (150 mg) was added to 10 mL of Milli-Q® water.

The membrane solution was placed on a magnetic stirrer for 30 min. Individual 3D printed cups were used, and 1000 μL of membrane solution was added and dried for 4 h (T = 70 ◦C). After drying, the membranes were stored in a desiccator under a vacuum. For the measurement procedure, the membrane was placed directly onto the membrane holder on top of the detector. The light emission was measured by adding 500 μL of the sample solution.

For straight and rapid spectrophotometric H2O2 detection, a detection module was built containing a highly sensitive detection system with a photodiode (model S8746-01 Hamamatsu Japan), a dedicated amplification system with variable gain, and an embed controlling unit. The sensitive optoelectronic system was isolated inside of a custom-made 3D printed case allowing the easy replacement of the sensing membrane and allowing the sample pipetting, preventing the detection of the ambient light. This module was powered with a low noise power source, and the data was acquired and analyzed with a user-friendly graphical interface (GUI) and a raspberry pi (Figure 1).

**Figure 1.** Schematic diagram of the analyte detection.

#### **3. Results and Discussion**

Semi-fat milk samples were adulterated with H2O2 concentrations from 0.001% *w*/*w* to 0.006% *w*/*w* by diluting a standard 30% *w*/*w* solution of H2O2. The variation of the chemiluminescent intensity is presented in Figure 2 for all samples, together with the time integral of the decaying chemiluminescent signal for each H2O2 concentration.

**Figure 2.** (**a**) Variation of the intensity of the light emission for the concentrations of 0.002, 0.004, and 0.006% *w*/*w* as a function of time; (**b**) Time integral of the decay time for each H2O2 concentration.

Taking into consideration that 0.05 % *w*/*w* of H2O2 is the defined limit for the FDA in milk for cheese production [6], the developed sensor would be suitable for determinations of H2O2 as a fraud controller in milk samples within the legal limits of different countries. Moreover, to achieve a more practical approach to the commonly time-consuming sample preparation methods, the pre-treatment step was successfully eliminated. In fact, the optimized sensor requires minimal solvent use and waste production. When compared with other methods available for the determination of H2O2 presence in milk, this portable biosensor is an easy and reliable method that ensures the required sensitivity while offering a low time of analysis and no need for additional laboratory equipment.

The methodology developed and optimized demonstrates that it is possible to detect very low concentrations of H2O2 (down to 0.001 % *w*/*w* in an aqueous system). As the H2O2 concentration increased, the intensity of the emitted light and the reaction time increased. Low limits of detection were achieved, thus indicating the applicability of this assay to real samples exhibiting the required sensitivity for the analytical determination of H2O2 in biological samples such as milk.

In this work, the reaction of H2O2 and luminol catalyzed by cobalt hydroxide was used to detect H2O2 in milk; however, another spectrophotometric method was described by Lima et al. [2] for the detection of H2O2 in milk, using the reaction between hydrogen peroxide and guaiacol, catalyzed by peroxidase, producing a red product, where a low detection limit was obtained.

#### **4. Conclusions**

The proposed sensor provided to be a rapid, cost-effective, and environmentally friendly approach for the determination of hydrogen peroxide as a milk adulterant. This optimized and validated method has a very good linearity range when the sample is in its liquid state, where concentrations of H2O2 as low as 0.001% *w*/*w* can be detected with good repeatability. As a practical application for this methodology under controlled conditions, an adulterated milk sample was analyzed. Concentrations of H2O2 of 0.002% *w*/*w* to 0.006% were detected, and the method was calibrated for semi-fat milk, proving that the limit of detection and linearity range of the proposed method are suitable for the analysis of milk samples in loco, which can add value to the food fraud department. Moreover, the reagents required are commonly used in analytical laboratories, are inexpensive, and can be consumed in low amounts (500 μL), thus resulting in negligible and nontoxic waste generation. In addition to the mentioned advantageous features, the proposed method validation is comparable to those found in the literature.

**Supplementary Materials:** The supporting information can be downloaded at: https://www.mdpi. com/article/10.3390/CSAC2021-10466/s1.

**Author Contributions:** Writing—review and editing, H.V., A.M., J.M., J.A., B.D. and P.S.; supervision, L.C.C.C., P.J., C.S. and J.M.M.M.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. Helena Vasconcelos acknowledges the support from FCT grant SFRH/BD/120064/2016 and Luís Coelho acknowledges the support from FCT research contract grant CEECIND/00471/2017.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** This work was financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. Helena Vasconcelos acknowledges the support from FCT grant SFRH/BD/120064/2016 and Luís Coelho acknowledges the support from FCT research contract grant CEECIND/00471/2017.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Locally Linear Embedding as Nonlinear Feature Extraction to Discriminate Liquids with a Cyclic Voltammetric Electronic Tongue †**

**Jersson X. Leon-Medina 1,\* , Maribel Anaya <sup>2</sup> and Diego A. Tibaduiza <sup>3</sup>**


**Abstract:** Electronic tongues are devices used in the analysis of aqueous matrices for classification or quantification tasks. These systems are composed of several sensors of different materials, a data acquisition unit, and a pattern recognition system. Voltammetric sensors have been used in electronic tongues using the cyclic voltammetry method. By using this method, each sensor yields a voltammogram that relates the response in current to the change in voltage applied to the working electrode. A great amount of data is obtained in the experimental procedure which allows handling the analysis as a pattern recognition application; however, the development of efficient machinelearning-based methodologies is still an open research interest topic. As a contribution, this work presents a novel data processing methodology to classify signals acquired by a cyclic voltammetric electronic tongue. This methodology is composed of several stages such as data normalization through group scaling method and a nonlinear feature extraction step with locally linear embedding (LLE) technique. The reduced-size feature vector input to a *k*-Nearest Neighbors (*k*-NN) supervised classifier algorithm. A leave-one-out cross-validation (LOOCV) procedure is performed to obtain the final classification accuracy. The methodology is validated with a data set of five different juices as liquid substances.Two screen-printed electrodes voltametric sensors were used in the electronic tongue. Specifically the materials of their working electrodes were platinum and graphite. The results reached an 80% classification accuracy after applying the developed methodology.

**Keywords:** electronic tongue; locally linear embedding; cyclic voltammetry; *k*-Nearest Neighbors; classification; machine learning

#### **1. Introduction**

Discriminating between different types of liquid substance is a daily task in the food industry. This procedure can be used to preserve the flavor of a product, identify adulterations, confirm the presence of a specific liquid, among others [1]. Generally, the analysis of liquid food products is carried out using a panel of previously trained experts [2] who allow tasting and identifying a specific flavor. This through the training of the human sense of taste. However, over time this ability may be deteriorated and human reliability may be a risk factor for the process. Another method used in the analysis of liquids is high-performance liquid chromatography (HPLC) [3], but this type of analysis is expensive and must be performed in laboratories with specialized equipment. As an alternative to the two mentioned methods, the electronic tongue sensor array has emerged because its advantages such as portability, reliability and low price [4]. Inspired by the human sense of

**Citation:** Leon-Medina, J.X.; Anaya, M.; Tibaduiza, D.A. Locally Linear Embedding as Nonlinear Feature Extraction to Discriminate Liquids with a Cyclic Voltammetric Electronic Tongue. *Chem. Proc.* **2021**, *5*, 56. https://doi.org/10.3390/ CSAC2021-10426

Academic Editors: Huangxian Ju and Manel del Valle

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

taste and the behavior of taste buds, electronic tongues use an array of non-selective sensors to capture signals from a specific liquid. An electronic tongue uses sensors of different materials and subsequently a sensor data fusion analysis based on pattern recognition algorithms to perform classification tasks of different liquids.

One of the applications of electronic tongue is the discrimination of different fruit juices. For example, in 2011 Dias et al. [5] developed a potentiometric electronic tongue using linear discriminant analysis (LDA) to differentiate four beverage groups including juices of orange, pineapple, mango and peach. In other work, eleven fruit juice varieties were correctly classified by a potentiometric electronic tongue using a Fuzzy ARTMAP neural network [6]. Several sensors composed the electronic tongue sensor arrays; thus, the datasets acquired are very large in size. To deal with this inconvenience, in 2012, Kiranmayee et al. [7] developed a method based on segmentation of the voltammetric signal with the objective to reduce the size of the signal maintaining meaningful information to discriminate the analyzed classes. The developed method was satisfactorily applied to an eight-juices dataset, reducing the data size by 78.94%. A common problem observed in the previous works is that the signals acquired with the electronic tongue have a high dimensionality. This work presents a novel methodology to correctly classify the signals acquired from a cyclic voltammetric electronic tongue.

In this work, the cyclic voltammetry technique was used to perform experiments on five different juices; two screen-printed electrodes (SPE) voltametric sensors were used. The working electrode materials were platinum and graphite. The amount of data captured when performing cyclic voltammetry experiments is high; therefore, these data have high dimensionality. This work uses the Locally Linear Embedding (LLE) [8] method to perform a dimensionality reduction of the original data. This dimensionality reduction serves as feature extraction method that is used as input of a *k*-Nearest Neighbor (*k*-NN) [9] classifier used as supervised machine learning method. In order to classify the five different juices a Leave-One-Out cross validation procedure is executed due to the small quantity of samples in the dataset, along to prevent over-fitting [10]. The results show a correct classification procedure of the juices evidenced with a high classification accuracy. The remainder of this papers is as follows: Section 2 describes the materials and methods including the experimental setup and the cyclic voltammetry tests performed. Following, Section 3 presents the data processing results including data unfolding, data scaling, dimensionality reduction, classification, and cross validation. Finally, the Section 4 outlines the main conclusions of this work.

#### **2. Materials and Methods**

#### *2.1. Experimental Setup for the Acquisition of the Juice Dataset*

The methodology developed in this work is used to classify 5 different classes of juices. This dataset of juices was obtained by conducting experiments on 5 different juices from a company located in the city of Tunja in the department of Boyacá-Colombia. Cyclic voltammetry tests were performed on each one of the 5 juices. For each juice, five experiments were performed, as shown in Table 1.

**Table 1.** Description of the type of juice in the dataset registered with the EVAL-AD5940ELCZ potentiostat.


Experiments were performed on the different juices using the EVAL-AD5940ELCZ [11] electrochemical evaluation board from Analog Devices. This board is commanded by the evaluation board EVAL-ADICUP3029, which is an Arduino- and PMOD-compatible development board that includes Bluetooth and WiFi connectivity [12]. The EVAL-ADICUP3029 board uses the ADuCM3029 ultra low power Arm Cortex-M3 processor as the main device. The ADuCM3029 is an integrated mixed-signal microcontroller system for processing, control, and connectivity. The integration of the EVAL -AD5940ELCZ and EVAL-ADICUP3029 boards is used as potentiostat equipment. This system provide only 1 channel in such a way that a cyclic voltammogram was obtained at a time, In the experimentation the sensor had to be changed to perform each cyclic voltammetry experiment. This electronic tongue used two screen-printed electrode voltammetric sensors from the BVT technologies company [13]. Specifically, the types of these two sensors were: AC1.W2.R2 DW = 1 and AC1.W4.R2 DW = 1. These type of sensors uses the same material for their working and auxiliary electrodes. The first sensor used as working and auxiliary electrode platinum and the second sensor used graphite. Silver covered by AgCl was used as reference electrode in both sensors. The hardware used to obtain the data set of five juices is depicted in Figure 1 left.

**Figure 1.** (**Left**) Integrated electronic tongue system: Computer with Sensor Pal software, USB cable to the EVAL-AD5940ELCZ and EVAL-ADICUP3029 attached boards, colored crocodile cable, sensor connection cable and SPE Sensor. (**Right**) Cyclic voltammetry test in Sensor Pal software from Analog Devices, blue line represents the excitation ramp signal, while the green line shows the response signal.

#### *2.2. Cyclic Voltammetry Tests to Obtain the Juice Data Set*

The Sensor Pal command software from Analog Devices was used to perform the cyclic voltammetry tests. The parameters used in the development of these experiments are shown in Table 2. The ramp-type drive signal shown in blue in Figure 1 right has a total duration of 4 s. The data points of each voltammogram is equal to 500, since there is a period of 8 ms for each sample. The scan rate used is equal to 500 mV/s. Results shown by the green line in the unfolding voltammogram present data current in the ordinate axis in the order of μA. According to Table 1, five measurements were taken per analyte. In this sense the two sensors are referred to one measure in Table 1. Thus, in total, five measures × 2 sensors = 10 voltammograms were acquired by each juice.

Figures 2 left and right show the cyclic voltammograms for two different juices with both the platinum and graphite sensors in the electronic tongue system by using the boards EVAL-AD5940ELCZ and EVAL-ADICUP3029 as potentiostat. In particular, Figure 2 left depicts the cyclic voltammograms obtained for green apple juice showing that the voltammogram obtained by the graphite sensor reaches higher positive current values than the platinum sensor. In contrast, Figure 2 right shows the cyclic voltammograms for

an experiment in red fruit juice, the magnitude of the current obtained by the graphite sensor is clearly lower than with the platinum sensor.

**Table 2.** Parameters used in cyclic voltammetry tests to obtain the juice data set.


#### *2.3. Dimensionality Reduction*

Due to the high dimensionality of the data obtained when performing cyclic voltammetry experiments and how the data are unfolded creating a two-dimensional matrix, it is necessary to carry out a dimensionality reduction process. There are different methods of dimensionality reduction, these can be classified as linear or non-linear. Within the linear methods is the principal component analysis (PCA) [9]. In this method, the greatest amount of variance of the data is represented in a low-dimensional linear space. The data normalization affects the result of the embedding performed by different dimensionality reduction methods.

