Simultaneous determination of three organophosphorus pesticides by differential pulse stripping voltammetry and chemometrics

Roll-to-Roll Manufactured Sensors for Nitroaromatic Organophosphorus Pesticides Detection

A fully roll-to-roll manufactured electrochemical sensor with high sensing and manufacturing reproducibility has been developed for the detection of nitroaromatic organophosphorus pesticides (NOPPs). This sensor is based on a flexible, screen-printed silver electrode modified with a graphene nanoplatelet (GNP) coating and a zirconia (ZrO₂) coating. The combination of the metal oxide and the 2-D material provided advantageous electrocatalytic activity toward NOPPs. Manufacturing, scanning electron microscopy-scanning transmission electron microscopy image analysis, electrochemical surface characterization, and detection studies illustrated high sensitivity, selectivity, and stability (∼89% current signal retention after 30 days) of the platform. The enzymeless sensor enabled rapid response time (10 min) and noncomplex detection of NOPPs through voltammetry methods. Furthermore, the proposed platform was highly group-sensitive toward NOPPs (e.g., methyl parathion (MP) and fenitrothion) with a detection limit as low as 1 μM (0.2 ppm). The sensor exhibited a linear correlation between MP concentration and current response in a range from 1 μM (0.2 ppm) to 20 μM (4.2 ppm) and from 20 to 50 μM with an R² of 0.992 and 0.991, respectively. Broadly, this work showcases the first application of GNPs/ZrO₂ complex on flexible silver screen-printed electrodes fabricated by entirely roll-to-roll manufacturing for the detection of NOPPs.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Electrochemical preparation of two nanostructured poly(sulfosalicylic acid) films with different morphologies and properties for selective sensing of dopamine: Comparative study

Two nanostructured poly(sulfosalicylic acid) (PSA) films were synthesized from room temperature ionic liquid (RTIL) or aqueous solution on a glassy carbon electrode (GCE) via potentiodynamic electropolymerization. The morphology and properties of the PSA films were characterized with scanning electron microscopy (SEM), scanning probe microscopy (SPM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). It was found that solvent had a major influence on the morphology and electrochemical properties of the resultant PSA films. The PSA(Ι) film, which was prepared from RTIL, consists of granular particles with cracks, whereas the PSA(II) film prepared from aqueous solution consists of nano-triangles with a more compact surface. The blocking effect of the PSA(Ι) film for the [Fe(CN)₆]^3-/4- electrochemical probe is much stronger, and a remarkably enhanced voltammetric response of the [Ru(NH₃)₆]³⁺ electrochemical probe can be observed for the PSA(II) film. When it is used to detect dopamine in the presence of a high concentration of ascorbic acid, PSA(II)/GCE has three linear parts with better discrimination and a detection limit of 0.03μM. For PSA(Ι)/GCE, there are two linear parts with a detection limit of 0.05μM. However, the reproducibility and storage stability of PSA(Ι)/GCE are better than those of PSA(ΙI)/GCE.

Boron-doped diamond as a sensor for the classification of carbamate pesticides using a chemometric approach

Electrochemical sensor for the classification of carbamate pesticides.

Simultaneous voltammetric determination of four triazine herbicides in water samples with the aid of chemometrics

A novel differential pulse voltammetry (DPV) method was developed for the simultaneous analysis of herbicides in water. A mixture of four herbicides, atrazine, simazine, propazine and terbuthylazine was analyzed simultaneously and the complex, overlapping DPV voltammograms were resolved by several chemometrics methods such as partial least squares (PLS), principal component regression (PCR) and principal component-artificial networks (PC-ANN). The complex profiles of the voltammograms collected from a synthetic set of samples were best resolved with the use of the PC-ANN method, and the best predictions of the concentrations of the analytes were obtained with the PC-ANN model (%RPET = 6.1 and average %Recovery = 99.0). The new method was also used for analysis of real samples, and the obtained results were compared well with those from the GC-MS technique. Such conclusions suggest that the novel method is a viable alternative to the other commonly used methods such as GC, HPLC and GC-MS.

The effect of mixtures of organophosphate and carbamate pesticides on acetylcholinesterase and application of chemometrics to identify pesticides in mixtures

Organophosphate (OP) and carbamate (CP) pesticides act by the inhibition of acetylcholinesterase (AChE). This enables the use of this enzyme for the detection of these pesticides in the environment. While many studies have looked at the effect of single pesticides on AChE, the effect of mixtures of pesticides still requires extensive investigation. This is important to evaluate the cumulative risk in the case of simultaneous exposure to multiple pesticides. Therefore we examined the effect of five different pesticides (carbaryl, carbofuran, parathion, demeton-S-methyl, and aldicarb) on AChE activity to determine whether combinations had an additive, synergistic, or antagonistic inhibitory effect. Results indicated that the mixtures had an additive inhibitory effect on AChE activity. The data from the assays of the mixtures were used to develop and train an artificial neural network (ANN) which was then utilised successfully for the identification of pesticides and their concentrations in mixtures. This study is significant because it evaluated mixtures of OPs and CPs where previous studies focused on either OPs or CPs. Previous studies have only examined up to three pesticides while this study evaluated mixtures of five pesticides simultaneously. This is also the first study where an ANN was able to utilise data from the inhibition of a single enzyme to differentiate five different pesticides and their concentrations from mixtures.

