Identification Of Pesticides Research Articles

Analysis, Quantification and Identification of Pesticides from Lettuce in Ouagadougou City (Burkina Faso)

In Burkina Faso, chemical inputs are used massively in market gardening to increase the yields. The misuse of these chemicals is responsible of contamination of market garden products, leading to the rapid rotting of vegetables. The objective of this study was to determine the pesticides in chemical inputs applied on vegetables produced in Ouagadougou City. To do this, lettuce has been sampled from producers in three sites (Tanghin, Boulmiougou and Kossodo) and pesticides amounts have been analyzed using Gas Chromatography coupled with mass spectrometry. Chemical analyses have carried out on three lettuce samples and the extraction of chemical input residues was done according to QuEChERS (Quick Easy Cheap Effective Rugged and Safe) method. Results revealed the presence of six (06) pesticide molecules (chlordimeform, quintozene, lindane, cypermethrin, α-cypermethrin, δ-deltamethrin) in Boulmiougou sample, four (04) molecules (methomyl, cypermethrin, α-cypermethrin, δ-deltamethrin) in Kossodo sample and three (03) molecules (cypermethrin, α-cypermethrin, δ-deltamethrin) in Tanghin sample. In this regard, analysis of pesticide amount revealed that the quantity of molecules was acceptable for the consumer, but these pesticides could contribute to the rapid rotting of our vegetables which generates economic and social losses for the country.

Simultaneous detection of carbendazim and pendimethalin residues using a compact Raman spectrometer

Abstract Surface-enhanced Raman spectroscopy (SERS) exhibits significant latent capacity in the prompt and accurate detection of minute concentrations of pesticides. In this study, carbendazim and pendimethalin were simultaneously detected within tobacco foliage by utilizing a PS@Ag composite substrate coupled with a compact Raman spectrometer. The detection limits for carbendazim is 0.1 mg/kg, and for pendimethalin, it is 5 mg/kg. The recovery rates for carbendazim within tobacco foliage ranged from 85.2% to 112.12%. And the recovery rates for pendimethalin ranged from 90.38% to 113.42%. The combination of the PS@Ag composite substrate in conjunction with a Raman spectrometer provides a highly effective means for the concurrent identification of pesticides in tobacco foliage.

Selective and sensitive molecularly imprinted polymer-based electrochemical sensor for detection of deltamethrin

Electrochemical sensors have a broad range of industrial applications due to their sensitivity, speed, and cost-effectiveness. These sensors enable the continuous monitoring and control of critical parameters in various industrial processes. For instance, they are essential in food safety, environmental monitoring, biomedical applications, and pharmaceutical production. In the food industry, electrochemical sensors facilitate the rapid and reliable detection of contaminants and pathogens in food products, thus enhancing product quality and consumer safety. An electrochemical sensor was developed with the molecularly imprinted polymer (MIP) technique to detect deltamethrin with high sensitivity and selectivity. The sensor was fabricated by electrodeposition of Co3O4 on indium tin oxide (ITO), followed by electropolymerization of o-phenylenediamine with deltamethrin as a template molecule. The template molecules were then removed from the modified electrode by a methanol. The MIP-based electrochemical sensor exhibited high sensitivity and selectivity towards deltamethrin. Under the optimized conditions, the LOD values for the MIP/Co3O4/ITO electrode in the first and second linear regressions were 1.53 nM for linear range of 2.82 nM to 56.5 nM and 0.34 μM for linear range of 0.25 μM to 3.98 μM. Moreover, the LOD values for the NIP/Co3O4/ITO electrode in the first and second regressions were 2.43 nM for the linear range of 3.91 nM to 65.0 nM and 726.0 nM for the linear range of 0.023 μM to 4.5 μM. The developed electrochromic pesticide sensor, being an electrochemical-based molecularly imprinted polymer (MIP) sensor incorporating electrochromic materials, enables both target-specific pesticide detection and visual pesticide identification based on color changes dependent on pesticide concentration. Consequently, this system is more advantageous compared to electrochemical-based MIP sensors, as it provides both qualitative and quantitative determinations. The qualitative assessment aims to enhance the ease of use of the sensor, thereby increasing the potential for it to become a commercially viable product by reducing the need for instrumental devices.