However, the data obtained by the electronic tongue can form a highly nonlinear manifold. To deal with this issue, different nonlinear dimensionality reduction methods have been developed [14]. These methods are based on the construction of a neighborhood graph and the idea that nearby points in the high-dimensional space can preserve this property in a low-dimensional space. One of the parameters that must be tuned in the dimensionality reduction process is the target dimension *d*. These target dimensions define the sized of the reduced feature matrix. Specifically, *d* defines the number of columns that the reduced feature matrix will have. The Locally Linear Embedding (LLE) method solely preserves manifold local properties.

#### **3. Data Processing Results**

#### *3.1. Data Unfolding*

The unfolding of the cyclic voltammogram data obtained by each sensor is carried out according to the group scaling method [15]. For each experiment carried out, the unfolding of the two sensors is performed, obtaining a signal of 1000 data points. Figure 3 left shows an unfolded signal by juice number 3 (red fruits). In this case, the ordinates correspond to current measurements in μA and the abscissa to data points. Since 25 juice samples were considered in total, the matrix size *X* is equal to 25 × 1000.

**Figure 3.** (**Left**) Example of unfolded signal of the cyclic voltammograms obtained by the platinum and graphite sensors. (**Right**) Three-dimensional scatter plot for the first three dimensions obtained using the LLE method on the juice data set.

#### *3.2. Dimensionality Reduction Results*

The next step in the juice recognition methodology using a cyclic voltammetry electronic tongue is to reduce the dimensionality of the data. In this case, the Locally Linear Embedding (LLE) algorithm was used, which allows to carry out the feature extraction process. The results of the first 3 dimensions after applying the LLE algorithm to the juice dataset are illustrated in the scatter diagram of Figure 3 right. Classes 3 and 5 are the ones better separated (according to the 3D-view shown in Figure 3 right). Thus, the use of a machine learning classifier algorithm is necessary. In this case, the classifying algorithm was *k*-Nearest Neighbors.

In order to compare the behavior of different methods [15] to perform the dimensionality reduction stage the PCA, Laplacian Eigenmaps, Isomap and t-distributed stochastic neighbor embedding (*t*-SNE) were selected to determine their behavior in terms of classification accuracy. In addition, a parameter tuning is performed for each manifold learning dimensionality reduction algorithm used. In this case there are 3 algorithms that in common need to create a neighborhood graph, which has the parameter *k*, on the other hand the algorithm *t*-SNE needs the calibration of its perplexity parameter *p*. Figure 4 shows the behavior in the classification accuracy with respect to the variation of each of these parameters. The used range for the parameters was from 4 to 24 since for the neighborhood graphs the minimum value of *k*= 4 and the maximum of 24 because there are 25 total samples in the data set. This same range was used for the perplexity *p* value. As can be seen in Figure 4, the LLE method is the one that achieves the highest accuracy values. Particularly when *k* = 22 the LLE method reaches 80% of classification accuracy.

#### *3.3. Classification and Cross Validation*

The LLE algorithm needs the definition of the destination dimension, to find this parameter, a study of the change of the destination dimension *d* vs. the classification accuracy obtained by the algorithm *k*-NN with *k* = 1 was carried out and Euclidean distance was considered. The cross-validation process executed was leaving one out (LOOCV) due to the small number of samples in the juice data set.

Influence of Target Dimensions Variation

Since the number of dimensions at the input of the *k*-NN classifier algorithm can vary, a study was carried out to determine the best classification accuracy for each of the dimensionality reduction algorithms studied. In Table 3, it can be seen how the LLE method is the one with the best performance in terms of classification accuracy, reaching an accuracy value of 80% with 9 dimensions. As it can be seen in Table 3 the accuracy behavior tends to increase as *d* is increased up to a maximum of *d* = 9 for a classification accuracy of 80%. After the dimension *d* = 9 accuracy tends to decrease, in this sense the optimum size selection was defined as *d* = 9. Therefore, the feature matrix size at the input of the *k*-NN classifier is equal to 25 × 9.

Figure 5 shows the results of the confusion matrix for the mentioned accuracy of 80%. In this case, class 2 was correctly classified, there was 1 error for classes 1,3 and 4; finally, the class that was classified worst was class 5 with two errors. Overall, of the 25 total samples, 20 were classified well and five were classified badly.

**Table 3.** Classification accuracy behavior of each dimensionality reduction method when varying the number of dimensions at the input of the *k*-NN classifier algorithm for the classification of 5 different commercial fruit-based products.


**Matriz de Confusión data set de Jugos LLE + KNN**

**Figure 5.** Confusion matrix for the juice data set obtained using 9 target dimensions of the LLE algorithm. The classification accuracy is equal to 80%.

#### **4. Conclusions**

This work presented a computational framework for processing the signals obtained by a cyclic voltammetry electronic tongue sensor array. The classification accuracy obtained by the developed methodology in a dataset of five different juices showed the advantages of apply this methodology as classification method. It processes the raw complete voltammograms obtained by each working electrode and unfolded them to create a two dimensional matrix. This matrix was normalized applying the group scaling method. Then, the locally linear embedding method is used as a nonlinear feature extraction approach to obtain the feature matrix at the input of a *k*-NN classifier. As future work, the developed methodology will be applied for classify other kind of substances and other approaches related to semi-supervised classification will be tested.

**Author Contributions:** All authors contributed to the development of this work, specifically their contributions are as follows: conceptualization, D.A.T. and J.X.L.-M.; data organization and preprocessing, J.X.L.-M. and M.A.; methodology, J.X.L.-M. and M.A.; validation, J.X.L.-M. and D.A.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by FONDO DE CIENCIA TECNOLOGÍA E INNOVACION FCTeI DEL SISTEMA GENERAL DE REGALÍAS SGR. The authors express their gratitude to the Administrative Department of Science, Technology and Innovation—Colciencias with the grant 779—"Convocatoria para la Formación de Capital Humano de Alto Nivel para el Departamento de Boyacá 2017" for sponsoring the research presented herein.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** J.X.L.-M. is grateful with Colciencias and Gobernación de Boyacá. J.X.L.-M. thanks Miryam Rincón Joya from the Department of Physics of the National University of Colombia and Leydi Julieta Cardenas Flechas, for their introduction to the electronic tongue sensor array field of research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Applied Voltage Effect in Lbl Sensors While Detecting 17**α**-Ethinylestradiol in Water Samples †**

**Paulo M. Zagalo 1,\*, Cátia Magro <sup>2</sup> , Paulo A. Ribeiro <sup>1</sup> and Maria Raposo 1,\***


**Abstract:** The effect of the applied voltage on impedance spectra, measured on sensors based on solid supports with interdigitated electrodes (IDE) that are either covered or not with a layer-by-layer film prepared with polyethylenimine and poly (sodium 4-styrenesulfonate), was analyzed to detect 17α-ethinylestradiol(EE2) in mineral water and tap water. The results show that the sensor response is strongly affected by the applied voltage, the presence of film, and the water matrix, meaning that electrochemical reactions develop near the IDE. However, for low values of applied voltage, the sensor response is reproducible with negligible electrochemical reactions, allowing us to conclude that 25 mV is the appropriate voltage.

**Keywords:** voltage effect; impedance; sensor; layer-by-layer; interdigitate electrodes; electrochemical reactions

**Citation:** Zagalo, P.M.; Magro, C.; Ribeiro, P.A.; Raposo, M. Applied Voltage Effect in Lbl Sensors While Detecting 17α-Ethinylestradiol in Water Samples. *Chem. Proc.* **2021**, *5*, 57. https://doi.org/10.3390/ CSAC2021-10460

Academic Editor: Ye Zhou

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

With the advent of more modern and advanced technologies, it has become possible to detect chemical compounds at very low concentrations. These compounds were previously hidden and invisible to former methods of analysis in water bodies such as ponds, lakes, rivers, underground waters, muds, or even wastewaters [1,2]. Considering that not only human lives, but also a vast majority of Earth's fauna and flora, rely deeply on the central and invaluable role that fresh and clean water plays, it is undoubtedly and alarmingly necessary to strive and succeed in finding novel ways to detect, monitor and conceivably remove these substances from water bodies. Some of these emerging contaminants are included in the category of pharmaceuticals and personal care products (PPCP), such as triclosan (5-chloro-2-(2,4-dichlorophenoxy) phenol) (TCS) which is a well-known and commonly used compound in toothpaste, shampoos and lotions due to its bacteriostatic and antimicrobial properties [3]. Amongst these PPCPs, there is a group of substances designated as endocrine disrupting compounds (EDC) and it is within this collection of composites that 17α-ethinylestradiol (EE2) is inserted [4].

More specifically, EE2 is a synthetic hormone that is commonly used in the manufacturing of women's oral birth control pills, a widespread and rather mainstream contraceptive. This compound finds its way into wastewater through regular bodily excretions and, even though this water enters wastewater treatment plants where it is treated and cleaned, it is not possible to achieve a 100% removal rate of EE2 from water [5,6]. The treated water that still contains low concentrations of EE2 is discharged into large bodies of water such as rivers and seas where it will subsequently impact the life cycles of both fauna and flora [7–9].

Given that some PPCP and EDC effects on the environment are already known or are the object of several studies, a certain degree of precautions has been taken. In the case of EE2, these include regulations such as the European Union Decision 840 of June 5th 2018 and the Regulation of synthetic estrogen 17α-ethinylestradiol in water bodies in Europe, the United States, and Brazil, and also other measures and guidelines which aim to limit and/or ban its usage [10–12].

This work is but a part of a major venture that aims to develop and put into practice cheaper, easier to fabricate and more user-friendly sensor devices capable of detecting and monitoring EE2 in different water bodies, while still retaining or improving the efficiency and detection limits of already existing devices of this nature. The combination of a range of sensors which would then work as a whole, such as an electronic tongue system (ET), to better detect harmful molecules in water bodies is not only a viable option but also a fascinating one due to its possibilities and versatility [13]. To that end, the use of sensorial units as interdigitated electrodes (IDE) coupled with an impedimetric system has been demonstrated as being a useful and rather simple method to study, analyze and infer the properties and variations of a multitude of medium samples, from environmental to biomedical ones [14,15].

IDE's versatility of being deployable in a myriad of applications without the added labor and expenses of modifying their core geometry and/or configuration has catapulted these devices into wide use in a vast array of fields and areas, also allowing for the possibility of lab-on-chip systems based on IDE. These types of sensors present further features and advantages that make them coveted and chosen by researchers worldwide in their works, such as the advent of more powerful and precise technological advances that paved the way for the production of increasingly smaller electrodes, at highly competitive costs due to processes as mass-fabrication [16–18].

In the present work, by combining interdigitated gold electrodes with thin-film techniques, for instance, layer-by-layer (LbL), and impedance spectroscopy the aforementioned goal of developing sensors to detect and monitor EE2 in different water bodies is made possible. In particular, the effect of different voltage levels applied to the IDE sensor devices and how it would impact the detection capabilities of EE2 in mineral water (MW) and tap water (TW) was analyzed. Although a myriad of studies and works have been conducted and performed on the subject of IDEs, none (to the best of the authors' knowledge) have+ analyzed the influence of the variation of voltage levels on IDE sensors and its impact on the sensors' performance and sensitivity when detecting 17α-ethinylestradiol in different water matrices.

#### **2. Materials and Methods**

In the course of the experimental work conducted for this study, gold IDE (200 μm/200 μm) deposited onto ceramic substrates were used as sensor devices to detect 17α-ethinylestradiol (EE2) in two different water matrices with distinct complexity levels. The water samples used were a commercial Portuguese mineral water (MW) (pH = 5.7 ± 0.3) and tap water (TW) (pH = 6.8 ± 0.1). These matrices were chosen to observe the way various water complexities interact and impact these electrical measurements performed as well as the detection of the hormone. Regarding the IDE, for each of the matrices two distinct types of sensors were prepared: naked (uncoated) IDE and thin film IDE. The thin films deposited onto the IDE were prepared through the technique of Layer-by-Layer (LbL), where the alternate deposition of polyelectrolytes (positive and negative) builds up to the formation of thin film bilayers [19]. The positive polyelectrolyte used was polyethylenimine (PEI) and its negatively charged counterpart was poly(sodium 4-styrenesulfonate) (PSS), prepared using aqueous solutions with a 10−<sup>2</sup> M concentration of both polyelectrolytes. Through this technique, thin films of PEI/PSS with 5 bilayers were produced ([PEI/PSS]5).

Solutions of both MW and TW were spiked with a concentration of 10−<sup>12</sup> M of EE2. Subsequently, both types of sensors were immersed in these solutions and electrical measurements were conducted whilst applying varying AC voltages to the IDE sensors: 25 mV, 50 mV, 100 mV, 200 mV, 300 mV, 400 mV, 500 mV, 600 mV, 700 mV, 800 mV, 900 mV, and 1000 mV. The aforementioned electrical measurements were performed using a Solartron 1260 Impedance Analyzer, with a frequency range of [1 − 1M] Hz.

Both polyelectrolytes (PEI and PSS) and EE2 standards were purchased from Sigma-Aldrich (Darmstadt, Germany).

#### **3. Results**

Figures 1 and 2 show the loss tangent, real, and imaginary spectra for the uncoated sensors and those coated with a (PEI/PSS)5 LbL film, respectively, while the sensors were immersed in aqueous MW and TW solutions, spiked with a fixed concentration of 10−<sup>12</sup> M of EE2. The voltage applied on the IDE sensors was then sequentially altered from an initial value of 25 mV up to a maximum value of 1000 mV.

**Figure 1.** Comparison of derived information from impedance experimental data as functions of frequency for uncoated sensors after immersion in solutions spiked with EE2 in MW and TW: (**a**,**d**) Loss tangent; (**b**,**e**) Real; (**c**,**f**) Imaginary.

Through an analysis of the plots shown in Figures 1 and 2, the roles that different types of water play become apparent. In both figures, when the transition from mineral water to tap water is performed, the degree of distinction and separation between the various voltage values applied to the sensors greatly increases, while also displaying a higher tendency to achieve an ordered sequence between voltages. Although this behavior can be observed in all spectra above, it is particularly prominent in the loss tangent spectra.

