Simultaneous voltammetric determination of four carbamate pesticides with the use of chemometrics

A review of the advanced developments of electrochemical sensors for the detection of toxic and bioactive molecules

The recent developments made regarding the novel, cost-effective, and environmentally friendly nanocatalysts for the electrochemical sensing of biomolecules, pesticides, nitro compounds and heavy metal ions are discussed in this review article.

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.

Near-infrared spectroscopy as a potential tool with radial basis function for measurement of residual acrylamide in organic polymer

Poly(acrylamide-co-diallyldimethylammonium chloride) (PDA), which is usually prepared by free radical polymerization of acrylamide monomer (AM) onto the cationic monomer dimethyl diallyl ammonium chloride (DMDAAC), has been widely applied to wastewater treatment; however, the free-radical polymerization is always incomplete with residual AM remaining in the PDA. The residual AM affects the PDA's performance while also posing as a potential threat to human health; therefore, during preparation of the PDA, the rapid detection of the residual AM plays an important role in controlling the residual AM while improving the PDA's performance. The objective of this study was to explore the possibilities for applying near-infrared (NIR) spectroscopy as a potential tool for detecting the residual AM in combination with a statistical tool. In this study, the radial basis function (RBF) network model as the statistical tool was combined with NIR spectroscopy for detection of the residual AM. The experimental results showed that five wavelengths in the NIR spectroscopy were the most important characteristic adsorption peaks, particular at 971.95 and 1077 nm. The simulation of the RBF model presented higher performance with R2-value greater than 0.98, RMSEC and RMSEP less than 7.22 x 10(-5) and coefficient of variation (CV) of the predicted residual AM less than 10%, which demonstrated the feasibility of the NIR spectroscopy being a rapid detection tool for prediction of the residual AM using the RBF model. Wavelet de-nosing was used for removing the interference/noise in the NIR spectroscopy and improved the generalization ability of the RBF model.

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.

Simultaneous determination of pesticides at trace levels in water using multiwalled carbon nanotubes as solid-phase extractant and multivariate calibration

The application of solid-phase extraction with multivariate calibration for simultaneous determination of three toxic pesticides in tap and reservoir waters was presented. The proposed analytical method was used for the determination of atrazine, methidathion, and propoxur in complex water samples without the need for chromatographic separation. Among the applied multivariate calibration methods, partial least squares (PLS-1) method was found the most effective for pesticides quantification. Multiwalled carbon nanotubes (MWCNTs) adsorbent showed a perfect extraction/preconcentration of pesticides present at trace levels. The experimental factors that affect pesticides extraction by MWCNTs adsorbent such as sample volume, eluent volume, solution pH, and extraction flow rate were studied and optimized. The figures of merit of the proposed method were: limits of detection 3, 2, and 3 microg l(-1) and linear ranges 5-30, 3-60, and 5-40 microg l(-1) for atrazine, methidathion, and propoxur, respectively. A good precision was reported for the method, R.S.D. values were always less than 5.0%. Satisfactory results were reported for simultaneous determination of trace levels of pesticides in complex matrices. In tap water, the percent recoveries for pesticides were extended from 95 to 104% and R.S.D. from 1 to 3%, while lower recoveries were observed in reservoir water: 84-93% (R.S.D.: 1-3). Although the pesticides can be accurately quantified by SPE and liquid chromatography, SPE-PLS-1 method was found simpler and operated at lower running costs.

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.

Simultaneous amperometric determination of lignin peroxidase and manganese peroxidase activities in compost bioremediation using artificial neural networks

The activities of lignin-degrading peroxidases are the primary decomposition indexes in compost bioremediation. In this paper, artificial neural networks (ANNs) have been combined with an enzyme sensor for simultaneous determination of lignin peroxidase (LiP) and manganese peroxidase (MnP) activities secreted by Phanerochaete chrysosporium in composting of municipal solid waste. The LiP and MnP activities were detected through catalytic redox of H2O2, hydroquinone and veratryl alcohol as substrates by an amperometric sensor immersed in the culture filtrate solution. Due to the dynamic, nonlinear and uncertain characteristics of the complex composting system, ANNs have been used as a chemometric tool for overlapping signal deconvolution and modelling to quantify the two enzyme activities separately. Feedforward backpropagation network was used for the training process. The effects of the transfer functions, the amount of current values, the number of hidden neurons and the optimization algorithm were investigated. The LiP activities in the filtrate varied from 8.14 to 29.79 U L(-1), and from 0.36 to 1.37 U L(-1) for MnP activities. A good prediction capability was obtained, with correlation coefficients of 0.9936 for LiP activity and 0.9976 for MnP activity between the expected and predicted values of the external test samples. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results show that the combination of amperometric enzyme sensor and artificial neural networks is a rapid, sensitive and robust method in the quantitative study of composting system.