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple pesticides. Herein, we first synthesize a Eu(III)-functionalized HOF-on-HOF composite (Eu@PFC-1@MA-TPA) and then utilize principal component analysis (PCA) and a machine learning (ML) algorithm to achieve simultaneous identification of the pesticides 2,6-dichloro-4-nitroaniline (DCN) and thiabendazole (TBZ) and their mixtures. Eu@PFC-1@MA-TPA displays high quantitative identification ability, which can distinguish single DCN and TBZ as low as 1 μM and their mixtures at 5 μM through PCA. In addition, the hydrogel film Eu@PFC-1@MA-TPA/AG is fabricated to monitor DCN and TBZ in drinking water, tap water, river water, and apple juice with high sensitivity. Furthermore, based on the obvious fluorescence color variance of pesticides, Eu@PFC-1@MA-TPA/AG achieves visual and in situ imaging detection of single DCN and TBZ and their mixtures. More importantly, we construct an intelligent artificial vision platform integrating Eu@PFC-1@MA-TPA/AG with a DenseNet algorithm, which can identify the concentrations and types of DCN and TBZ and their mixtures within 1 s with over 98% accuracy. This work develops a precise and rapid analysis method for simultaneous identification of multiple pesticides through combining a visualized fluorescence sensor and an ML algorithm.

Internal light-driven efficient chemiluminescence of graphitic carbon nitride quantum dots for improved carboxin sensing

Developing a new chemiluminescence (CL) sensor platform to achieve the identification of pollutants in the water environment is a significant and currently hot topic for researchers. Building on the effective development of a CL internal light source, this paper designed a direct, simple, and rapid pesticide identification strategy. Specifically, graphite carbon nitride quantum dots (CNQDs) with excellent optical properties were synthesized using a solvent heat treatment strategy. Acting as an energy acceptor, CNQDs triggered CL emission in the presence of singlet oxygen (1O2). The newly developed CNQDs/1O2 CL sensor exhibited a specific response toward carboxin (CBX) with high sensitivity. Based on this discovery, we have developed a highly selective, ultra-sensitive, and fast CL sensing platform for detecting CBX in water environments. Through numerous experiments, we attributed the CL emission of the CNQDs/1O2 system to the specific chemiluminescent resonance energy transfer (CRET) process between CNQDs and 1O2. Interestingly, the CL light of the CNQDs/1O2 system could serve as an internal light source to excite CBX generating a large amount of 1O2 through photosensitization, which was crucial for further enhancing the CL efficiency of the CNQDs/1O2/CBX system and also enabled the CL sensing of CBX. This study is the first to successfully identify CBX in water using CL sensors, showcasing the potential of CL internal light sources in regulating CL efficiency and offering new perspectives on detecting pollutants in complex environments.

Ftalates and pesticides effects in maternal-infant health: a bibliographic review

"Due to its nutritional contribution and composition, breast milk is the ideal food for the newborn and up to 6 months of age, however mothers’ exposure to phthalates and pesticides could threaten its benefits; the purpose of this literature review will be to analyze the safety of breast milk after mother exposure to phthalates or pesticides. For this literature review, Clinical Key, PubMed, and Google Scholar databases were consulted, articles in Spanish and English from 2011 to 2021 in humans were included, duplicate studies, studies conducted in animals, theses, case studies, books, guides, letters to the editor and non-relevant articles were excluded; 9657 articles were found, 9593 were eliminated and 64 were included. There is evidence of the transmission of phthalates and pesticides to breast milk; due to their lipophilic capacity they are stored in the mammary glands and are excreted during lactation, their ability to interfere with normal hormonal processes and consequently produce adverse health effects confers them the term endocrine disruptors. Both the World Health Organization (WHO) and the Food and Agriculture Organization of the United Nations (FAO) have established safety levels for consumption; since their prohibition and awareness of health effects, their use has decreased or has been replaced by other compounds. The articles consulted indicate that despite the identification of pesticides and/or phthalates in breast milk, benefits outweigh health effects. Although more studies are needed to ensure that it remains safe."

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.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Accelerated and precise identification of antioxidants and pesticides using a smartphone-based colorimetric sensor array

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1–10 μM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.

Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis.

In this research, a method was developed for fabricating Au-Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au-Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au-Au nanorod array substrate was enhanced to 10-11 M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10-4 to 5 × 10-7 M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification.

Pesticide Identification Using Surface-Enhanced Raman Spectroscopy and Density Functional Theory Calculations: From Structural Insights to On-Site Detection.

Pesticides play an important role in conventional agriculture. Yet, their harmful effects on the environment are becoming increasingly apparent. The occurrence of pesticides is hence being monitored worldwide. For fast, easy, yet sensitive identification, surface-enhanced Raman spectroscopy (SERS) is a powerful tool. In this study, a method is introduced that may be amended to in-field detection of pesticides. Gold and silver nanoparticles were synthesized, size-tailored, and characterized. The herbicide paraquat and the fungicide thiram served as model compounds. The preparation yielded reproducible SERS spectra. Using quantum chemical computation, Raman and SERS spectra were calculated and analyzed. The interpretation of vibrational modes in combination with SERS enhancement and attenuation allowed us to identify compound-specific bands. The assignment was interpreted in terms of the orientation of paraquat and thiram on the gold and silver nanoparticle surfaces. Paraquat preferred a co-planar arrangement parallel to the gold nanoparticle surface and a head-on orientation on the silver nanoparticle. For thiram, breaking of the disulfide bond was recognized, such that interaction with the surface occurred via the sulfur atoms. Successful detection of the pesticides after recollection from vegetable leaves demonstrated the method's applicability for pesticide identification.