**Figure 2.** Comparison of derived information from impedance experimental data as functions of frequency for (PEI/PSS)5 sensors after immersion in solutions spiked with EE2 in MW and TW: (**a**,**d**) Loss tangent; (**b**,**e**) Real; (**c**,**f**) Imaginary.

#### **4. Discussion**

By delving deeper into the analysis of Figures 1 and 2, in the spectra of loss tangent for TW (Figures 1d and 2d), it becomes possible to infer that the increase in conductivity of the water medium translates itself in a right shift of the curves towards higher frequencies, which in turn underlines an increase in signal intensity for that range of frequency. Building upon what was stated in the previous section, there is an evident improvement regarding the distinction and separation of the different voltage values, not only when transitioning from MW to a medium richer and densely filled with electric charges as is the case for TW, but also by adding the (PEI/PSS)5 thin film coating onto the surface of the IDE sensors. There is one more interesting behavior to note, which is the inversion of the voltage ordered sequence observable in Figure 1a,d from lowest (25 mV) to highest (1000 mV) to the one displayed in Figure 2a,d which transitions from highest to lowest. This behavior change is most likely due to the addition of the thin film coating, given that it introduces other electrochemical reactions on the IDE surface.

To the best of the authors' knowledge, this type of study has not yet been conducted, with regard to an analysis of how LbL thin films behave when subjected to different applied voltages, while detecting PPCP or EDC such as in this case with EE2. However, the need to perform this experiment arose from the lack of information regarding this subject while conducting experimental works within our research group that are directly related to both this kind of sensor, (LbL films on IDE) and emerging compounds such as triclosan and EE2 [20–23]. This type of film ([PEI/PSS]5) was chosen since it is viable, as demonstrated in previous works conducted where several types of thin film combinations were studied to ascertain which one (or ones) would be the most suitable to use in experiments that aim to detect (and possibly monitor) noxious compounds in water bodies [14]. These films have been found to be the most promising among all of the thin films that were analyzed, particularly as the complexity and pH of the water matrices increased. The reason being that these factors have a deep impact on this thin film's stability, namely on the sulfonate group present within the chemical composition and the structure of the outer layer (PSS), which achieves more stable behaviour when in the presence of pH of approximately 7 and above [23,24].

Figure 3 presents curves that illustrate the behavior of the maximum loss tangent and the applied voltage for each IDE upon the collection of data through impedance spectroscopy.

**Figure 3.** Plots of loss tangent values at the peak vs voltage for both types of IDE used (naked and [PEI/PSS]5) for: (**a**) mineral water; (**b**) tap water.

From these plots, one can surmise, for both MW and TW, that by increasing the voltage that is applied to each IDE device, a decrease in the polarization of the thin film sensors occurs, which results in these sensor devices exhibiting a poorer signal response to the external electrical stimuli. It was also possible to observe that the higher the voltage, the less reproducible the sensors tend to be. This effect could be due to a combination of factors, namely the sensors becoming more prone to external noise at higher voltages and possible structural damage on either the IDE Au layer or the (PEI/PSS)5 thin film itself, or of both simultaneously. These results are in accordance with the results observed by Magro et al. [14] which demonstrated the IDE are damaged by electrical measurements, inducing irreproducibility in the electrical measurements, which emphasizes the necessity of the presence of a thin film covering the IDE and the use of low voltage to measure the impedance spectra.

#### **5. Conclusions**

This work set out to study and understand how the effect of varying applied voltage to IDE sensors influences their overall responses while detecting EE2 in complex water matrices. For this purpose, two types of sensor devices consisting of ceramic solid supports coated with Au IDE were used, namely, naked sensors (no thin film) and (PEI/PSS)5 thin film sensors. From the impedance spectra, it was possible to observe that by increasing the complexity of the water matrix, in this case from MW to TW, the sensors exhibit, while detecting EE2, a strong dependence with the applied voltage. Furthermore, by depositing a thin film of (PEI/PSS) with 5 bilayers, the abovementioned sensors' spectra was also found to change with the increase of the applied voltage with an opposite behavior of that achieved with the uncoated electrodes. This points to the increase in chemical reactions with an increase in the electric field between the electrodes. These reactions differ depending on whether the electrodes are covered by the thin film or not. Therefore, one can conclude that the sensor impedance response is strongly affected by the voltage applied and by the water matrix, meaning that electrochemical reactions are developed near the IDE electrodes. However, for low values of applied voltage the sensor is reproducible, and the electrochemical reactions are negligible. Moreover, the chemical reactions are dependent on the presence and type of the LbL film deposited on the electrodes. Finally, one can conclude that, to avoid chemical reactions, the applied voltage should be reduced to 25 mV when one can use this technique to characterize this kind of sensor. However, further studies and analyses should be conducted to investigate this subject further, while also striving to optimize the experimental system used.

**Author Contributions:** Conceptualization, P.M.Z., M.R.; methodology, P.M.Z.; software, P.M.Z., C.M.; validation, P.M.Z.; formal analysis, P.M.Z., C.M., M.R.; literature review, P.M.Z., M.R.; resources, M.R., P.A.R.; data curation, P.M.Z., M.R.; writing—original draft preparation, P.M.Z.; writing—review and editing, P.M.Z., M.R.; visualization, P.M.Z.; supervision, P.A.R., M.R.; project administration, P.A.R., M.R.; funding acquisition, P.A.R., M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** Fundação para a Ciência e a Tecnologia (FCT), Portugal, through research project grants PTDC/FIS-285 NAN/0909/2014, UID/FIS/00068/2019 and the Bilateral Project entitled "Deteção de Estrogénio- um 286 Contaminante Emergente—em Corpos Hídricos" within the scope of "Cooperação Transnacional\_FCT 287 (Portugal)-CAPES (Brazil) 2018".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors acknowledge the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through research project grants PTDC/FIS-NAN/0909/2014, UID/FIS/00068/2019 and the Bilateral Project entitled "Deteção de Estrogénio- um Contaminante Emergente—em Corpos Hídricos" within the scope of "Cooperação Transnacional\_FCT (Portugal)-CAPES (Brazil) 2018". The authors also acknowledge the research center Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys)—FCT/UNL. PM Zagalo acknowledges his fellowship PD/BD/142767/2018 from RABBIT Doctoral Programme.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Smart Sensing for Antibiotic Monitoring in Mineral and Surface Water: Development of an Electronic Tongue Device †**

**Cátia Magro 1,\* , Tiago Moura 1, Paulo A. Ribeiro <sup>2</sup> , Maria Raposo <sup>2</sup> and Susana Sério 2,\***


**Abstract:** Sensors are considered the future monitoring tools, since, compared to traditional sampling and analysis techniques, they provide fast response on the output data in a timely, continuous, safe, and cost-effective fashion. Antibiotics are important pharmaceuticals with a large variety of applications. However, the overconsumption of these drugs is under the spotlight, since traces of antibiotics are being found in aquatic ecosystems and may lead to the development of antibiotic resistance. Thus, in this work, sensors consisting of ceramic or glass BK7 solid supports with interdigitated gold electrodes coated with five bilayers of polyethyleneimine (PEI) and poly(sodium 4-styrenesulfonate) (PSS) thin films were developed and able to distinguish clarithromycin concentrations between 10−<sup>15</sup> M and 10−<sup>5</sup> M in mineral and surface water matrices. In mineral water, the ceramic support sensors have shown high reproducibility, whereas glass support sensors are not reproducible for this matrix. For the surface water matrix, both types of sensors proved to be reproducible. The surface water's principal component analysis, obtained for an electronic tongue composed of the aforementioned sensors, demonstrated the concept's ability to discriminate between different concentrations of the target compound, although no significant pattern of trend was achieved.

**Keywords:** environmental monitoring; antibiotics; clarithromycin; electronic tongue; impedance spectroscopy

#### **1. Introduction**

The consumption of antibiotics has grown substantially since their discovery, and they represent an important class of pharmaceuticals employed for treating bacterial infections by killing bacteria or preventing them from spreading. Antimicrobial drugs are widely utilized in human and veterinary medicine as well as in agriculture [1]. Although these chemicals allow us to live longer and have healthier lives, their overconsumption poses a great threat, mainly regarding the development of antibiotic resistance [2].

Clarithromycin is a macrolide, a class of antibiotic drugs produced by multiple *streptomyces* strains, mostly effective against Gram-positive bacteria [3]. It is paramount to understand the occurrence and fate of macrolides in the environment, since traces of these drugs are frequently detected in surface and ground waters. Accordingly, the European Commission included clarithromycin, together with other macrolides, in the 2nd Watch List of Emerging Water Pollutants. This surface water watch list was developed with the purpose of obtaining high-quality monitoring data regarding several potential water pollutants in order to establish their environmental and health risks, thus emphasizing the importance of developing efficient techniques to detect and quantify such pollutants.

**Citation:** Magro, C.; Moura, T.; Ribeiro, P.A.; Raposo, M.; Sério, S. Smart Sensing for Antibiotic Monitoring in Mineral and Surface Water: Development of an Electronic Tongue Device. *Chem. Proc.* **2021**, *5*, 58. https://doi.org/10.3390/ CSAC2021-10606

Academic Editor: Huangxian Ju

Published: 5 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

Analytical methods based on liquid chromatography (LC) and mass spectrometry (MS) provide low detection limits but require intensive sample preparation, expensive equipment, and experienced operators and are not designed for in situ analysis [4,5]. Sensors are considered the future of monitoring tools and present many advantages compared to traditional techniques [6]. Sensor devices are low cost, simple to operate, and can be used for continuous, fast, and reliable in situ monitoring [7–9]. Even though sensors are not as selective as LC/MS methods, they have proved to be able to identify multiple analytes simultaneously [6]. Moreover, sensors have the ability to work as smart devices that may be incorporated in monitoring systems with real-time data transmission.

The electronic tongue (e-tongue) refers to a device that consists of an array of nonspecific chemical and/or physical sensors that display cross-sensitivity to target compounds in a liquid matrix [10]. E-tongue devices may rely on potentiometry, voltammetry, or impedance spectroscopy as transducing methods [11].

The present work aims to explore the potential of the e-tongue concept to monitor different clarithromycin concentrations in two environmental aqueous matrices with incremental complexity. Relying on the discussion of reproducibility, an array of sensors coated with PEI/PSS thin films, which previously showed more mechanical stability [12], was studied as a potential smart device to monitor clarithromycin in mineral and surface water.

#### **2. Materials and Methods**

Clarithromycin (Sigma-Aldrich, Steinheim, Germany) solutions with concentrations between 10−<sup>15</sup> M and 10−<sup>5</sup> M were prepared by sequential dilutions of a mother solution with a concentration of 10−<sup>4</sup> M. All dilutions, as well as the mother solution, were prepared with experimental matrix/MeOH (9:1) solutions. The experimental matrices utilized to prepare the solutions were a commercial Portuguese mineral water (MW) and a surface water (SW) collected from Tagus River at Porto Brandão, Caparica, Portugal. Lastly, solutions without clarithromycin, containing only experimental matrix/MeOH (9:1), were prepared for the MW and SW matrices to be used as the blank standard (0 M).

The sensors used in this work consist of interdigitated gold electrodes (IDE) deposited on ceramic and glass BK7 solid supports purchased from DropSens (Oviedo, Asturias, Spain). The dimensions of the ceramic support are 22.8 mm × 7.6 mm × 1 mm; the width of each "finger" and the spacing between "fingers" are both 200 μm. The glass support's dimensions are 22.8 mm × 7.6 mm × 0.7 mm, and the width of each "finger", like the spacing between "fingers", is 10 μm. The sensor devices were coated with thin films of polyethyleneimine (PEI) and poly(sodium 4-styrenesulfonate) (PSS) produced by the layerby-layer (LbL) technique [13]. The substrates were alternately immersed in positively and negatively charged polyelectrolyte solutions with concentrations of 10−<sup>2</sup> M for 30 s. After the adsorption of each layer, the substrates were immersed in water in order to remove any polyelectrolyte molecules that were not completely adsorbed. At the end of the deposition of each bilayer, the substrates were dried with nitrogen gas stream (99% purity, Air Liquide, Algés, Portugal). The thin films of PEI/PSS were prepared with 5 bilayers, (PEI/PSS)5. Prior to the deposition of thin films, all sensors were cleaned with ethanol and ultra-pure water. Thereafter, the substrates were dried with compressed nitrogen gas (99% purity, Air Liquide, Algés, Portugal).

The electrical analysis was achieved by impedance spectroscopy measurements of the sensor devices when immersed in aqueous matrices spiked with a sequence of increasing concentrations of clarithromycin, from 0 to 10−<sup>5</sup> M. The impedance spectra were obtained with a Solartron 1260 Impedance Analyzer (Solartron Analytical, AMETEK Scientific Instruments, Berwyn, PA, USA) in the frequency range of 1 Hz to 1 MHz by applying an alternate voltage with an amplitude of 25 mV. Each measurement was performed at room temperature (≈23 ◦C).

Principal component analysis (PCA) was performed, with respect to the normalized (Z-score normalization: *value* <sup>−</sup> *<sup>μ</sup> <sup>ϑ</sup>* , *μ* and *ϑ* being the mean value and the standard deviation of the samples, respectively) impedance spectroscopy data, to reduce the size of the data and to obtain a new space of orthogonal components in order to detect and explain different concentration patterns. The clarithromycin detection in the target matrices was further evaluated by an array of sensors, composed of all of the produced thin films, using the e-tongue concept.

