An ultrasensitive LC-MS/MS method for the determination of glyphosate, AMPA and glufosinate in water - analysis of surface and groundwater from a hydrographic basin in the Midwestern region of Brazil

Basic research for identification and classification of organophosphorus pesticides in water based on ultraviolet-visible spectroscopy information

In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely chlorpyrifos, acephate, parathion-methyl, trichlorphon, dichlorvos, profenofos, malathion, dimethoate, fenthion, and phoxim, respectively. Firstly, the spectral data of all the samples was obtained using a UV-visible spectrometer. Secondly, five preprocessing methods, six manifold learning methods, and five machine learning algorithms were utilized to build detection models for identifying OPPs in water bodies. The findings indicate that the accuracy of machine learning models trained on data preprocessed using convolutional smoothing + first-order derivatives (SG + FD) outperforms that of models trained on data preprocessed using other methods. The backpropagation neural network (BPNN) model exhibited the highest accuracy rate at 99.95%, followed by the support vector machine (SVM) and convolutional neural network (CNN) models, both at 99.92%. The extreme learning machine (ELM) and K-nearest neighbors (KNN) models demonstrated accuracy rates of 99.84% and 99.81%, respectively. Following the application of a manifold learning algorithm to the full-wavelength data set for the purpose of dimensionality reduction, the data was then visualized in the first three dimensions. The results demonstrate that the t-distributed domain embedding (t-SNE) algorithm is superior, exhibiting dense clustering of similar clusters and clear classification of dissimilar ones. SG + FD-t-SNE-SVM ranks highest among the feature extraction models in terms of performance. The feature extraction dimension was set to 4, and the average classification accuracy was 99.98%, which slightly improved the prediction performance over the full-wavelength model. As shown in this study, the ultraviolet-visible (UV-visible) spectroscopy system combined with the t-SNE and SVM algorithms can effectively identify and classify OPPs in waterbodies.

Chromatographic Methods for the Determination of Glyphosate in Cereals Together with a Discussion of Its Occurrence, Accumulation, Fate, Degradation, and Regulatory Status

The European Union's recent decision to renew the authorization for the use of glyphosate until 15 December 2033 has stimulated scientific discussion all around the world regarding its toxicity or otherwise for humans. Glyphosate is a chemical of which millions of tons have been used in the last 50 years worldwide to dry out weeds in cultivated fields and greenhouses and on roadsides. Concern has been raised in many areas about its possible presence in the food chain and its consequent adverse effects on health. Both aspects that argue in favor of toxicity and those that instead may indicate limited toxicity of glyphosate are discussed here. The widespread debate that has been generated requires further investigations and field measurements to understand glyphosate's fate once dispersed in the environment and its concentration in the food chain. Hence, there is a need for validated analytical methods that are available to analysts in the field. In the present review, methods for the analytical determination of glyphosate and its main metabolite, AMPA, are discussed, with a specific focus on chromatographic techniques applied to cereal products. The experimental procedures are explained in detail, including the cleanup, derivatization, and instrumental conditions, to give the laboratories involved enough information to proceed with the implementation of this line of analysis. The prevalent chromatographic methods used are LC-MS/MS, GC-MS/SIM, and GC-MS/MS, but sufficient indications are also given to those laboratories that wish to use the better performing high-resolution MS or the simpler HPLC-FLD, HPLC-UV, GC-NPD, and GC-FPD techniques for screening purposes. The concentrations of glyphosate from the literature measured in wheat, corn, barley, rye, oats, soybean, and cereal-based foods are reported, together with its regulatory status in various parts of the world and its accumulation mechanism. As for its accumulation in cereals, the available data show that glyphosate tends to accumulate more in wholemeal flours than in refined ones, that its concentration in the product strictly depends on the treatment period (the closer it is to the time of harvesting, the higher the concentration), and that in cold climates, the herbicide tends to persist in the soil for a long time.

Quantitation of glyphosate, glufosinate, and AMPA in drinking water and surface waters using direct injection and charged-surface ultra-high performance liquid chromatography-tandem mass spectrometry

Herbicides glyphosate (N-(phosphonomethyl)glycine) and glufosinate (2-amino-4-(hydroxymethylphosphinyl)butanoic acid) and the main transformation product of glyphosate, aminomethanephosphonic acid (AMPA), are challenging to analyze for in environmental samples. The quantitative method developed by this study adapts previously standardized dechlorination procedures coupled to a novel charged surface C18 column, ultra-high performance liquid chromatography-tandem mass spectrometry, polarity switching, and direct injection. The method was applied to chlorinated tap water, as well as river samples, collected in the City of Winnipeg and rural Manitoba, Canada. Using only syringe filtration without derivatization, the validated method resulted in good accuracies in both tap and surface water, at both 2 and 20 μg L-1. Method limits of detection (MLD) and quantification (MLQ) ranged from 0.022/0.074 to 0.11/0.36 μg L-1, with precisions of 0.46-2.2% (intraday) and 1.3-7.3% (interday). The mean (SEM) of the pesticides in μg L-1 for tap water were 0.11 (0.007) (AMPA), glufosinate and glyphosate < MLDs; and for Red River water were 0.56 (0.045) (AMPA), glufosinate < MLQ, and glyphosate 0.40 (0.072). For the smaller tributaries, glufosinate was >MLD but < MLQ once and that was for Shannon Creek at 0.2 μg L-1. For the remaining rivers, the mean concentrations ranged from 0.31 to 3.1 μg L-1 for AMPA, and 0.087-0.53 μg L-1 for glyphosate. The method will be ideal for supporting monitoring and risk assessment programs that require high throughput sampling and quantitative methods capable of producing robust results that leverages chromatographic and mass spectrometric paradigms instead of being extraction technology focused.

