Detection Of Pesticide Residues Research Articles

Surface-enhanced Raman scattering of a gold core-silver shell-sponge substrate for detection of thiram and diquat.

Aiming at the difficulty of traditional pesticide sampling, a low-cost and convenient flexible surface enhanced Raman scattering (SERS) gold core-silver shell-sponge (Au-Ag-sponge) substrate was synthesized by chemical reduction. The SERS substrate consisted of Au-AgNPs and a melamine sponge. The sponge had a rich open pore structure, which could well "capture" Au-AgNPs, generating a large number of "hot spots". The SERS enhancement activity of the flexible substrate was characterized with rhodamine 6G (R6G) Raman probe molecules. The substrate showed good activity to 10-12 M rhodamine 6G with an enhancement factor (EF) of 7.72 × 106. Applying this substrate to the qualitative and quantitative detection of pesticide residues, the results showed that the Raman intensity was well related to the concentration of pesticide solution with the range of 0.1-10 mg L-1 of thiram and 1-10 mg L-1 of diquat. Furthermore, the substrate was analyzed by finite difference time domain (FDTD) simulation and the results were in good agreement with the experimental results. The reason for the difference in Raman signals of pesticide molecules on the same substrate was the different binding modes of Au-AgNPs on the sponge. Finally, we pointed out the advantages of flexible substrates in the field of pesticide residues, as well as future opportunities and challenges.

Research on a three-dimensional SERS substrate based on a CNTs/Ag@Au/SiO2 composite for detection of fipronil and imidacloprid pesticides.

Surface-enhanced Raman scattering (SERS) has a unique fingerprint spectrum, which allows for rapid, highly sensitive, and non-destructive detection without the need for sample pretreatment. However, SERS substrates have disadvantages such as short storage time and poor reproducibility. In this study, carbon nanotubes, gold, and silver were combined to take advantage of their inherent structural and characteristic properties that enhance the Raman effect. A new type of SERS composite substrate, CNTs/Ag@Au/SiO2, was prepared using a hydrothermal method and seed growth method. The substrate was characterized using transmission electron microscopy (TEM), and the average distance between the core-shell nanoparticles was found to be 3.1 nm, which is more suitable than other gold-silver combined core-shell structures and significantly improves the SERS enhancement factor. The substrate demonstrated high sensitivity even at low concentrations of probe molecules and good uniformity at five randomly selected locations. After storage for 45 days, the substrate still exhibited good stability. In most gold-silver combined core-shell structures, the detection limit for Rhodamine 6G (R6G) is 10-9 mol L-1, while in this substrate, the detection limit for R6G is 10-11 mol L-1. Furthermore, the contribution of the substrate's enhancement was deeply investigated using finite-difference time-domain (FDTD), which revealed that the substrate's hotspots were present in two forms: the "hotspots generated between Ag@Au nanoparticles" and the "hotspots generated between Ag@Au nanoparticles and carbon nanotubes". These two forms of hotspots also demonstrated that the performance brought about by the preparation of the substrate structure was reliable. The simulation results were compared with the experimental results, and the analysis showed that the real environment would have an impact on the substrate's structure during the actual substrate preparation process. Finally, the substrate was used for detecting the pesticide fipronil, and the results showed clear peaks even at a concentration of 0.1 mg L-1. The results indicated that the Raman intensity was linearly exponential with the fipronil solution concentration, with a determination coefficient of R2 = 0.991. This study provides a new SERS substrate for pesticide residue detection and further explores the improvement of pesticide detection limits.