#### **3. Results and Discussion**

#### *3.1. Sensor Reproducibility*

To draw conclusions on the reproducibility of the (PEI/PSS)5 sensors, each solution of clarithromycin was analyzed with two identical sensors produced under the same conditions. The average of the two sensors' data was calculated as a function of the frequency for each matrix and type of sensor (glass or ceramic support). The standard deviation was used as a measure of the uncertainty. In Figure 1 is depicted the average of the impedance magnitude measured by (a) two ceramic support and (b) two glass support sensors when immersed in the MW matrix. Figure 1a reveals that the ceramic support sensors coated with (PEI/PSS)5 are reproducible when monitoring clarithromycin in MW, since the values of the standard deviation are small (two orders of magnitude smaller than the average), meaning that the results of both sensors are similar. Furthermore, Figure 1b shows that there is a significant discrepancy between the measurements of the two glass support sensors, which results in larger standard deviation values. These results can be explained, since the spacing between the IDE "fingers" is smaller in the glass support sensors, and, thus, the electric field generated between them, when an AC voltage signal is applied, has a larger magnitude. Additionally, the MW matrix has a lower conductivity, containing ion species in lower concentrations and affecting, therefore, the interactions between the matrix and the sensor. Consequently, at lower frequency values (<10 Hz), measurements of the glass sensors immersed in MW are compromised (see Figure 1b, region highlighted by a red dashed rectangle).

**Figure 1.** Reproducibility of the impedance magnitude measured by (**a**) two ceramic support and (**b**) two glass support sensors in a MW matrix.

Regarding the analysis of clarithromycin solutions prepared with SW, Figure 2a,b shows the average of the capacitance measured by two ceramic support sensors and two glass support sensors, respectively.

Figure 2a reveals that there is not a considerable difference between the results of both ceramic sensors. As stated before, this conclusion can be drawn from the low values of the standard deviation (10−10–10−<sup>8</sup> F). Figure 2b provides similar conclusions; thus, both types of sensors show good reproducibility when monitoring SW matrices. However, for the SW matrix, the glass support sensors coated with (PEI/PSS)5 thin films present a more

noticeable sensitivity in discriminating clarithromycin concentrations in the frequency range of 1–100 Hz.

**Figure 2.** Reproducibility of the capacitance measured by (**a**) two ceramic support and (**b**) two glass support sensors in a SW matrix.

On the other hand, the sensors with a ceramic support showed superior reproducibility for both experimental matrices. Furthermore, the ceramic sensors produced more reliable results in the frequency region of 1–100 Hz for MW. For SW, both types of sensors achieved better reproducibility in the measurements from 1 Hz to 100 Hz.

#### *3.2. Principal Component Analysis: E-Tongue*

The results of the principal component analysis applied to the e-tongue concept will be presented and discussed only for SW. The e-tongue concept was not applied for MW, since the glass support sensors were not reproducible, as evidenced by the analysis of the impedance electrical characterization (Figure 1b). Thus, Figure 3 displays the PCA plots obtained for an array of sensors composed of two ceramic support sensors, for MW (Figure 3a), and two ceramic support sensors combined with two glass support sensors for SW (Figure 3b). In both target matrices, the array of sensors was coated with (PEI/PSS)5 thin films.

**Figure 3.** PCA plots for clarithromycin concentrations (0–10−<sup>5</sup> M) discriminated by an e-tongue for (**a**) MW and (**b**) SW.

In Figure 3a, the first two principal components account for 91.68% of the total variance. The PCA plot reveals that the ceramic support sensors provide the ability to discriminate between different clarithromycin concentrations and the blank solution. There can also be

observed a pattern in the concentration decay, with 10−<sup>13</sup> M as an outlier. Figure 3b shows the PCA plot for an array of sensors identical to the one discussed above but immersed in SW solutions. In this case, the first two principal components accounted for 79.07% of the total variance. The plot reveals that the e-tongue concept provides the ability to discriminate between non-doped and doped SW solutions. Additionally, the sensors were able to distinguish the different clarithromycin concentrations.

#### **4. Conclusions**

Sensors composed of ceramic or glass BK7 solid supports, with interdigitated gold electrodes, were coated with five bilayers of PEI/PSS thin films produced by the LbL technique. An electronic tongue consisting of an array of these sensors was shown to provide the ability to distinguish between clarithromycin concentrations in the range of 10−<sup>15</sup> M to 10−<sup>5</sup> M in surface water.

The electrical analysis of the samples was performed with impedance spectroscopy by immersing the sensors in the water samples with different clarithromycin concentrations. An average of the measurements obtained with two identical sensors and the associated standard deviation were used to study the reproducibility of the sensors. In the MW matrix, the ceramic sensors showed reproducibility. The opposite can be said for the glass support sensors, which for lower frequencies struggle to identify the target compound. In the SW matrix, both types of sensors, ceramic or glass support, were proven to be highly reproducible.

Results of the principal component analysis of the impedance data did not show a clear pattern or trend but was able to distinguish between doped and non-doped solutions, both for MW and SW matrices. To achieve better results, the e-tongue concept requires a wider variety of thin films deposited on the sensors, such as, for example, metal oxides or carbon-based thin films.

**Author Contributions:** Conceptualization, C.M., T.M., M.R. and S.S.; methodology, T.M.; software, T.M.; validation, C.M., M.R. and S.S.; formal analysis, C.M, M.R. and S.S.; investigation, T.M. and C.M.; resources, P.A.R., M.R. and S.S.; data curation, T.M.; writing—original draft preparation, C.M., T.M., M.R. and S.S.; writing—review and editing, C.M., M.R. and S.S.; supervision, C.M., M.R. and S.S.; project administration, M.R.; funding acquisition, P.A.R., M.R. and S.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research leading to these results has received funding from the Portuguese funding agency FCT—Fundação para a Ciência e a Tecnologia—within projects PTDC/FIS-NAN/0909/2014, UID/FIS/04559/2020 to LIBPhys-UNL from the FCT/MCTES/PIDDAC and the Bilateral Project entitled "Deteção de Estrogénio- um Contaminante Emergente em Corpos Hídricos" within the scope of "Cooperação Transnacional\_FCT (Portugal)-CAPES (Brazil) 2018".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** C. Magro acknowledges NOVA.ID of NOVA-FCT for her post-doc fellowship.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Abstract* **Antimony Tin Oxide—Prussian Blue Screen-Printed Electrodes for Electrochemical Sensing of Potassium Ions †**

**Cecilia Lete 1, Mariana Marin <sup>1</sup> , Francisco Javier del Campo 2,3,\* , Ioana Diaconu <sup>4</sup> and Stelian Lupu 4,\***


**Abstract:** In this work, the characterization and the electro-analytical applications of antimony tin oxide (ATO)–Prussian blue (PB) screen printed electrodes (SPE) are presented. The ATO conducting particles have been used recently in the development of screen-printed electrodes due to their excellent spectroelectrochemical properties. PB is a transition metal hexacyanoferrate with high electrocatalytic properties towards various biologically active compounds like hydrogen peroxide, besides its outstanding electrochromic properties. A combination of ATO and PB ingredients into a screen-printing paste provided a versatile and cost-effective way in the development of novel electrode materials for electrochemical sensing. The ATO-PB electrode material displayed good electrochemical properties demonstrated by means of cyclic voltammetry and electrochemical impedance measurements. In addition, the PB provided a high selectivity towards potassium ions in solution due to its zeolitic structures and excellent redox behavior. The cyclic voltammetric responses recorded at the ATO-PB-SPE device in the presence of potassium ions revealed a linear dependence of the cathodic peak current and cathodic peak potential of the Prussian blue/Everitt's salt redox system on the potassium concentrations ranging from 0.1 to 10 mM. This finding could be exploited in the development of an electrochemical sensor for electro-inactive chemical species. The potential application of the ATO-PB electrode in the electrochemical sensing of electro-active species like caffeic acid was also studied. An increase of the anodic peak current of the PB/ES redox wave in the presence of caffeic acid was observed. These results point out to the potential analytical applications of the ATO-PB electrode in the sensing of both electro-active and electro-inactive species.

**Keywords:** antimony tin oxide; Prussian blue; screen-printed electrodes; cyclic voltammetry; electrochemical impedance spectroscopy; electrochromic material; electrochemical sensing

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/CSAC2021-10639/s1.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data are available in the PP presentantion.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Citation:** Lete, C.; Marin, M.; del Campo, F.J.; Diaconu, I.; Lupu, S. Antimony Tin Oxide—Prussian Blue Screen-Printed Electrodes for Electrochemical Sensing of Potassium Ions. *Chem. Proc.* **2021**, *5*, 59. https://doi.org/10.3390/ CSAC2021-10639

Academic Editor: Huangxian Ju

Published: 1 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

### *Proceeding Paper* **Towards Low Temperature VOCs Chemoresistors: Graphene Oxide Versus Porphyrin-Based Materials †**

**Eleonora Pargoletti 1,2 , Francesca Tessore 1,2 , Gabriele Di Carlo 1,2, Gian Luca Chiarello <sup>1</sup> and Giuseppe Cappelletti 1,2,\***


**Abstract:** The sensing of gas molecules is of fundamental importance for environmental monitoring, the control of chemical processes, and non-invasive medical diagnostics based on breath analysis in humans. Herein, the synthesis of hybrid materials (SnO2/graphene oxide-GO and SnO2/porphyrins composites) with ad hoc properties led to chemoresistors able to reduce the acetone sensing temperature, guaranteeing acceptable LOD values. As such, boosted potentialities, especially in terms of tuned selectivity and low water interference, may be obtained.

**Keywords:** VOC chemoresistors; hybrid materials; low-T sensing

**Citation:** Pargoletti, E.; Tessore, F.; Carlo, G.D.; Chiarello, G.L.; Cappelletti, G. Towards Low Temperature VOCs Chemoresistors: Graphene Oxide Versus Porphyrin-Based Materials. *Chem. Proc.* **2021**, *5*, 60. https://doi.org/ 10.3390/CSAC2021-10418

Academic Editor: Elisabetta Comini

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Volatile Organic Compounds (VOCs) are a huge class of molecules emitted from a large variety of both biogenic and anthropogenic sources [1]. They are considered as a critical factor for air pollution, and they give rise to serious problems for both the environment and human health [1,2] due to their easy diffusivity, volatility, and toxicity even at low concentrations [3]. Moreover, some VOCs, present in the human's breath can be considered as biomarkers of specific illnesses, as they are strictly correlated to several metabolic processes. Among them, acetone can be considered a biomarker for type I diabetes as its concentration in breath varies from 300 to 900 ppb in healthy people to more than 1800 ppb for diabetics [4].

For all these reasons, the monitoring of these compounds has become mandatory. A promising solution for the detection and quantification of VOCs consists in the implementation of chemoresistive gas sensors, based on the electrical resistance variation of the sensing material in the presence of target molecules. The key to facing this challenge is the development of miniaturized chemical sensors, capable of selective sensing a few ppb of VOCs, giving stable and reproducible responses in the presence of high concentrations of interfering species, such as water vapor and other gases [5].

Notably, n-type semiconductor metal oxide (MOS such as SnO2, WO3, ZnO, and TiO2) devices have already been used quite extensively for several applications. They are compact, low-cost, easy to produce and use, and able to detect a wide variety of gaseous species [6,7]. Although these features make such kind of sensors a convenient alternative to the traditional and most sophisticated analytical techniques (e.g., mass spectrometry and gas chromatography), there are also some drawbacks. Specifically, they can only operate at high temperatures (200–400 ◦C), showing short lifetime and low selectivity, so that it is difficult to selectively analyze multiple species in complex matrices [3].

To overcome these problems, the coupling of metal oxides with other matrices (such as carbonaceous- or porphyrin-based materials) [8,9] seems to be the key factor to create nanocomposites capable of sensing at low temperatures (<100 ◦C), simultaneously achieving good selectivity and sensitivity (ppb level) towards a target compound.

Therefore, the present work is aimed at evaluating and comparing the sensing performances of SnO2 coupled with different porphyrins and graphene oxide (GO) towards acetone molecules, at mild temperatures (150 ◦C and 75 ◦C) under UV light, in a fixed SnO2/matrix weight ratio.

#### **2. Materials and Methods**

#### *2.1. Hybrid Synthesis, Electrodes Preparation and Sensing Tests*

SnO2 nanoparticles were chosen to be grown onto graphene oxide (GO) material by following a very easy hydrothermal method, already reported in our previous works [6–8]. According to earlier studies, we adopted 32:1 salt precursor-to-GO weight ratio since it resulted as the optimal one in terms of sensing performances at low operating temperatures [3].

Three different ZnII porphyrins were synthesized according to previous papers [10–12], namely ZnTPP, ZnTPP(F20), and ZnTPP(F20CN); their relative chemical structures together with the UV-Vis spectra in CH2Cl2 solution are reported in Figure 1. The UV-Vis patterns are the ones typically observed for porphyrins metal complexes [13], with an intense (<sup>ε</sup> ≈ <sup>10</sup><sup>5</sup> <sup>M</sup>−<sup>1</sup> cm<sup>−</sup>1) Soret or B band at 400–450 nm and two weaker (<sup>ε</sup> ≈ <sup>10</sup>3–104 <sup>M</sup>−<sup>1</sup> cm<sup>−</sup>1) Q bands at 500–600 nm (see the inset in Figure 1).

**Figure 1.** Investigated ZnII porphyrins and their UV-Vis spectra.

Then, the SnO2-porphyrin sensors were obtained by depositing onto Pt-based interdigitated electrodes (IDEs), through a hot-spraying method [6–8], a first layer of porphyrin (0.6 mg mL−<sup>1</sup> in EtOH) followed by a thin film of SnO2 (2.5 mg mL−<sup>1</sup> in EtOH). The mass ratio between SnO2 and porphyrin (verified by a microbalance) is 32:1 as in the case of the SnO2/GO composite.

Sensing measurements towards acetone at 150 ◦C and 75 ◦C under UV light (Jelosil HG500 lamp; effective irradiation power: 30 mW cm−2) were performed, adopting the chamber, already described elsewhere [14]. The tests have been carried up to 150 ◦C since the porphyrin complexes degrade at higher temperatures [15]. The sensor response is reported as: (Rair/Racetone) − 1, where Rair is the film resistance in air and Racetone is the film resistance at a given concentration of the acetone gas. We also computed the sensor response (tres) and the recovery times (trec).