Evaluation of health risk of glyphosate pesticide intake via surface and subsurface water consumption: A deterministic and probabilistic approach

As the usage of pesticides for both agricultural and non-agricultural uses increases, it is more important than ever to employ probabilistic methods rather than deterministic ones to calculate the danger to human health. The current work demonstrates the application of deterministic and probabilistic approaches to assess the human health risk related to glyphosate during the consumption of surface and groundwater by different population groups. To that aim, the concentration of glyphosate pesticide in the surface and groundwater was measured and human health risk for three population groups including children, teens, and adults was evaluated. Overall, the probabilistic approach via Monte Carlo simulation showed a valid result for the estimation of human health risk and determination of dominant input parameters.•The health risk of glyphosate exposure during water consumption for various population groups were evaluated using deterministic and probabilistic methods.•The modeling is performed by Crystal Ball (11.1.2.4) software, as open access software, and requires a limited number of inputs.•The probabilistic method could reliably assess the risks of glyphosate by considering the variability and uncertainty in input variables.

Maternal impacts of pre-conceptional glyphosate exposure

Maternal glyphosate (GLY) impacts remain unclear despite associations between urinary GLY and birth outcomes. Whether maternal pre-conceptional GLY exposure would have phenotypic and molecular impacts in the dam and offspring was tested. Female C57BL6 mice (6 wk) were exposed to saline (CT; n = 20) or GLY (2 mg/kg; n = 20) per os five d per week for 20 wk. Females were housed with males and on gestation day (GD) 14, divided into: CT non-pregnant (CNP), CT pregnant (CP), GLY non-pregnant (GNP), GLY pregnant (GP). Another cohort (CT; n = 10 or GLY; n = 10) completed three pregnancy rounds and pregnancy index (PI), number of pups per litter and pups surviving to postnatal day (PND) 5 calculated. The PI in GLY mice was higher in breeding rounds 1 and 2, but lower in round 3. Pregnancy increased (P ≤ 0.1) GD14 liver and ovary weight. Spleen weight was increased (P < 0.05) in GP relative to GNP mice. No offspring phenotypic impacts were observed. Approximately six months after cessation of exposure, secondary follicle number was reduced (P < 0.05) by pre-conceptional GLY exposure. The ovarian proteome analyzed by LC-MS/MS was altered (P < 0.05) by pregnancy (49 increased, 43 decreased) and GLY exposure (non-pregnant: 75 increased, 22 decreased, pregnant: 27 increased, 29 decreased; aged dams: 60 increased, 98 decreased) with several histone proteins being altered. These findings support ovarian transient and persistent impacts of GLY exposure and identify pathways as potential modes of action.

CuCeTA nanoflowers as an efficient peroxidase candidate for direct colorimetric detection of glyphosate

Conventional nanozyme-based pesticide detection often requires the assistance of acetylcholinesterase. In this work, a CuCeTA nanozyme was successfully designed for the direct colorimetric detection of glyphosate. Direct detection can effectively avoid the problems caused by cascading with natural enzymes such as acetylcholinesterase. By assembling tannic acid, copper sulfate pentahydrate and cerium(III) nitrate hexahydrate, CuCeTA nanoflowers were prepared. The obtained CuCeTA possessed excellent peroxidase-like activity that could catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to blue oxidized TMB in the presence of hydrogen peroxide. Glyphosate could effectively inhibit the peroxidase-like activity of CuCeTA while other pesticides (fenthion, chlorpyrifos, profenofos, phosmet, bromoxynil and dichlorophen) did not show significant inhibitory effects on the catalytic activity of CuCeTA. In this way, CuCeTA could be used for the colorimetric detection of glyphosate with a low detection limit of 0.025 ppm. Combined with a smartphone and imageJ software, a glyphosate test paper was designed with a detection limit of 3.09 ppm. Fourier transform infrared spectroscopy demonstrated that glyphosate and CuCeTA might be bound by coordination, which could affect the catalytic activity of CuCeTA. Our CuCeTA-based nanozyme system exhibited unique selectivity and sensitivity for glyphosate detection and this work may provide a new strategy for rapid and convenient detection of pesticides.