Research on Talent Cultivation Models and Strategies for Pesticide Residue Detection in Higher Education

Research on Talent Cultivation Models and Strategies for Pesticide Residue Detection in Higher Education

Synthesis of group I–III–VI semiconductor quantum dots and its application in food safety testing

Abstract As a good fluorescent material, most I–III–VI ternary semiconductor quantum dots (QDs) do not contain heavy metal elements such as Cd and Pb and thus have low toxicity. In addition, they also have excellent data performance such as narrow band gap, large absorption coefficient, large Stokes shift, low self-absorption effect, and luminescence wavelength in the near infrared region. I–III–VI QDs also have a size-dependent luminescence in the visible and near-infrared ranges, and their fluorescence quantum yields can exceed 50% especially when coated with ZnS shells, thus offering great potential for applications in illumination, display, and bioimaging. The review focuses on the synthesis of group I–III–VI QDs, their properties, and applications. In the first part, we discuss the approaches to synthesize these QDs. In the second section, we provide an overview of the applications of I–III–VI QDs in food safety detection, with emphasis on the detection of heavy metal ions, foodborne pathogens, food additives, pesticide, and veterinary drug residues. The final section summarizes the current application prospects of ternary semiconductor QDs.

MoS2/Au/Ag Nanostructures for Ratiometric Surface-Enhanced Raman Scattering Determination of Pesticide Residues

Surface-enhanced Raman scattering (SERS) is a promising method for rapid and sensitive detection of pesticide residues. Herein, a three-dimensional MoS2/Au/Ag nanostructure was developed by employing composite systems containing semiconductors, noble metal nanoparticles, and internal standard. The MoS2 nanoflowers provide abundant anchors for the loading of plasmonic nanoparticles so as to generate intense electromagnetic “hot spots,” and the Au-MBN/Ag nanostructures can increase the accuracy of SERS detection with the calibration ability of the internal standard molecules (4-mercaptobenzonitrile, 4-MBN). Therefore, the MoS2/Au/Ag nanostructures integrate the advantages of high-enhancement SERS substrates and high-accuracy ratiometric Raman detection. Moreover, the nanostructures were fabricated via a facile procedure and showed high sensitivity, accuracy, and reproducibility in SERS detection. The nanostructures also showed good linear fitting between the ratios of Raman intensity and logarithmic concentrations of analytes in ratiometric SERS analysis. In addition, thiabendazole (TBZ) in apple juice was successfully detected by using the as-prepared SERS substrates with a limit of detection down to 0.1 ppm in practical applications. To conclude, the MoS2/Au/Ag nanostructures showed excellent SERS activity in ratiometric analysis and the substrate is expected to have great potential for the quantitative detection of low-concentration pesticide residues.

ZnO nanorods decorated with Ag nanoflowers as a recyclable SERS substrate for rapid detection of pesticide residue in multiple-scenes

Pesticide residues threaten the ecological environment and human health. Therefore, developing high performance SERS substrate to achieve highly sensitive detection of pesticide residues is meaningful. In this study, based on the strategy of combining “hot spots” engineering and material hybridization, we construct a novel hybrid SERS substrate by depositing Ag nanoflowers (NFs) on ZnO nanorods (NRs). Benefiting from the synergistic effect of electromagnetic enhancement and charge transfer effect, the Ag NFs@ZnO NRs substrate exhibits a low detection limit (10−13 M) for crystal violet molecules. This SERS substrate has good uniformity with a relative standard deviation of 7.463 %. Besides, owning to the photocatalytic property of ZnO NRs, the hybrid substrate can degrade probe molecules after SERS detection and realize recyclability. As a demonstration, we employed our SERS substrate for the trace detection of pesticide residues on apple surface and in river water. This study provides a new idea for improving the SERS performance of hybrid substrates.

Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine.

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.

Electrochemical Organophosphorus Pesticide Detection Using Nanostructured Gold-Modified Electrodes