#### *2.2. Powders and Porphyrins Characterizations*

SnO2 and SnO2/GO samples were characterized by specific surface area measurements (Micromeritics Tristar II 3020, (Norcross, GA, USA), X-ray Powder Diffraction (XRPD, Philips PW 3710, Texas City, TX, USA) analyses and Diffuse Reflectance Spectroscopy (DRS, Shimadzu UV-2600, Kyoto, Japan) to evaluate powders optical band gaps (Eg) by means of Kubelka-Munk equation [3,8].

The goodness of the as-synthesized powders has been verified through 1H- and 19F-NMR spectroscopy in CDCl3. The NMR spectra are fully in agreement with the recent literature [10–12].

#### **3. Results and Discussion**

Hybrid sensing materials, such as SnO2-porphyrins and SnO2-graphene oxide composites, arouse interest thanks to the possible complementary features between the two components, showing cooperative and synergistic behavior [6,9].

In the case of SnO2/GO hybrid, the formation of nano-heterojunctions with a threedimensional SnO2 network has been verified by a combination of physical and chemical characterizations. Specifically, Table 1 reports the specific surface area (*S*BET), together with total pores volume (*V*tot. pores), the average domain size by X-ray diffraction analysis (<dXRPD>) and optical band gap (Eg) of pure SnO2 and GO, together with the composite (SnO2/GO) sample. Moreover, HRTEM, XPS, Raman and responsivity analyses (already reported in our previous works [3,8]) corroborate that a nano-heterojunction occurs when tin oxide particles are grown onto GO sheets, allowing an intimate contact between the semiconductor and the graphene oxide matrix. This fact leads to a signal intensity of three times higher with respect to that of the pure SnO2 (Figure 2a) in the case of 20 ppm of acetone at 150 ◦C under UV light. Notably, the SnO2/GO was able to reach a LOD of 100 ppb of acetone thanks to the synergistic effect between n-type MOS and p-type GO [3,6]. Furthermore, the response (tres around 60 s) and recovery (trec around 90 s) times seem to be comparable with those of pure SnO2.

**Table 1.** Surface area (*S*BET), total pore volume (Vtot. pores), crystallite domain size by XRD analysis (<dXRD>) and optical band gap (Eg, by Kubelka-Munk extrapolation).


**Figure 2.** Sensor response intensities for both pure SnO2 and hybrid materials towards 20 ppm of acetone under UV light at (**a**) 150 ◦C and (**b**) 75 ◦C.

Then, the sensors obtained overlapping a SnO2 and porphyrin layers were tested. Specifically, to evaluate the effect of the porphyrin matrix on the sensing properties of SnO2, the responses of SnO2/ZnTPP and SnO2/ZnTPP(F20) were compared to that of pristine SnO2 as shown in Figure 2a. The combination of SnO2 with the ZnTPP porphyrin matrix has undoubtedly a beneficial effect on the sensing performance, as reported in the recent literature [9,15,16]. Li et al. observed that light has a beneficial influence in the gas sensing by ZnO nanorods functionalized with porphyrins [16]. They asserted that light activates a charge transfer from the porphyrin to the ZnO, simultaneously creating a depletion of electrons, which favors the charge transfer from the donor-absorbed species.

Moreover, the main interfering species in the gas sensing process is humidity, especially at low T: Chen et al. [17] observed that the moisture can adsorb on the semiconductor oxide surface, interacting with the acetone molecules and leading to a dramatic change in the final sensor behavior. Indeed, a fluorine modified porphyrin, named as SnO2/ZnTPP(F20), was synthesized and tested, since fluorine atoms may confer hydrophobic character leading to a possible reduction of the water interference. Unfortunately, no positive results were obtained and the signal intensity halved compared to that of the pure SnO2 powders, probably due to the strong electron density attractor capability of F-groups [10–12]. Notably, the sensor response of SnO2/ZnTPP(F20CN) at 150 ◦C (Figure 2a) is the most intense one among the tested hybrid materials, since this ZnII porphyrin carries a cyano-acrylic moiety able to bind SnO2 and to impart a proper directionality to charge-injection [11,12]. Moreover, the CN group acts as a buffer towards the strong electron acceptor behavior of F atoms, guaranteeing concomitantly the desired hydrophobicity to prevent the water interference. All the porphyrin-based sensors reached LOD values of 200 ppb at 150 ◦C, notwithstanding an increase in the response times of around 25–30% and the recovery times significantly longer (around 200 s).

Finally, further tests were carried out at 75 ◦C (Figure 2b): while no acetone response (20 ppm) was appreciable in the case of pure SnO2, a reversed behavior in conductance with SnO2/GO sample occurred. This phenomenon is reported to be typical of metal oxide semiconductors, operating at low temperatures due to a greater amount of adsorbed oxygen species and moisture [17]. Instead, under these conditions SnO2/ZnTPP(F20CN) produces a positive response, even if the LOD is 600 ppb, corroborating the synergistic effect between the fluorine species and the anchor group.

We believe that these findings can provide guidelines for the engineering of miniaturized chemoresistive sensors for low-temperature detection of acetone molecule. The excellent performances of the SnO2-GO nano-heterojunctions and especially of SnO2/ZnTPP(F20CN) composite can pave the way for the development of tunable low-cost materials for the fabrication of optoelectronic devices for various applications.

**Author Contributions:** Conceptualization, E.P., F.T. and G.C.; methodology, E.P., G.L.C. and G.D.C.; validation, E.P.; formal analysis, E.P.; investigation, E.P.; data curation, E.P. and G.D.C.; writing original draft preparation, G.D.C.; writing—review and editing, E.P., F.T. and G.D.C.; supervision, G.D.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work received financial support from the Università degli Studi di Milano through the "PSR2019 Azione A" projects.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression †**

**Rachid Laref \*, Etienne Losson , Alexandre Sava and Maryam Siadat**

Laboratoire de Conception, Optimisation et Modélisation des Systèmes, LCOMS EA 7306 Université de Loraine, 57000 Metz, France; etienne.losson@univ-lorraine.fr (E.L.); alexandre.sava@univ-lorraine.fr (A.S.); maryam.siadat@univ-lorraine.fr (M.S.)

**\*** Correspondance: rachid.laref@cnrs-imn.fr

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors' responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

**Keywords:** partial least square regression; gas sensors; electrochemical sensors; air pollution monitoring

#### **1. Introduction**

Urban air pollution is a major preoccupation [1]. Government organizations encourage research on low-cost gas sensors to improve their performances in order to complement the actual air pollution monitoring networks by providing better spatiotemporal resolution of the pollutants' spread [2]. Today, low-cost sensors such as electrochemical sensors can sense most pollutant gases at the magnitude of parts per billion (ppb) [3]. However, several limitations inhibit systems based on these sensors to reach high performance similar to the regular instruments [4]. Among these limitations is the influence of environmental factors—essentially, the temperature and humidity and the interfering gases present in the ambient air, particularly in the case of measuring O3 and NO2 [5]. The existing commercial electrochemical sensors for measuring O3 respond simultaneously to O3 and NO2, without discrimination, because NO2 and O3 are reducible at similar potentials on carbon or gold electrodes [6]. Therefore, the responses of these sensors are proportional to the combined concentration of O3 and NO2. This nonselectivity of sensors becomes an obstacle for air monitoring applications where NO2 and O3 are present simultaneously with the same order of concentration magnitude. In this paper, we evaluate electrochemical sensors for O3 and NO2 for in field and in real application conditions. We propose to calibrate simultaneously the two sensors using Partial Least Square regression (PLS) while considering also the temperature and humidity variations. The remainder of this paper is organized as follows: we present first the experimental set up and data collection, then, the calibration procedure with results, and finally, a conclusion.

**Citation:** Laref, R.; Losson, E.; Sava, A.; Siadat, M. Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression. *Chem. Proc.* **2021**, *5*, 61. https://doi.org/10.3390/ CSAC2021-10622

Academic Editor: Huangxian Ju

Published: 6 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **2. Experiment Set Up and Data Collection**

In this work, we focused on measuring the concentration of O3 and NO2 in ambient air because they are the principal pollutant gases in French cities that are still exceeding the limited values defined by the European directives [7]. Therefore, we designed a device constituted of two electrochemical sensors provided by Alphasense LTD—NO2- B41F and OX-B431—dedicated to measure NO2 and oxidizing gases, respectively. The device also contains the sensors' conditioning circuits, the gas exposure chamber, and the data acquisition unit. The conditioning circuits consist of potentiostat circuits that allow amplifying and converting the sensor electrode currents to voltages. The device is placed inside the air monitoring station managed by ATMO Grand Est agency. This station is located beside a highway, crossing Metz city, France. Our device works in dynamic mode for air sampling; thus, a pump and a mass flow controller are placed on the exposure chamber exit to generate a constant and continuous airflow by suction (Figure 1). We set the airflow rate to 500 mL/min, in order to obtain the same airflow rate as the ATMO Grand Est O3 and NO2 analyzers. The collected data represent the voltages of the sensor responses with a data sampling frequency of 200 Hz. Sensor responses are then averaged over a period of 10 s and recorded on a computer using Matlab software. Finally, recorded data are averaged again every 15 min in order to comply with the reference data provided by ATMO Grand Est. Data are collected continuously from 22 February 2019 to 14 April 2019.

**Figure 1.** Experiment setup diagram and the schematic of filtered and unfiltered electrochemical sensors.

#### **3. Sensors Calibration**

To quantify O3 and NO2 concentrations, the manufacturer Alphasense recommends the use of a pair of electrochemical sensors: a model OX-B431 that responds to both gases (O3 + NO2) and a model NO2-B43F that responds only to NO2. The NO2-B43F sensor is equipped with a manganese dioxide filter, which catalyzes O3 into oxygen, thus preventing the sensor from responding to O3 present in the environment (Figure 1). To determine the O3 concentration, the contribution of NO2 to the response of the OX-B431 sensor must be removed. Therefore, we first need to calculate the NO2 concentration with the NO2-B43F sensor and then subtract it from the concentration provided by the OX-B431 sensor. The calibration procedure of this pair of sensors is performed as follows:

• Calibrate the NO2-B43F sensor for measuring NO2 according to Equation (1):

$$\text{I[NO}\_2\text{]} = \left(\text{WE}\_{\text{NO2-B43F}} - \text{AE}\_{\text{NO2-B43F}}\right) \propto\_1 + \alpha\_2.\tag{1}$$

where [NO2] is the concentration of the NO2; WENO2-B43F and AENO2-B43F are signals of the working and auxiliary electrodes of NO2-B43F sensor, respectively; α<sup>1</sup> and α<sup>2</sup> are regression coefficients that can be determined by a simple linear regression.

• Calibrate the OX-B431 sensor to measure the mixture (NO2 + O3) according to Equation (2):

$$\left[\rm{NO}\_{2} + \rm{O}\_{3}\right] = \left(\rm{WE}\_{\rm{OX}\cdot\rm{B431}}\right) - \rm{AE}\_{\rm{OX}\cdot\rm{B431}})\left[\rm{b}\_{1} + \rm{b}\_{2}\right] \tag{2}$$

where WEOX-B431 and AEOX-B431 are signals of the working and auxiliary electrodes of OX-B431 sensor, respectively; b1 and b2 are regression coefficients determined by a simple linear regression.

The concentration of O3 will be the difference between the concentration obtained by the OX-B431 sensor and the concentration obtained by the NO2-B43F sensor:

$$\text{[O}\_3\text{]} = \text{[NO}\_2 + \text{O}\_3\text{]} - \text{[NO}\_2\text{]},\tag{3}$$

The concentrations of both O3 and NO2 are typically 5 to 120 μg/m<sup>3</sup> at the roadside, so intelligent data analysis is required to differentiate each gas concentration. Our proposition is to combine both sensors' signals in the same equation plus the temperature and humidity variations:

$$\text{C}\_{1}\text{[NO}\_{2}\text{]}=\text{c}\_{0}+\text{c}\_{1}\text{ WE}\_{\text{NO2-B43F}}+\text{c}\_{2}\text{ AF}\_{\text{NO2-B43F}}+\text{c}\_{3}\text{ WE}\_{\text{OX-B45I}}+\text{c}\_{4}\text{ AF}\_{\text{OX-B45I}}+\text{c}\_{5}\text{ T}+\text{c}\_{6}\text{ H},\tag{4}$$

$$\text{H}\_{\text{1}}\text{[O}\_{3}\text{]} = \text{d}\_{0} + \text{d}\_{1}\text{ W}\text{E}\_{\text{NO2-B43F}} + \text{d}\_{2}\text{ A}\text{E}\_{\text{NO2-B43F}} + \text{d}\_{3}\text{ W}\text{E}\_{\text{OX-B431}} + \text{d}\_{4}\text{ A}\text{E}\_{\text{OX-B431}} + \text{d}\_{5}\text{ T} + \text{d}\_{6}\text{ H},\tag{5}$$

where c0, c1 ... .c6 and d0, d1 ... .d6 are regression coefficients determined by using PLS [8]; T and H are the temperature and humidity, respectively.

The comparison between calibration of each sensor individually and the combination of the two sensors' signals with temperature and humidity variation using PLS regression shows that the concentration estimation is better in the case of using PLS regression than the case of calibrating each sensor individually. Figure 2 illustrates that in the case of using PLS regression, the root-mean-square errors (RMSE) are 4.71 μg/m3 and 6.89 μg/m3 for NO2 and O3, respectively, whereas in the case of using each sensor individually, the RMSE values were 6.34 μg/m3 and 8.76 μg/m3, respectively. We note also that the estimation of NO2 is better than the estimation of O3 in both calibration cases. The reason behind this is that NO2 estimation depends essentially on one sensor, whereas the estimation of O3 depends on both sensors.