Detection and Identification of Nitrile Compounds via Recognition-Enabled Chromatographic 19F NMR.

The surge in applications of nitrile compounds across diverse fields, such as pharmaceuticals, agrochemicals, dyes, and functional materials, necessitates the development of rapid and efficient detection and identification methods. In this study, we introduce a chemosensing strategy employing a novel 19F-labeled probe, facilitating swift and accurate analysis of a broad spectrum of nitrile-containing analytes. This approach leverages the reversible interaction between the 19F-labeled probe and the analytes to produce chromatogram-like outputs, ensuring the precise identification of various pharmaceuticals and pesticides within complex matrices. Additionally, this dynamic system offers a versatile platform to investigate through-space 19F-19F interactions, showcasing its potential for future applications in mechanistic studies.

Ratiometric Dual-Mode Optical Sensor Array for the Identification and Differentiation of Pesticides in Vegetables with Mixed Plasmonic and Fluorescent Nanostructures

Ratiometric Dual-Mode Optical Sensor Array for the Identification and Differentiation of Pesticides in Vegetables with Mixed Plasmonic and Fluorescent Nanostructures

Multi-signal sensor array based on a fluorescent nanozyme for broad-spectrum screening of pesticides

Accurate identification of broad-spectrum pesticides remains a challenge for existing rapid detection technologies based on natural cholinesterase. Sensor arrays show excellent promise, but typically rely on increasing receptors to improve sensing dimensionality, which is both cumbersome and imprecise. In this work, inspired by the multiple properties of a fluorescent nanozyme, we constructed a facile and simple sensor array based on the laccase-like activity, peroxidase-like activity, and fluorescent feature of one nanozyme (Cu-BDC-NH2). In the presence of pesticides, they produced differential positive responses and could be captured by linear discriminant analysis and hierarchical clustering analysis. Thus, different pesticides belonged to organophosphates, carbamates, organochlorines, pyrethroids, sulfonylureas, and diphenyloxazolines, respectively, have been successfully discriminated at 1 – 100 μg/mL. The differentiation of cross-contamination at different concentrations and compositions further validated the sensing performance. More importantly, they demonstrated strong immunity to interference as well as high feasibility for recognizing pesticides in fruits and vegetables, and performed precise identification for blind samples. Therefore, the sensor arrays provided a novel and reliable solution for broad-spectrum pesticide screening, which not only bridged the gap of existing rapid detection methods, but also opened up a new way for food safety regulation.

Development of a green analytical methodology based on a deep eutectic solvent for the determination of pesticide residues in industrial hemp

In this study, in accordance with the green analytical chemistry principles, the use of eco-friendly solvents has been proposed for the first time for the analysis of pesticide residues in industrial hemp (Cannabis sativa L.). With this aim, betaine-based natural deep eutectic solvents with lactic acid and propylene glycol were explored as extraction solvents in the solid–liquid extraction of fifteen pesticides from hemp samples. The mixture composed of betaine:propylene glycol at molar ratio 1:4 (n/n) was selected due to its better analytical performance. The separation, identification and quantification of pesticides were carried out by gas chromatography coupled to tandem mass spectrometry. The extraction protocol was optimised through the step-by-step strategy, evaluating NADES composition, water addition to the solvent, solid–liquid ratio, and re-extraction solvent. The analytical methodology was validated in terms of calibration (linear regression with R2 ≥0.9966), trueness and precision (average relative recovery of 63–77% with relative standard deviation ≤20%) and sensitivity (limits of quantification of 0.663–3.625 µg/kg). These results successfully demonstrated the convenience of the developed approach for the determination of pesticides in industrial hemp, which has industrial applications such as the production of food and supplements, cosmetics, and textiles, which should not compromise the health of consumers. In addition, the comparison with previously reported research in this field showed that not only the present method is more sensitive, but also more sustainable and economical.

Detection of carbofuran in fruits and vegetables by Raman spectroscopy combined with immunochromatography.