In this study, nanostructured gold was successfully prepared on a bare Au electrode using the electrochemical deposition method. Nanostructured gold provided more exposed active sites to facilitate the ion and electron transfer during the electrocatalytic reaction of organophosphorus pesticide (methyl parathion). The morphological and structural characterization of nanostructured gold was conducted using field-emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD), which was further carried out to evaluate the electrocatalytic activity towards methyl parathion sensing. The electrochemical performance of nanostructured gold was investigated by electrochemical measurements (cyclic voltammetry (CV) and differential pulse voltammetry (DPV)). The proposed nanostructured gold-modified electrode exhibited prominent electrochemical methyl parathion sensing performance (including two linear concentration ranges from 0.01 to 0.5 ppm (R2 = 0.993) and from 0.5 to 4 ppm (R2 = 0.996), limit of detection of 5.9 ppb, excellent selectivity and stability), and excellent capability in determination of pesticide residue in real fruit and vegetable samples (bok choy and strawberry). The study demonstrated that the presented approach to fabricate a nanostructured gold-modified electrode could be practically applied to detect pesticide residue in agricultural products via integrating the electrochemical and gas chromatography coupled with mass spectrometry (GC/MS-MS) analysis.

Facile Method to Fabricate Cactus-like Ag NPs/CuO/Cu2O Nanocomposites for Recyclable SERS Detection of Trace Carbendazim Residues

Detection of trace carbendazim in agricultural products or environment is challenging but important for human health and environmental protection. Developing a sensitive and recyclable surface-enhanced Raman scattering (SERS) substrate is one of the effective methods to detect such pesticide residues. In this work, we developed a facile method to fabricate a three-dimensional cactus-like Ag nanoparticles/CuO/Cu2O nanocomposite (CACN) based on a galvanic replacement reaction between Tollens’ reagent and Cu foam. This design combined the Raman enhancement effect and photocatalytic activity, providing a multifunctional and reusable substrate for highly sensitive SERS detection. It was found that the enhancement and photocatalysis of the CACN substrate were closely related to the concentration of Tollens’ reagent, and the 10-CACN substrate (prepared at 10 mM) demonstrated excellent sensitivity, reproducibility, and recyclability. The substrate was successfully applied for the detection of carbendazim solution with a detection limit of 1.5 × 10–10 M and could be reused up to 7 times. More importantly, the substrate can be employed for the detection of trace carbendazim in real apple peels with a detection limit of ∼8.5 × 10–4 ppm, which is far below that of 2.0 × 10–1 ppm suggested by China, United Kingdom, and European Union. These results suggest that the facile fabrication method and the excellent substrate would facilitate the SERS technology closer to practical application in the detection of pesticide residues.

Application of GC-MS in Food Pesticide Residue Detection

Application of GC-MS in Food Pesticide Residue Detection

3D Hot Spots Droplets on the MoS2/Al Hydrophobic Array Interface for SERS Quantitative and Multiplex Detection of Pesticide Residues

AbstractThe practical application of surface‐enhanced Raman spectroscopy (SERS) technology for quantitative analysis still confronts the hinder of controlling the uniformity of signal and anchoring target molecules in a designed space. Herein, a facile method of devising the shrinkage of three‐dimension (3D) hot spots droplets on a hydrophobic array substrate is demonstrated for the quantitative and multiplex detection of pesticide residues by using a portable Raman spectrometer. The shrunk droplet which is consisted of the analyte solution and AgNPs sol can induce simultaneously increasing density of plasmonic hot spots and immobilizing target molecules in hot spots. Besides, the closely packed Ag nanoparticles (AgNPs) are anchored in the gaps of the MoS2 nanosheet array, generating uniform hotspots distribution with relative standard deviation (RSD) of about 8 %. In addition, the synergetic effects between MoS2 and AgNPs further enhance the SERS sensitivity, and thereby target molecules of 10−9 m are readily detected. Besides, the proposed strategy exhibits excellent SERS sensitivity for the analysis of pesticides down to 10−8 m. Importantly, quantitative analysis and multi‐components detection of pesticide residues on honeysuckle surface are achieved. Therefore, it is expected to be used for quantitative analysis of complex pesticide systems on real samples.