**Figure 2.** Correlation between reference concentration and estimated concentration of NO2 and O3.

#### **4. Conclusions**

In this work, electrochemical sensors calibration is proposed using PLS regression. First, we deployed a device to collect data in real outdoor conditions; then, we proposed multiple linear regression to estimate simultaneously nitrogen dioxide and ozone concentration. We found that the use of a pair with PLS regression is better than calibrating each sensor individually, as the RMSE reduced from 8.76 μg/m3 to 6.89 μg/m<sup>3</sup> for ozone concentration estimation.

**Author Contributions:** The work presented here was carried out in collaboration between all authors. Conceptualization, R.L.; methodology, R.L.; validation, E.L., A.S., and M.S.; writing—original draft, R.L.; writing—review and editing, E.L., A.S., and M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank ATMO GRAND EST agency for their support, including access to monitoring stations and data from their analyzers. The authors would also to thank Damien Durant from ATMO GRAND EST, head of metrological unit for his assistance and his helpful advice.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Tropomyosin Analysis in Foods Using an Electrochemical Immunosensing Approach †**

**Ricarda Torre 1, Maria Freitas <sup>1</sup> , Estefanía Costa-Rama 2, Henri P. A. Nouws 1,\* and Cristina Delerue-Matos <sup>1</sup>**


**Abstract:** A screen-printed carbon electrode was used as the transducer for the development of an electrochemical immunosensor for the determination of tropomyosin (a major shrimp allergen) in food samples. Monoclonal and polyclonal antibodies were used in a sandwich-type immunoassay. The analytical signal was electrochemically obtained using an alkaline phosphatase-labelled secondary antibody and a 3-indoxyl phosphate/silver nitrate substrate. The total assay time was 2 h 50 min and allowed the quantification of tropomyosin between 2.5 and 20 ng mL<sup>−</sup>1, with a limit of detection of 1.7 ng mL−<sup>1</sup> The immunosensor was successfully applied to the analysis of commercial food products.

**Keywords:** seafood allergy; tropomyosin; shrimp; food allergy; screen-printed electrodes; electrochemical biosensor

**Citation:** Torre, R.; Freitas, M.; Costa-Rama, E.; Nouws, H.P.A.; Delerue-Matos, C. Tropomyosin Analysis in Foods Using an Electrochemical Immunosensing Approach. *Chem. Proc.* **2021**, *5*, 62. https://doi.org/10.3390/ CSAC2021-10471

Academic Editor: Ye Zhou

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Over the past few years, food allergies have increasingly been regarded as a significant worldwide public health problem. Among shellfish allergies, shrimp is the predominant crustacean causing over 80% of allergic reactions that can result in severe hypersensitivity such as urticaria and asthma, and it is a major cause of anaphylaxis [1,2].

Tropomyosin (TPM), a major common allergenic protein found in seafood, is relatively resistant to peptic acidic digestion, which causes a continuous effect of the protein on the immune system. To protect the consumer from harmful allergens and potentially life-threatening reactions, food manufacturers are required to label and highlight shellfishallergenic ingredients on food packages [3].

Currently, multiple technical approaches have been developed to identify the presence of shrimp tropomyosin in food, including enzyme-linked immunosorbent assays (ELISA), DNA detection, polymerase chain reaction (PCR), microarray and qualitative/semi-quantitative lateral flow assays. Although ELISA is the most commonly used method for TPM detection and quantification, it presents some disadvantages such as the long and tedious steps in the analysis procedure, long analysis times and high costs [4,5]. An alternative way to determine TPM in foods is through the use of electrochemical immunosensors. These sensors provide highly selective, sensitive, fast and cheap analysis and are suitable for in situ applications. Therefore, in this work, a simple voltametric immunosensor for the determination of TPM in commercial food products was developed. The immunoassay was based on a sandwich-type assay using screen-printed carbon electrodes (SPCE) as transducers. Monoclonal and polyclonal antibodies were used to capture and detect TPM. To obtain the analytical signal, an alkaline phosphatase-labelled secondary antibody and

3-indoxyl phosphate/silver nitrate (enzymatic substrate) were employed; the enzymatically deposited silver was analyzed by linear sweep voltammetry [6–8].

The applicability of the immunosensor was assessed by analyzing different food samples.

#### **2. Materials and Methods**

#### *2.1. Instrumentation*

Linear sweep voltametric analyses were performed using an Autolab PGSTAT204 potentiostat/galvanostat from Methrohm Autolab. Disposable screen-printed carbon electrodes (DRP-110) with a carbon working electrode (WE, d = 4 mm), a carbon counter electrode and a silver pseudoreference electrode were purchased from Methrohm DropSens.

#### *2.2. Reagents and Solutions*

Tris(hydroxymethyl)aminomethane (Tris, ≥99.8%), magnesium nitrate hexahydrate (Mg(NO3)2, 99%), nitric acid (HNO3, ≥65%), 3-indoxyl phosphate (3-IP, ≥98%), silver nitrate (AgNO3, ≥99.9995%), β-casein from bovine milk (≥98%), and bovine serum albumin (BSA) were obtained from Sigma-Aldrich.

Mouse IgG1 monoclonal antibody (capture antibody, C-Ab), purified natural shrimp tropomyosin standard (antigen) and rabbit polyclonal antiserum shrimp tropomyosin (detection antibody, D-Ab) were purchased from Indoor Biotechnologies. An alkaline phosphatase goat anti-rabbit IgG antibody (AP-Ab) was supplied by Invitrogen. Throughout the work, ultra-pure water (resistivity = 18.2 MΩ cm), obtained from a Millipore (Simplicity 185) water purification system, was used. Working solutions of BSA, the antibodies and the antigen were prepared in 0.1 M Tris-HNO3 pH 7.4 buffer (Buffer 1, B1). A second buffer (B2, 0.1 M Tris-HNO3 pH 9.8 containing Mg(NO3)2 (2 × <sup>10</sup>−<sup>2</sup> M)) was used to prepare the solution containing 3-IP (1 × <sup>10</sup>−<sup>3</sup> M) and AgNO3 (4 × <sup>10</sup>−<sup>4</sup> M).

#### *2.3. Sample Preparation*

Shrimp, shrimp sauce and crab and chicken paste were used to evaluate the immunosensor's applicability to food analysis. Samples were prepared as follows: (a) 1 g of sample was mixed with 10 mL of Tris-HNO3 (pH 8.2, 1% NaCl) at 60 ◦C during 15 min in a water bath; (b) the resulting suspension was then centrifuged at 2500 rpm for 20 min and (c) the supernatant was divided in aliquots and stored at −20 ◦C until use.

#### *2.4. Immunosensor Assay and Electrochemical Measurements*

The representative scheme of the immunosensor assay and detection strategy is presented in Figure 1. The WE of the SPCE was coated with C-Ab (10 μL, 20 μg mL−1) and left to incubate overnight at 4 ◦C. After rinsing the sensor with buffer B1, surface blocking was carried out using 40 μL of a 2-% (m/V) BSA solution during 30 min. After this, the sensor was washed with buffer B1 and incubated with 40 μL of a previously mixed (10 min before use) solution containing the antigen, the detection antibody (1:2000) and BSA (1% (m/V)) during 60 min. After rinsing with buffer B1, 40 μL of an AP-Ab solution (1:40,000) was placed on the sensor for 60 min. The sensor was then rinsed with buffer B2, and the enzymatic reaction was carried out by depositing 40 μL of a mixed solution containing 3-IP and silver nitrate on the SPCE for 20 min. LSV was used to record the analytical signal (potential range: −0.03 V to +0.4 V; scan rate: 50 mV/s). All analyses were performed in triplicate and carried out at room temperature (20 ± 1 ◦C).

**Figure 1.** Schematic representation of the developed immunoassay. (1) Screen-printed carbon electrode; (2) C-Ab immobilization; (3) addition of a mixture containing standard/sample and D-Ab; (4) addition of AP-Ab; (5) addition of the enzymatic substrate (3-IP) and silver ions; and (6) voltametric detection of Ag0.

#### **3. Results and Discussion**

#### *3.1. Optimization Studies*

The immunosensing strategy was based on a sandwich-type assay performed on bare SPCEs as transducers. In the first phase of the immunosensor development, two different surface blockers were tested: β-casein (2% (m/V)) and BSA (2% (m/V)). As can be observed in Figure 2, when BSA was used, the highest peak current intensity (*i*p) and signal-to-blank ratio (S/B) was obtained.

**Figure 2.** Peak current intensities (*i*p) obtained for the study of the surface blocker (casein and BSA, both at 2% (m/V)). Black bars: blank assay. White bars: TPM (10 ng mL<sup>−</sup>1). Results are presented as average <sup>±</sup> standard deviation (*<sup>n</sup>* = 3). Experimental conditions: C-Ab—10 <sup>μ</sup>g mL−1; D-Ab—1:250 dilution; AP-Ab—1:20,000 dilution; 3-IP—1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> M; and AgNO3—4.0 <sup>×</sup> <sup>10</sup>−<sup>4</sup> M.

In order to select the optimum concentrations of both the capture and detection antibodies, a standard solution of tropomyosin (10 ng mL−1) was used. First, for fixed dilutions of D-Ab (1:250) and AP-Ab (1:20,000), different C-Ab concentrations of between 2.5 and 20 μg mL−<sup>1</sup> were tested. The obtained results reveal that a concentration of 20 μg mL−<sup>1</sup> resulted in the highest peak current intensity and S/B ratio. After this and maintaining the AP-Ab dilution at 1:20,000, different D-Ab dilutions (between 1:250 and 1:12,000) were tested. The selected dilution was 1:2000 because the highest *i*<sup>p</sup> and lowest blank signal were obtained. After selecting the C-Ab concentration (20 μg mL<sup>−</sup>1) and D-Ab dilution (1:2000), different assay formats were studied in order to reduce the number of incubation steps and, subsequently, the assay time. Different steps were combined and the most adequate combination, the previous mixing of the antigen with the D-Ab, led to a 60-min reduction in the assay time. The next studies were performed to select the optimum AP-Ab dilution by testing dilutions of between 1:10,000 and 1:40,000. A 1:40,000 dilution was selected because a low blank signal and the highest S/B ratio were observed. After this, the AP-Ab incubation time was studied between 15 and 60 min, with the best

results obtained for the 60 min incubation time. A summary of the optimization studies is indicated in Table 1.

**Table 1.** Optimization of the different experimental variables involved in the construction of the immunosensor for TPM analysis.


#### *3.2. Analytical Performance*

To establish the performance characteristics of the immunosensor, standard solutions with different TPM concentrations (2.5–50 ng mL−1) were analyzed. A linear relationship was observed between 2.5 and 20 ng mL−<sup>1</sup> (*i*<sup>p</sup> (μA) = 0.787 (tropomyosin) (ng mL−1) + 5.45, r = 0.990, *n* = 5). Examples of voltammograms in the linear range (Figure 3a) and the calibration plot (Figure 3b) are shown in Figure 3. The limit of detection (LOD) was calculated as three times the standard deviation of the blank divided by the slope and the value obtained was 1.7 ng mL−1. The limit of quantification (LOQ) was calculated as 10 times the standard deviation of the blank divided by the slope, obtaining a concentration of 5.7 ng mL<sup>−</sup>1. The coefficient of variation of the method was <9%.

#### *3.3. Selectivity and Interference Studies*

The selectivity of the sensor towards TPM was evaluated by analyzing other allergens such as Ara h 1 (peanut allergen, 250 ng mL−1), Cyp C 1 (fish allergen, 20 ng mL−1) and Ovalbumin (GAL d 2, chicken egg allergen, 1% (m/V)). Examples of the obtained voltammograms are shown in Figure 3c. Besides these allergens, histamine (6.8 mg mL<sup>−</sup>1), a biogenic amine and the most important fish freshness indicator, was also included in this study. The signal for all these compounds was similar to the blank signal, confirming the selectivity of the proposed sensor. Besides this, TPM was mixed with each of the compounds to evaluate their interference in the analysis. The obtained signals were nearly the same as the one obtained for a 10-ng mL−<sup>1</sup> TPM solution, which indicates that the other allergens and histamine did not significantly interfere in the analysis.

#### *3.4. Applicability to Food Analysis*

The feasibility of the sensor for the determination of TPM in commercial food samples was tested. Shrimp, shrimp sauce and crab paste were analyzed, obtaining TPM concentrations of 80.42 ± 2.7 <sup>μ</sup>g g−1, 170.4 ± 1.80 ng g−<sup>1</sup> and 21.6 ± 4.13 ng g−1, respectively. The developed immunosensor was also used to detect the presence of TPM in chicken paste. As expected, this sample gave a negative result (no significant difference when compared with the blank signal), so the TPM concentration was below the sensor's LOD. Examples of the obtained voltammograms are shown in Figure 3d.

**Figure 3.** (**a**) Examples of voltammograms in the linear range (a—blank; b—2.5 ng mL−1; c—10 ng mL−1; d—12.5 ng mL−1; e—15 ng mL−1; and f—20 ng mL−1). (**b**) Calibration plot. (**c**) Examples of voltammograms obtained in the selectivity and interference studies: TPM (10 mg L−1, blue line, control) combined with Cyp C 1 (20 ng mL<sup>−</sup>1, red line) and Ovalbumin (1% (m/V), black line) and blank (0 ng mL−1, blue dashed line, control) with the addition of Cyp c 1 (200 ng mL−1, red dashed line) and Ovalbumin (1% (m/V), black dashed line). (**d**) Examples of voltammograms obtained in the analysis of food samples (shrimp sauce—black dashed line; shrimp—red line; crab paste—blue line; and chicken paste—green line). Experimental conditions: C-Ab—20 μg mL−1; BSA—2% (m/V); mixture of standard TPM solutions with D-Ab—1:2000; AP-Ab—1:40,000; 3-IP— <sup>1</sup> <sup>×</sup> <sup>10</sup>−<sup>3</sup> M; and AgNO3—4 <sup>×</sup> <sup>10</sup>−<sup>4</sup> M.