It has long been desired to develop rapid methods for the rapid identification and quantification of pesticides and their metabolites. Carbofuran, a representative pesticide of the carbamate group, is highly systemic and is used on vegetables, fruits and grains, which has led many countries to test for residues in food and the environment. In this study, gold and silver composite core-shell (Au@Ag) nanoparticles were used to label the carbofuran antibody and Raman molecule 5,5-dithiobis-2-nitrobenzoic acid (DTNB) to synthesize Raman immune probes. The signal value of DTNB was read using a Raman spectrometer, and the quantitative detection technology of carbofuran was established based on lateral flow immunochromatographic assay (ICA) combined with surface-enhanced Raman spectroscopy (SERS). SERS-ICA is a rapid, quantitative and ultrasensitive test for the determination of carbofuran in fruits and vegetables with a sensitivity of 0.1 pg mL-1. Consequently, the results demonstrate that the SERS-based lateral flow immunosensor developed in this study has the advantages of excellent assay sensitivity and remarkable multiplexing capability, and thus it will have great application potential in food safety monitoring.

Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system

Raman spectroscopy can be used for accurately detecting pesticides and determining the chemical composition of a pesticide. To facilitate field detection, the present study used a portable Raman spectrometer for analysis. However, this spectrometer was found to be susceptible to noise interference and signal offsets, which increased the difficulty of pesticide identification. The most commonly used algorithm for Raman spectrum identification is principal component analysis (PCA). However, accurate classification often cannot be achieved with PCA because of the offset and noise in the Raman spectrum data. Therefore, in this study, after the collected Raman spectrum data were processed using the small-step, center-weighted moving-average method, these data were employed to train a convolutional neural network (CNN) model for prediction. To optimize the CNN model, the hyperparameters of the CNN were adjusted using various optimization algorithms, and the optimal solution was obtained after multiple iterations. Data preprocessing and architecture training models were then constructed in a self-optimized manner to improve the ability of the algorithm model to handle diverse types of data. Finally, a CNN model optimized using the cat swarm optimization algorithm was developed. This model was trained on 3000 samples containing three pesticides, and its accuracy for pesticide composition identification was discovered to be 89.33%.

Mixed pesticide recognition based on three-dimensional fluorescence spectroscopy and a convolutional neural network.

Kasugamycin, spinosad, and lambda-cyhalothrin are common organic pesticides that are widely used to control and prevent diseases and pests in fruits and vegetables. However, the unreasonable use of pesticides will cause great harm to the natural environment and human health. Pesticides often exist in the form of mixtures in nature. Establishing recognition models for mixed pesticides in large-scale sample testing can provide guidance for further precise analysis and reduce resource waste and time. Therefore, finding a fast and effective identification method for mixed pesticides is of great significance. This paper applies three-dimensional fluorescence spectroscopy to detect mixed pesticides and introduces a convolutional neural network (CNN) model structure based on an improved LeNet-5 to classify mixed pesticides. The input part of the model corresponds to fluorescence spectrum data at excitation wavelengths of 250-306nm and emission wavelengths of 300-450nm, and the mixed pesticides are divided into three categories. The research results show that when the learning rate is set to 1 and the number of iterations is 300, the CNN classification model has ideal performance (with a recognition accuracy of 100%) and is superior to the performance of the support vector machine method. This paper provides a certain methodological basis for the rapid identification of mixed pesticides.

Non-target analysis of livestock products and feed for residues of drugs, pesticides, mycotoxins and their metabolites by high-resolution mass spectrometry (a review)

A review of techniques of non-target analysis for simultaneous identification and determination of veterinary drugs, pesticides, mycotoxins, their metabolites, and substances of chemical transformation in livestock products using liquid chromatography and high-resolution mass spectrometry is presented. Some limitations of the approach are noted, such as the necessity of using common extraction conditions, the possibility of analyte transformations during the sample preparation, false positive results for isobaric and isomeric compounds, and the lack of spectral data for previously unexplored substances. However, the method is the most promising tool for the determination of pollutants not identified in the targeted analysis, as in the case of multicomponent screening of food and raw materials, and in the study of the parent compounds transformation.

Smartphone-assisted sensor array constructed by copper-based laccase-like nanozymes for specific identification and discrimination of organophosphorus pesticides.

Accurate pesticide identification is of great importance for regulating food safety. However, the discrimination between organophosphorus pesticides (OPs) and carbamate pesticides (CPs) is still a challenge for existing analytical methods based on cholinesterase inhibition. It mainly because of the similar inhibitory effect of OPs and CPs on cholinesterase. Herein, we found that OPs and CPs differentially affected nanozymes with laccase-like activity, which would be interfered by OPs in different degrees rather than CPs. Thus, we fabricated a nanozyme sensor array and successfully achieved the OPs identification and similar individual discrimination, ignoring the interference from CPs or other potential interferents (antibiotics, ions, other pesticides). On the basis of nanozyme sensor array, a portable method using smartphone was constructed and utilized to determine OPs in fruits and vegetables. This work would contribute to the development of portable sensors and the highly selective identification and discrimination of OPs in complex samples.