A study on a hybrid SERS substrates based on arrayed gold nanoparticle/graphene/copper cone cavities fabricated by a conical tip indentation

The pesticide residues with low concentration are important to detect in food safety. Although a lot of methods, such as spectrophotometry, colorimetry and liquid chromatography, are used to detect pesticide residues, they often suffer from low efficiency and long detection time. Here, a hybrid Surface Enhanced Raman Scattering (SERS) substrate, namely, Au nanoparticle/copper-based graphene/arrayed cone cavities (Au@GR@Cu cone cavities), is fabricated for pesticide residues detection through sputtering Au nanoparticle and generating periodical cone cavity structures on the copper-based graphene surface. By actively controlling the interspace between adjacent cone cavities, both surface topographies and the number of graphene layers on the SERS substrate could be modulated, creating an effective control on the functional performance of the SERS substrate. Both sensitivity and reliability of the hybrid SERS substrate were evaluated by Raman Spectroscopy detection. The experimental results indicated that both 10−9 mol/L malachite green and 10−7 mol/L paraquat could be detected, respectively. Moreover, the detected Raman intensity of malachite green only dropped by 11.89% in a month and 29.8% in two months, respectively. The hotspot distribution was also characterized by 2-dimensional Raman mapping. The research finding is expected to provide a new insight in development of innovative SERS substrates.

Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution.

Pesticide residues directly or indirectly threaten the health of humans and animals. We need a rapid and nondestructive method for the safety evaluation of fruits. In this study, the feasibility of visible/near-infrared (Vis/NIR) spectroscopy technology was explored for the discrimination of pesticide residue levels on the Hami melon surface. The one-dimensional convolutional neural network (1D-CNN) model was proposed for spectral data discrimination. We compared the effect of different convolutional architectures on the model performance, including single-depth, symmetric, and asymmetric multiscale convolution. The results showed that the 1D-CNN model could discriminate the presence or absence of pesticide residues with a high accuracy above 99.00%. The multiscale convolution could significantly improve the model accuracy while reducing the modeling time. In particular, the asymmetric convolution had a better comprehensive performance. For two-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 93.68% and 95.79%, respectively. For three-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 86.32% and 89.47%, respectively. For four-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 87.37% and 93.68%, respectively, and the average modeling time was 3.5 s. This finding will encourage more relevant research to use multiscale 1D-CNN as a spectral analysis strategy for the detection of pesticide residues in fruits.

Multiple pesticide residue determination in major vegetables purchased from Gazipur district of Bangladesh

The present study was initiated to detect and quantify pesticide residues in three selected major vegetables (hyacinth bean, cauliflower and yard long bean) collected from 5 local markets of Gazipur district of Bangladesh. The vegetable samples were prepared by Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) extraction technique and analyzed by Gas Chromatography (GC) equipped with Flame Thermionic Detector (FTD). In this study, 45 vegetable samples were analyzed. Out of 15 samples of hyacinth bean, 2 samples (13% of the total samples) contained pesticide residues above MRL and the remaining samples did not contain any detectable pesticide residues. In the instance of cauliflower, 2 samples (13% of the total number of samples) contained pesticide residues above MRL, while the remaining samples had no detectable pesticide residues. Pesticide residues with levels exceeding MRL were found and measured in 3 samples (20% of the total number of samples) from 15 yard long bean samples. From remnant samples, no pesticide residue was detected. This study depicts the overall scenario of pesticide contamination in vegetables specially in hyacinth bean, cauliflower and yard long bean available in the local markets of Gazipur city, Bangladesh. As a result, it is important to intensify routine pesticide residue monitoring in commercially cultivated vegetables. Asian Australas. J. Food Saf. Secur. 2022, 6 (2), 57-64

Sensitive SERS detection of pesticide residues in beverages based on an extraction integrated plasmonic platform