#### **4. Conclusions**

The current trends in analytical chemistry are focused on the development of simple and in situ analysis devices to ensure food safety. In this work, a simple immunosensor for tropomyosin analysis was developed. This immunoassay only takes 2 h 50 min, and it requires 40 μL of sample to perform the analysis. The sensor can determine tropomyosin in a concentration range between 2.5 and 20 ng mL−<sup>1</sup> and a limit of detection of 1.7 ng mL−<sup>1</sup> was achieved. The developed methodology fulfills the requirements of (bio)sensor construction such as small size and the use of low amounts of reagents and samples. Moreover, it allows the possibility of decentralized analysis, which could be useful for the control of tropomyosin, avoiding cases of food allergy.

**Author Contributions:** Conceptualization, M.F. and H.P.A.N.; methodology, M.F. and H.P.A.N.; validation, all authors; formal analysis, M.F. and R.T.; investigation, M.F. and R.T.; resources, H.P.A.N. and C.D.-M.; data curation, M.F. and R.T.; writing—original draft preparation, R.T.; writing—review and editing, M.F. and H.P.A.N.; visualization, R.T.; supervision, M.F., H.P.A.N., E.C.-R. and C.D.-M.; project administration, H.P.A.N.; funding acquisition, H.P.A.N. and C.D.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union (FEDER funds through COMPETE POCI-01-0145-FEDER-030735) and National Funds (Fundação para a Ciência e a Tecnologia) through the project PTDC/QUI-QAN/30735/2017—TracAllerSens—Electrochemical sensors for the detection and quantification of trace amounts of allergens in food products.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This work was also supported by Portuguese national funds (FCT/MCTES, Fundação para a Ciência e a Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior) through the project UIDB/50006/2020.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Simultaneous Sensing of Codeine and Diclofenac in Water Samples Using an Electrochemical Bi-MIP Sensor and a Voltammetric Electronic Tongue †**

**Hafsa El Youbi 1, Alassane Diouf 1,2, Benachir Bouchikhi <sup>2</sup> and Nezha El Bari 1,\***


**Abstract:** Codeine and diclofenac overdoses have been widely reported. Here, a biomimetic sensor (bi-MIP) was devised, and an electronic tongue was used to analyze water samples simultaneously containing both these drugs. The bi-MIP sensor limits of detection for diclofenac and codeine taken individually were 0.01 μg/mL and 0.16 μg/mL, respectively. Due to a cross-reactivity effect when using the bi-MIP sensor, the electronic tongue was shown to differentiate samples containing both analytes. The results confirm the feasibility of simultaneous detection of two target analytes via a bi-MIP sensor. Additionally, they demonstrate the ability of a multi-sensor to classify different water samples.

**Keywords:** drug analysis; molecularly imprinted polymer sensor; nanoparticles; electrochemical multi-sensor; chemometrics; water

#### **1. Introduction**

Diclofenac (DCF) and codeine (COD) are drugs administered to treat certain human health problems. Here the focus is first on DCF, which is a non-steroidal anti-inflammatory drug (NSAID), widely prescribed for the treatment of a wide variety of conditions. It reduces the need for morphine after surgery and is effective against menstrual pain and endometriosis. Although DCF has outstanding medical features, it is sometimes misused and can, as a result, easily move into the synovial fluid. This unfortunately leads to a reduction in the secretion of prostaglandins [1]. In consequence, the consumer can experience many health problems [2].

The second study focus is on COD, which is an opiate used clinically for its analgesic, antitussive and antidiarrheal properties. However, it is said to be addictive and can cause psychological damage to the patient if abused. Extreme consumption of COD can even cause death [3]. For these reasons, the World Health Organization (WHO), the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA), among other international organizations, have issued strict warnings about the adverse effects of COD [4].

Electrochemical methods are very good candidates for drug analysis [5]. This is attributed to their low cost, lower detection limits, wide range of potential windows, and ease of surface renewal.

Firstly, electrochemical devices based on molecularly imprinted polymers (MIPs) can be considered as good alternatives to conventional techniques. However, according to

**Citation:** El Youbi, H.; Diouf, A.; Bouchikhi, B.; El Bari, N. Simultaneous Sensing of Codeine and Diclofenac in Water Samples Using an Electrochemical Bi-MIP Sensor and a Voltammetric Electronic Tongue. *Chem. Proc.* **2021**, *5*, 63. https://doi.org/10.3390/ CSAC2021-10483

Academic Editor: Nicole Jaffrezic-Renault

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

our literature research, the MIP strategy has not yet been exploited for the simultaneous detection of these two analytes. Currently, the immobilization of MIPs, as a sensing element on portable electrochemical transducers, such as screen-printed electrodes (SPEs), offers an interesting approach. A study has been reported for the detection of dopamine and uric acid using MIP technology.

Secondly, as drugs are usually released in wastewater, and wastewater treatment plants are not totally efficient, this work focuses on the analysis of mineral water samples with different concentrations of the drugs in question. When multiple targets are to be detected, it is appropriate to use various electrical interfaces, such as multi-sensor systems.

The following content of this study is devoted to the qualitative analysis of drugs in mineral water samples using a voltammetric electron tongue (VE-Tongue) combined with chemometric methods. When using the bi-MIP sensor, a cross-reactivity effect due to the presence of several compounds was encountered. To avoid it, qualitative analysis via VE-Tongue can help to classify/discriminate drug samples with different concentrations of the drugs in question.

Taking all these points into consideration, the primary objective of this paper was to report on the development of an electrochemical sensor based on molecularly imprinted polymers for the simultaneous detection of DCF and COD. Electrochemical techniques, such as electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), and cyclic voltammetry (CV), were used to investigate the electrochemical behavior of the electrodes during the different steps of the bi-MIP sensor fabrication. Principal components analysis (PCA) was used to process the database from the VE-Tongue sensor array for the purpose of discriminating between water samples containing DCF and COD.

#### **2. Materials and Methods**

*2.1. Samples*

Five sets of mineral water samples were prepared for the electrochemical analysis:

Set 1: Mineral water sample used as reference sample which was not spiked;

Set 2: Mineral water samples spiked with different concentration of diclofenac (0.001, 0.01, 0.1, 1, 10, 100, 300, 500 μg/mL);

Set 3: Mineral water samples spiked with codeine at the same concentrations as described above;

Set 4: Mineral water samples spiked with diclofenac at the same concentrations as described above, but each containing 300 μg/mL codeine;

Set 5: Mineral water samples spiked with codeine at the same concentrations as described above, but each containing 300 μg/mL diclofenac.

#### *2.2. Instrumentation and Electrochemical Techniques*

Figure 1 shows the experimental setup used in this study. The five sets described above were studied using both detection systems (i.e., bi-MIP sensor and VE-Tongue).

The bi-MIP sensor was designed on a screen-printed gold electrode (Au-SPE).

The voltammetric electronic tongue (VE-Tongue) consisted of an array of 5 working electrodes made of gold, copper, glassy carbon, platinum, and palladium. A silver/silver chloride (Ag/AgCl) reference electrode and a platinum counter electrode completed the three-electrode configuration.

A computer interfaced to a potentiostat device was used for data acquisition. Using the potentiostat, electrochemical characterization techniques, including CV, DPV and EIS, were run.

These three established techniques were used for the electrochemical measurements. The CV was operated from −0.4 to 0.6 V at a scan rate of 30 mV/s. To investigate the surface properties of the bi-MIP sensor, the EIS was performed in an open circuit at a low AC potential of 10 mV amplitude and a frequency range of 0.1 to 50,000 Hz. The retention properties of the bi-MIP sensor were investigated using DPV over a potential range of

−0.2 to 0.3 V and a slew rate of 50 mV/s. All measurements were performed at room temperature (25 ◦C).

**Figure 1.** Graphical overview of the experimental setup.

#### *2.3. Bi-MIP Sensor Preparation*

Figure 2 illustrates the procedures for the bi-MIP sensor elaboration. Briefly, a layer of polyvinyl carboxylic chloride (PVC-COOH) was first assembled to modify the bare Au-SPE. Then, after activation of -COOH groups by 1-ethyl-3-(3-dimethylaminipropyl) carbodiimide (EDC) and N-hydrosuccinimide (NHS), a solution (1 mg/mL), containing simultaneously DCF and COD, was deposited on the modified electrode. After DCF and COD binding, a solution containing methacrylic acid, as the functional monomer, and silver nanoparticles (AgNPs) was immobilized. An extraction stage of template molecules followed to complete the fabrication of the bi-MIP sensor.

**Figure 2.** The development stages of the bi-MIP sensor.

#### *2.4. Data Analysis*

The multivariate responses of the VE-Tongue were processed by a known unsupervised method called PCA. This statistical technique reduces the dimensionality of the multivariate data while retaining maximum information on new variables called principal components (PCs) [6,7]. This allows for better visualization of the data and better interpretation of the analyzed samples.

#### **3. Results and Discussion**

#### *3.1. Biomimetic Receptor Assembly*

During the development of the biomimetic sensor, several immobilization procedures to form the sensitive layer were performed. After each step, the electrochemical behavior of the electrode was observed using a supporting electrolyte (PBS pH 7.4) containing electroactive species ([Fe(CN)6] <sup>4</sup>−/3−). For this purpose, the CV and EIS techniques were run in PsTRACE software. The results of these characterizations are presented in Figure 3. At each step of the sensor development, the electrochemical behavior of the electrode changed compared to the bare electrode. Moreover, the CV and EIS results were in good agreement.

**Figure 3.** Electrochemical signals corresponding to the development stages of the bi-MIP sensor: (**a**) cyclic voltammograms, (**b**) *Nyquist* diagrams.

#### *3.2. Bi-MIP Sensor Responses*

In the first step, the analysis of DCF alone (set 2), at different concentrations on the bi-MIP sensor, was performed using the differential pulse voltammetry (DPV) technique. The calibration curves related to these responses are shown in Figure 4. A clear decrease in the amplitude of the voltammograms was observed as the concentration of DCF increased, expressed in the linear regression equation shown in Figure 4a. The equation is y = −0.083Log (C)—0.355 with a determination coefficient R<sup>2</sup> = 0.93. The calculated detection limit was 0.01 μg/mL using the formula described by *DIOUF* et al. [8].

**Figure 4.** Calibration curves of the bi-MIP sensor with increasing concentrations of: (**a**) diclofenac (**b**) codeine, from 0.001 to 500 μg/mL.

Secondly, COD alone (set 3) was analyzed under the same conditions. The corresponding equation of the bi-MIP sensor responses (voltammograms) is shown in Figure 4b. Here, a similar trend to that of the DCF was obtained with a calibration equation of y = −0.089Log (C) − 0.347 with R<sup>2</sup> = 0.98. The limit of detection was 0.16 <sup>μ</sup>g/mL.

When detecting the two analytes individually, it was found that the bi-MIP sensor had almost equivalent sensitivity. However, because of cross-reactivity, the results for the simultaneous detection of both analytes by the bi-MIP were not satisfactory. An electronic tongue was used to explore a potential strategy to address this.

#### *3.3. PCA Analysis of the VE-Tongue Dataset*

Due to cross-reactivity and limitations encountered with the bi-MIP sensor, measurement of samples containing both target analytes simultaneously was performed using the VE-Tongue. After data pre-processing, principal components analysis (PCA) was used to classify the samples from all sets. The results are presented in Figure 5, which shows the projections of the experimental results onto a two-dimensional (2D) space formed by the first two principal components; 78.90% of the total variance of the data was explained by the first two PCs indicating significant pattern separation.

**Figure 5.** PCA plot showing the discrimination of the different sets using ΔI and Area as features. ΔI is the difference between the maximum current of the oxidation wave and the reduction wave. Area is the area of the VE-Tongue response (voltammogramme) using the trapezoidal method.

PCA was also applied to data after analysis of samples from set 4 and set 5, according to their concentrations.

Set 4 contained water samples with varying concentrations of DCF and a fixed concentration of COD (300 μg/mL) for each. As shown in Figure 6a, all samples in set 4 were well separated with only 85.87% of the total variance expressed by PC1 and PC2. In addition, the samples containing low and high concentrations of DCF clustered in the top right and bottom of the graph, respectively.

**Figure 6.** PCA plot showing the discrimination between set 1 and water samples of (**a**) set 4 and (**b**) set 5 at different concentrations using ΔI and Area as features.

In Figure 6b, the same trend is also observed for the analysis of samples in set 5. In this set, COD was varied but the concentration of DCF was maintained at 300 μg/mL. In the graph, the clean water sample and the spiked samples are well separated, with a score of 41.1% of the total variance, expressed as PC2 and PC3.

These results clearly show that the VE-Tongue was able to discriminate water samples containing several compounds at different concentrations.

#### **4. Conclusions**

In this study, a new bi-MIP-sensor-based electrochemical detection system for the detection of diclofenac and codeine was proposed. The principle of simultaneous detection was highlighted by using an electronic tongue combined with pattern recognition methods. The proposed analytical tools represent a breakthrough in water analysis.

**Author Contributions:** Conceptualization, N.E.B.; methodology, H.E.Y., N.E.B.; Software, B.B.; validation, N.E.B.; formal analysis, H.E.Y., N.E.B. investigation, N.E.B., H.E.Y.; resources, N.E.B.; data curation, N.E.B., H.E.Y.; writing—original draft preparation, H.E.Y., N.E.B., A.D.; writing—review and editing, H.E.Y., N.E.B.; visualization, supervision, N.E.B.; project administration, N.E.B.; funding acquisition, N.E.B.; Software: B.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Authors gratefully acknowledge Moulay Ismaïl University of Meknes for financial support of the project "Research support".