Glassy carbon and boron doped glassy carbon electrodes for voltammetric determination of linuron herbicide in the selected samples

In this study the application of home-made unmodified (GC) and bulk modified boron doped glassy carbon (GCB) electrodes for the voltammetric determination of the linuron was investigated. The electrodes were synthesized with a moderate temperature treatment (1000°C). Obtained results were compared with the electrochemical determination of the linuron using a commercial glassy carbon electrode (GC-Metrohm). The peak potential (E p ) of linuron oxidation in 0.1 mol dm−3 H2SO4 as electrolyte was similar for all applied electrodes: 1.31, 1.34 and 1.28 V for GCB, GC and GC-Metrohm electrodes, respectively. Potential of linuron oxidation and current density depend on the pH of supporting electrolyte. Applying GCB and GC-Metrohm electrodes the most intensive electrochemical response for linuron was obtained in strongly acidic solution (0.1 mol dm−3 H2SO4). Applying the boron doped glassy carbon electrode the broadest linear range (0.005–0.1 µmol cm−3) for the linuron determination was obtained. The results of voltammetric determination of the linuron in spiked water samples showed good correlation between added and found amounts of linuron and also are in good agreement with the results obtained by HPLC-UV method. This appears to be the first application of a boron doped glassy carbon electrode for voltammetric determination of the environmental important compounds.

Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes.

Review on the use of enzymes for the detection of organochlorine, organophosphate and carbamate pesticides in the environment

Pesticides are released intentionally into the environment and, through various processes, contaminate the environment. Three of the main classes of pesticides that pose a serious problem are organochlorines, organophosphates and carbamates. While pesticides are associated with many health effects, there is a lack of monitoring data on these contaminants. Traditional chromatographic methods are effective for the analysis of pesticides in the environment, but have limitations and prevent adequate monitoring. Enzymatic methods have been promoted for many years as an alternative method of detection of these pesticides. The main enzymes that have been utilised in this regard have been acetylcholinesterase, butyrylcholinesterase, alkaline phosphatase, organophosphorus hydrolase and tyrosinase. The enzymatic methods are based on the activation or inhibition of the enzyme by a pesticide which is proportional to the concentration of the pesticide. Research on enzymatic methods of detection, as well as some of the problems and challenges associated with these methods, is extensively discussed in this review. These methods can serve as a tool for screening large samples which can be followed up with the more traditional chromatographic methods of analysis.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of approximately +/-10% is acceptable.

Administering Pesticide Assays in In Vivo-Implanted Biosensors

An analytical pesticide assay of O-ethyl-O-4-(nitrophenyl)phenyl phosphonothioate (EPN) was carried out using the following: a carbon nanotube paste electrode, a mercury-immobilized carbon nanotube paste electrode, a glassy carbon electrode, a metal–gold electrode, and a DNA-immobilized carbon nanotube paste electrode (DPE), which is two-fold more sensitive than other sensors. The DPE was optimized using cyclic and square wave stripping voltammetry. Linear working ranges approached 5–55 mg L–1 EPN and the nano-range of 10–210 ng L–1 in a 0.1 mol L–1 NH4H2PO4 electrolyte solution, with a speedy analytical time of 30-s stripping. The detection limit was 2.57 ng L–1 (7.94 × 10–12 mol L–1), and the precision was 0.102% relative standard deviation (n = 15) at the 10.0 mg L–1 EPN spike. This indicates that the method is more sensitive than common voltammetric methods. This method was applied to fruit samples using patch- and needle-type electrodes, specifically on the skin tissues of an orange and an apple. Moreover, the implanted electrode was interfaced with a fish brain cell at the electrochemical workstation. Results showed that the aforementioned method can be used to conduct a pesticide assay in neuro-treated and non-treated cell systems.

Simultaneous enzymatic kinetic determination of pesticides, carbaryl and phoxim, with the aid of chemometrics

A sensitive and selective enzymatic kinetic method for the simultaneous determination of mixtures of carbaryl and phoxim pesticides was researched and developed. It was based on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5'-dithiobis(2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The DTNB-thiocholine reaction was investigated by a spectrophotometric-kinetic approach. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, which enabled the absorbance (A(P), at lambda(max)=412 nm) from the mixtures of the two pesticide inhibitors to be directly related to their concentrations provided the absorbance additivity was followed. The spectra were measured for mixtures of carbaryl and phoxim at different concentrations, and at t=904 s, T=35 degrees C, pH=7.5, c(ATChI)=0.14, and c(AChE)=0.10 mg mL(-1). The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the carbaryl and phoxim were 4.7 and 0.59 microg L(-1), respectively. Calibration models for chemometrics methods, such as principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural network (RBF-ANN) were constructed and verified with synthetic samples of the mixtures of the two pesticides. The best performing model was based on the RBF-ANN method yielding at approximately 10 ppb analyte concentrations, %RPE(T) (carbaryl=5.2; phoxim=6.5), %Recovery (approx.105%) and %RPE(T) (6.5). Various spiked town-water samples produced recoveries in the range of 98.8-103% for each pesticide.