Organic extraction is a commonly used pretreatment method for the detection of pesticide residues in food samples. However, this process increases the complexity for surface-enhanced Raman scattering (SERS) detection since the traditional sol-based SERS substrates are usually aggregated in organic solvents, while the drop-on-solid method always leads to inevitable coffee-ring effect. Therefore, in this study, an extraction integrated plasmonic platform was developed to achieve the sensitive surface-enhanced Raman scattering (SERS) detection of pesticide residues in beverages. This platform was based on a floating SERS substrate (HSM@AuNPs) that was fabricated by evenly decorating gold nanoparticles (AuNPs) on hollow silica microspheres (HSMs). The HSM@AuNPs can self-located at water/oil interface and can be easily transferred onto a filter paper, thus providing accessible and flexible hot spots for the SERS detection of analytes that were enriched in a bit of extraction phase. We verified that this platform displayed much lower limit of detection (LOD) in the detection of thiabendazole in tea drink (102 ppb) and thiram in apple juice (10 ppb), by using ethyl acetate as extraction solvent. This platform not only provides a sensitive and convenient methodology for the SERS detection of pesticide residues, but also provides new ideas for the application of SERS technology in multi-phase systems.

Engineering an Ag/Au bimetallic nanoparticle-based acetylcholinesterase SERS biosensor for in situ sensitive detection of organophosphorus pesticide residues in food.

Developing simple, efficient, and inexpensive method for trace amount organophosphorus pesticides' (OPs) detection with high sensitivity and specificity is of significant importance for guaranteeing food safety. Herein, an Ag/Au bimetallic nanoparticle-based acetylcholinesterase (AChE) surface-enhanced Raman scattering (SERS) biosensor was constructed for in situ simple and sensitive detection of pesticide residues in food. The principle of this biosensor exploited 4-mercaptophenylboronic acid (4-MPBA)-modified Ag/Au bimetallic nanoprobes as SERS signal probe to improve sensitivity and stability. The combination of AChE and choline oxidase (CHO) can hydrolyze acetylcholine (ATCh) to generate H2O2. The product of H2O2 selectively oxidizes the boronate ester of 4-MPBA, decreasing the Raman intensity of the B-O symmetric stretching. In the presence of OPs, it could inhibit the production of H2O2 by destroying the AChE activity, so the reduction of the SERS signal was also alleviated. Based on the principle, an Ag/Au bimetallic nanoparticle-based AChE SERS sensor was established without any complicated pretreatments. Benefiting from the synergistic effects of Ag/Au bimetallic hybrids, a linear detection range from 5×10-9 to 5×10-4 M was achieved with a limit of detection down to 1.7×10-9 M using parathion-methyl (PM) as the representative model of OPs. Moreover, the SERS biosensor uses readily available reagents and is simple to implement. Importantly, the proposed SERS biosensor was used to quantitatively analyze OP residues in apple peels. The levels of OPs detected in real samples by this method were consistent with those obtained using gas chromatography-mass spectrometry (GC-MS), suggesting the proposed assay has great potential applications for OPs in situ detection in food safety fields.

A two-stage framework for detection of pesticide residues in soil based on gas sensors

The analysis of pesticide residues in soil is essential for the ecological environment, food safety and human health. Focused on the real time detection of pesticide residues in soil by using E-nose technique, this paper proposes a two-stage framework to identify the categories and concentrations of pesticide residues. This method is based on multi-task learning and transfer learning, which improves the generalization ability of the model. Experimental results show that the proposed method outperforms the comparing existed methods, and verify the effectiveness of two-stage stratege and transfer learning as well. It can be potentially applied to the in situ identification of soil pesticide residues, which is of great significance for soil improvement and agricultural production.

Controllable assembly of high sticky and flexibility surface-enhanced Raman scattering substrate for on-site target pesticide residues detection