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Electrochemical Detection of Fenthion Insecticide in Olive Oils by a Sensitive Non-Enzymatic Biomimetic Sensor Enhanced with Metal Nanoparticles †**

**Youssra Aghoutane 1,2 , Nezha El Bari 1,\*, Zoubida Laghrari <sup>1</sup> and Benachir Bouchikhi <sup>2</sup>**


**Abstract:** Fenthion, an organophosphate insecticide, is a cholinesterase inhibitor and is highly toxic. An electrochemical sensor based on molecularly imprinted polymer is developed here for its detection. For this purpose, 2-aminothiophenol mixed with gold nanoparticles was immobilized on screenprinted gold electrodes. The FEN pattern was then fixed before being covered with 2-aminothiophenol. Cyclic voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy methods were used for the electrochemical characterization. The low detection limit was 0.05 mg/Kg over a range of 0.01–17.3 μg/mL. The sensor was successfully applied for the determination of FEN in olive oil samples with high recovery values.

**Keywords:** fenthion; molecularly imprinted polymer (MIP); screen-printed gold electrode (Au-SPE); electrochemical sensor; olive oils

#### **1. Introduction**

Olive oil production is located in the countries of the Mediterranean basin, specifically in Spain, Portugal, Italy, Greece, Turkey, Tunisia and Morocco [1]. However, production in other countries, such as Australia and the United States, is increasing.

Organophosphorus insecticides are the pesticides used in the largest quantities in olive groves to control pests. The most commonly used are those belonging to the class of organophosphate insecticides, because of their relatively low persistence under natural conditions, their ease of synthesis, their low cost and their high effectiveness in eradicating insects. Fat-soluble pesticides tend to concentrate in oils and toxic residues in lipids have been reported [2,3]. In addition, however, residues of POs in the environment present significant risks to the ecosystem and agricultural products; due to their lack of specificity, they affect the nervous system of non-target species, such as mammals, birds and aquatic fauna. Fenthion (FEN) is among the most commonly used pesticides [4]. The Codex Alimentarius Commission of the Food and Agriculture Organization of the United Nations (FAO) have set maximum residue limits (MRLs) for pesticides in olives and olive oil (e.g., 1 and 2 mg/kg for FEN) [5]. Many different detection methods have been used for the determination of residues of organophosphorus pesticides in olive oil. The most commonly used techniques are gas chromatography (GC) methods, which require the extraction of pesticides from samples. Reversed phase liquid chromatography–gas chromatography was also applied to olive oil [6]. These techniques are generally expensive, and require large quantities of samples and organic solvents as well as cleaning and preconcentration steps. Complementary analytical methods, such as enzymatic biosensors, which are based on

**Citation:** Aghoutane, Y.; Bari, N.E.; Laghrari, Z.; Bouchikhi, B. Electrochemical Detection of Fenthion Insecticide in Olive Oils by a Sensitive Non-Enzymatic Biomimetic Sensor Enhanced with Metal Nanoparticles. *Chem. Proc.* **2021**, *5*, 64. https://doi.org/10.3390/ CSAC2021-10773

Academic Editor: Huangxian Ju

Published: 17 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

the inhibition of acetylcholinesterase, were also developed for the detection of FEN [6]. However, enzymes have drawbacks such as difficulty in purification due to their instability, sensitivity and selectivity, which often depend on the nature of the materials. Immunosensing applications are also used for the FEN detection of organophosphorus insecticides, such as indirect enzyme-linked immunosorbent assay (ELISA) [7] and electrochemical immunosensor [8] applications. Their main limitations lie in the availability of antibodies and the fact that they cannot be used for the determination of low-molecular-weight hapten analytes [9,10].

Alternative methods based on molecularly imprinted polymers (MIPs) have also been adopted. They can mimic the functions of biological receptors but with fewer stability constraints. There are few applications of MIPs for the detection of FEN [11].

Due to the growing concern about the presence of pesticide residues in olive oil, it is necessary to implement procedures that can be applied in the field, with sensitive and selective detection.

The objective of this work was to develop a new low-cost, selective and highly sensitive electrochemical sensor, based on MIP, for the determination of FEN in olive oil samples.

This electrochemical sensor was fabricated by immobilizing a 2-aminothiophenol (2-ATP) complex mixed with gold nanoparticles (AuNPs) onto a screen-printed gold electrode (Au-SPE) via Au-S bonds. Then, the FEN template was bound onto Au-SPE/ATP-AuNPs before being coated with 2-ATP. The synthesis process of the electrochemical MIP sensor was straightforward. More generally, we believe that the results obtained open up new opportunities to detect other organophosphate insecticides in various food products and in the environment.

#### **2. Materials and Methods**

#### *2.1. Reagents and Solutions*

Fenthion (FEN), malathion (MAL), dimethoate (DMT), ethanol (99.8%), methanol, hydrochloric acid (HCL), acetonitrile, gold nanoparticles (AuNPs), potassium chloride, 2-aminothiophenol (2-ATP), phosphate-buffered saline (PBS) and ferri-ferrocyanide (K4[Fe(CN)6], K3[Fe(CN)6]), were all purchased from Sigma-Aldrich, Saint-Quenti-Fallavier, France. Ultra-pure water was used throughout the experiments.

#### *2.2. Synthesis of MIP and NIP Materials on the Gold Electrode*

As shown in Figure 1, the overall process for the preparation of the MIP sensor can be summarized by the following steps: First, the functionalization process was performed by immobilizing 0.1 M of 2-aminothiophenol (2-ATP), mixed with 1 mL of AuNPs, on the surface of the gold electrodes followed by incubation for 12 h at room temperature. In the second step, the FEN pattern was deposited on the modified surface and incubated for 2 h at room temperature. In the third step, the second layer containing 5 mM 2-ATP was electropolymerized with 0.1 M KCl and 0.05 M PBS (pH = 6.8) over the potential range (−0.2 V to 0.6 V) at a scan rate of 100 mV/s for 10 cycles. Finally, the printed pattern was extracted in HCl solution (0.5 M) for 20 min. Electrochemical detection of FEN by the MIP sensor was performed by placing 10 μL of each concentration of FEN on the working electrode for 30 min. The electrochemical characteristics of the stepwise MIP sensor fabrication were studied in a [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> 5 mM solution containing PBS (pH = 7.2). The redox probe [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> was chosen as a marker to study the changes at the electrode surface after each step of the sensor preparation. Similarly, a non-printed film (Au-SPE/NIP) was prepared using the same procedure, but without adding the template into the polymer solution. This was carried out in order to ensure that the observed effects during the MIP detection steps were only related to the printing characteristics.

**Figure 1.** Representation of the experimental procedure of stepwise MIP sensor fabrication.

#### *2.3. .Electrochemical Measurements*

A portable instrument (PalmSens3, Houten, The Netherlands) was used for performing the electrochemical measurements. The screen-printed gold electrodes (Au-SPE) consisted of a three-electrode system (purchased from Dropsens, Asturias, Spain), with a gold working electrode. (0.19 cm2), a silver reference electrode and a gold counter electrode (0.54 cm2). The following three electrochemical techniques, cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS), were applied. They were used during the development and detection phases to evaluate the sensor response by measuring changes in current and resistance. The DPV measurements were carried out by scanning the potential from −0.1 V to 0.2 V with a scan rate of 50 mV/s. The EIS was measured at a bias potential of 10 mV over a frequency range of 0.1 Hz to 50 kHz. The impedance data were appropriately adjusted using the Randles equivalent circuit.

#### *2.4. Analysis of Olive Oil Samples*

The prepared MIP sensor was applied to olive oil samples to detect the presence of FEN. For this purpose, the extract of a Moroccan extra virgin olive oil collected from fields in the province of Taounate, Morocco, supposed to be free of residues of the pesticide FEN, a contaminated oil collected from an olive field in the province of Ouarzazate in Morocco, and a commercial oil called Al Hora, were pretreated. The two olive oil samples were pre-treated as follows: 1 mL of the oil samples was macerated and mixed with 5 mL of methanol/water (4:1, *v*/*v*), for 1 h at room temperature. This allowed the remaining pesticide residues in the samples to be extracted. This solution was centrifuged for 15 min at 6000 rpm, which accelerated the transfer of the pesticide residues into the solution. Next, the solvent methanol was evaporated from the supernatant at 65 ◦C (boiling point of methanol), for 5 min and the extract was collected in an Eppendorf tube. Finally, we deposited a volume of 10 μL of this mixture on the surface of the MIP sensor.

#### **3. Results and Discussion**

#### *3.1. Electropolymerization of FEN Imprinted Film*

After the functionalization step of the Au-SPE electrode, by 2-ATP with Au-NPs, the prepared pre-electropolymerization complex, composed of a mixture of FEN with acetonitrile, was electropolymerized via reactions between FEN and the second layer containing 2-ATP.

In order to study the surface changes of Au-SPE (bare gold, after electropolymerization and extraction), the CV was used for further characterization in a [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> 5 mM solution, over the potential range of −0.4 V to 0.6 V at a scan rate of 20 mV/s. As shown in Figure 2, the peak anode current (Ia) and potential (Ea) were recorded. As can be seen, the CV signal of the bare gold electrode was lower than the MIP current, indicating that FEN had been successfully trapped on the Au-SPE electrodes, preventing [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> from diffusing onto the Au-SPE surface. In addition, the NIP sensor had a lower peak current due to the absence of FEN molecules in the polymer.

**Figure 2.** Cyclic voltammograms, Nyquist plots of 5 mM [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> solution at bare Au-SPE, Au-SPE/MIP and Au-SPE/NIP.

Impedance spectroscopy was also used. As can be seen, the results obtained from the EIS and the CV are consistent with each other.

#### *3.2. Molecular Recognition by MIP and NIP Sensors*

To verify the retention capacity of the sensor for different concentrations of FEN, DPV and EIS techniques were used. Figure 3a shows the DPV and EIS responses of the modified electrode for the detection of FEN in the range of 0.01 μg/mL to 17.3 μg/mL. The [Fe(CN)6] <sup>3</sup>−/4<sup>−</sup> was used as a mediator between the printed electrodes and the standard solutions.

In Figure 3a, it can be seen that the redox current peaks increased with increasing FEN concentrations. This increase may have been due to the conductivity of the film covering the surface of the Au-SPE. This confirms the binding of the FEN molecules that hindered the electron transport of the redox probe [Fe(CN)6] <sup>3</sup>−/4−.

The same results were observed for the EIS technique. The semicircles of the Nyquist diagrams decreased as the FEN concentrations increased. Thus, the increase in FEN concentration produced a decrease in charge transfer resistance. The deposition of FEN concentrations resulted in an increase in the overall conductivity of the electrode surface. This demonstrates that the FEN molecules were well captured by the MIP sites.

When analyzing the NIP data obtained under the same conditions, it was found that the current peaks varies slightly. This was probably due to the absence of FEN during electropolymerization that did not involve the creation of specific cavities (Figure 3b). The results of the NIP Nyquist plots show semi-circular patterns and negligible changes in resistance values (Rtc). This means that the NIP sensor was not specific to FEN molecules, confirming that the responses obtained by the MIP sensor were only due to the presence of FEN-specific cavities.

#### *3.3. Calibration Curve and Detection Limit*

After optimizing the manufacturing process parameters, the MIP sensor was used for synthetic FEN detection.

Figure 4 shows the calibration curve referring to the sensor responses to FEN exposure.

**Figure 3.** Voltammograms, Nyquist plots of different FEN concentrations for (**a**) MIP and (**b**) NIP sensors.

**Figure 4.** Calibration curves obtained by DPV for MIP and NIP sensors towards FEN.

The DPV technique was performed in a potential window of −0.1 to 0.2 V. The working range for synthetic detection was 0.01 to 17.3 μg/mL.

As a result, a satisfactory logarithmic relationship between the MIP sensor responses and FEN concentrations was achieved (Y = 0.3Log C + 1.3; R2 = 0.98).

The limits of detection (LOD) and quantification (LOQ) were calculated using:

$$\text{LOD/LOQ} = \text{k}\_{\text{iLOD/LOQ}} \times \text{s/m}\_{\text{\textdegree}} \tag{1}$$

where ki corresponds to the signal/noise ratio, k = 3.3 for the LOD and k = 10 for the quantification limit (LOQ), s is the standard deviation of the intercept and m is the slope [12]. The LOD value was found to be 0.05 mg/Kg for the DPV measurements.

#### *3.4. Selectivity of the MIP Sensor*

To examine the selectivity of the MIP electrochemical sensor towards FEN, the interference of some similar molecular structures, including dimethoate (DMT) and malathion (MAL), was examined [11,13,14]. The interference test was carried out with satisfactory results. Figure 5 clearly shows that FEN's MIP sensor has much higher current responses compared to both analogues. It is, therefore, better suited for the selective detection of FEN.

**Figure 5.** Calibration curves obtained by DPV for MIP towards FEN and interferences.

#### *3.5. Analysis of Olive Oil Samples*

The developed MIP sensor was tested for the determination of FEN in contaminated olive oil samples. Using the DPV technique, the responses of the MIP sensor were exploited. In fact, the difference between the maximum current values of the blank and the sample can be calculated using Equation (2) to obtain the FEN concentration in the real samples.

$$\mathbf{y} = 0.\overline{\mathbf{z}} \text{Log } \mathbf{C} + \mathbf{1}.\mathbf{3} \tag{2}$$

The results obtained are summarized in Table 1. These results show a satisfactory measurement accuracy of the MIP sensor with an acceptable RSD of 0.14% for olive oils. The measured FEN content of the olive oil samples was 0.25 pg/mL for the Al Horra commercial oil and 0.74 pg/mL for the Ouarzazate field oil.

**Table 1.** Detection of fen in olive oil samples.


**Author Contributions:** Conceptualization, N.E.B.; methodology, Y.A. and N.E.B.; software, B.B. and N.E.B.; validation, N.E.B.; formal analysis, Y.A. and N.E.B.; investigation, N.E.B. and Y.A.; resources, N.E.B.; data curation, N.E.B. and Y.A.; writing—original draft preparation, Y.A., N.E.B. and Z.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to express their thanks for the financial support received from the Moulay Ismail University "Scientific Research Promotion".

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