Pesticide residues in fruit and vegetable constitute a great threat to mankind's health, and a technique of rapid, cost-effective, ultra-sensitive and non-destructive analysis is especially critical for on-site detection in practical application. A strategy was utilized to fabricate highly viscous and flexible WPU@AgNPs sticky tape for the rapid extraction and Surface-enhanced Raman Scattering (SERS) assay of thiabendazole residues on the surface of fruits. Simply put, the novel WPU@AgNPs tapes were constructed by decorating the waterborne polyurethane (WPU) film with silver nanoparticles (AgNPs), and demonstrated excellent sensitivity with 4-Mercaptopyridine (4-Mpy) as the probe molecule. Meanwhile, the enhancement factor of the novel SERS substrate could reach 7.01 × 106, which was consistent with the finite difference time domain (FDTD) theoretical simulation results. What’s more, the high viscosity and flexibility of WPU@AgNPs sticky tapes allowed them to detect the thiabendazole residues on the pear, apple and banana surfaces by a straightforward “paste and peel off” approach. The results showed that the detection limit could reach 1.44, 1.12 and 1.63 ng/cm2. This strategy would be as a common method for prompt, on-site and ultra-sensitive assay of thiabendazole residues in chemical and biological fields.

Copper-Modified Double-Emission Carbon Dots for Rapid Detection of Thiophanate Methyl in Food.

The detection of food safety and quality is very significant throughout the food supply. Stable dual-emission copper-modified fluorescent carbon dots (Cu-CDs) were successfully synthesized by a simple and environment-friendly hydrothermal, which was used for the real-time detection of pesticide residues in agricultural products. By optimizing the reaction conditions, Cu-CDs showed two emission peaks, with the highest fluorescence intensities at 375 and 450 nm. The structure, chemical composition and optical properties of Cu-CDs were investigated by XRD, TEM and IR. The results showed that thiophanate methyl (TM) could induce fluorescence quenching of Cu-CDs with no other ligands by the electron transfer through π-π stacking. The synchronous response of the dual-emission sensor enhanced the specificity of TM, which showed remarkable anti-interference capability. The fluorescence quenching degree of Cu-CDs had a good linear relationship with the TM concentration; the low detection limit for a pear was 0.75 μM, and for an apple, 0.78 μM. The recoveries in the fruit samples were 79.70-91.15% and 81.20-93.55%, respectively, and the relative standard deviations (RSDs) were less than 4.23% for the pear and less than 3.78% for the apple. Thus, our results indicate the feasibility and reliability of our methods in detecting pesticide residues in agricultural products.

Glutathione-modified graphene quantum dots as fluorescent probes for detecting organophosphorus pesticide residues in Radix Angelica Sinensis

A novel fluorescent sensor was developed in this study based on glutathione-functionalized graphene quantum dots (GQDs@GSH) to detect organophosphorus pesticide residues in Radix Angelica Sinensis. GQDs@GSH was synthesized by a one-step pyrolysis method with a fluorescence quantum yield as high as 33.9% and its structure was characterized by transmission electron microscopy and X-ray photoelectron spectroscopy. GQDs@GSH exhibited excellent fluorescence property showing strong blue fluorescence under UV irradiation. The fluorescence of GQDs@GSH could be quenched by Fe3+ by electron transfer and the quenched fluorescence could be recovered due to the strong chelating and reducing ability of phytic acid (PA). Under the catalyzation of acetylcholinesterase (AChE) and choline oxidase (ChOx), acetylcholine (ACh) could be decomposed to H2O2, which could further oxidize Fe2+ to Fe3+ thus quenching the fluorescence of GQDs@GSH once again. Coumaphos, a kind of organophosphorus pesticide, could inhibit AChE activity, thus making the quenched fluorescence turn on again. Several parameters influencing the fluorescence response such as Fe3+, PA, ACh and coumaphos concentration, pH value and reaction time were optimized. Based on such a fluorescence “off-on-off-on” ngkmechanism, GQDs@GSH was successfully applied to the detection of coumaphos in Radix Angelica Sinensis. A good linear relationship between the fluorescence intensity and coumaphos concentration was obtained in the range of 0.1–10.0 μmol·L−1. By a standard addition method, the recoveries were measured to be 101.44–117.90% with RSDs lower than 1.98%. The biosensor system is simple, sensitive and accurate. It has a good application prospect in the detection of organophosphorus pesticide residues in traditional Chinese medicine and agricultural products, and also expanded the application scope for glutathione as a highly selective biological molecule.