Fabrication of UL-AuAgMSs@β-CD composite substrate for synergistically enhanced SERS trace detection of organophosphorus pesticide residues

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000–2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Single-atom Ce-N-C nanozyme bioactive paper with a 3D-printed platform for rapid detection of organophosphorus and carbamate pesticide residues

The excessive pesticide residues in cereals, fruit and vegetables is a big threat to human health, and it is necessary to develop a portable, low-cost and high-precision pesticide residue detection scheme to replace the large-scale laboratory testing equipment for rapid detection of pesticide residues. In this study, a colorimetric device for rapid detection of organophosphorus pesticide residues with high precision based on a microfluidic mixer chip was proposed. The microchannel structure with high mixing efficiency was determined by fluid dynamics simulation, while the corresponding microfluidic mixer chip was designed. The microfluidic mixer chip was prepared by a self-developed liquid crystal display (LCD) mask photo-curing machine. The influence of printing parameters on the accuracy of the prepared chip was investigated. The light source with the optimal wavelength of the device was determined by absorption spectrum measurement, and the relationship between the liquid reservoir depth and detection limit was studied by experiments. The correspondence between pesticide concentration and induced voltage was derived. The minimum detection concentration of the device could reach 0.045 mg·L−1 and the average detection time was reduced to 60 s. The results provide a theoretical and experimental basis for portable and high-precision detection of pesticide residues.

Pesticide residues in the fruits, vegetables and agricultural commodities are harmful to humans and are becoming a health concern nowadays. Detection of pesticide residues on various commodities in an open environment is a challenging task. Hyperspectral sensing is one of the recent technologies used to detect the pesticide residues. This paper addresses the problem of detection of pesticide residues of Cyantraniliprole on grapes in open fields using multi temporal hyperspectral remote sensing data. The re ectance data of 686 samples of grapes with no, single and double dose application of Cyantraniliprole has been collected by handheld spectroradiometer (MS- 720) with a wavelength ranging from 350 nm to 1052 nm. The data collection was carried out over a large feature set of 213 spectral bands during the period of March to May 2015. This large feature set may cause model over-fitting problem as well as increase the computational time, so in order to get the most relevant features, various feature selection techniques viz Principle Component Analysis (PCA), LASSO and Elastic Net regularization have been used. Using this selected features, we evaluate the performance of various classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to classify the grape sample with no, single or double application of Cyantraniliprole. The key finding of this paper is; most of the features selected by the LASSO varies between 350-373nm and 940-990nm consistently for all days. Experimental results also shows that, by using the relevant features selected by LASSO, SVM performs better with average prediction accuracy of 91.98 % among all classifiers, for all days.

Improvements in living standards have led to an increase in the consumption of animal-derived foods. Pesticides may be used illegally during animal breeding as well as meat production and processing for pest control and preservation. Pesticides applied to crops may also be enriched in animal tissues through the food chain, thereby increasing the risk of pesticide residue accumulation in muscles and visceral tissues and endangering human health. China has stipulated maximum residue limits for pesticide residues in livestock and poultry meat and their viscera. Many other major developed countries and organizations, including the European Union, Codex Alimentarius Commission, and Japan, have also set maximum residue limits for these residues (0.005-10, 0.004-10, and 0.001-10 mg/kg, respectively). Research on pretreatment technologies for pesticide residue detection in plant-derived foods is widely available, but insufficient attention has been paid to animal-derived foods. Thus, high-throughput detection technologies for pesticide residues in animal-derived foods are limited. The impurities that can interfere with the detection process for plant-derived foods mainly include organic acids, polar pigments, and other small molecular compounds; by contrast, the matrix of animal-derived foods is much more complex. Macromolecular proteins, fats, small molecular amino acids, organic acids, and phospholipids can interfere with the detection of pesticide residues in animal-derived foods. Thus, selecting the appropriate pretreatment and purification technology is of great importance. In this study, the QuEChERS technique was combined with online gel permeation chromatography-gas chromatography-tandem mass spectrometry (GPC-GC-MS/MS) to determine 196 pesticide residues in animal-derived foods. The samples were extracted with acetonitrile, purified using the QuEChERS technique coupled with online GPC, detected by GC-MS/MS, determined in multiple reaction monitoring mode (MRM), and quantified using the external standard method. The effects of the extraction solvent and purification agent type on the extraction efficiency and matrix removal of the method were optimized. The purification effect of online GPC on the sample solution was investigated. The optimal distillate receiving time was obtained by studying the recoveries of the target substances and matrix effects over different distillate receiving periods to achieve the effective introduction of target substances and efficient matrix removal. Further, the advantages of the QuEChERS technique combined with online GPC were evaluated. The matrix effects of 196 pesticides were assessed; ten pesticide residues showed moderate matrix effects, while four pesticide residues showed strong matrix effects. A matrix-matched standard solution was used for quantification. The 196 pesticides showed good linearity in the range of 0.005-0.2 mg/L, with correlation coefficients greater than 0.996. The limits of detection and quantification were 0.002 and 0.005 mg/kg, respectively. The recoveries of 196 pesticides at spiked levels of 0.01, 0.05, and 0.20 mg/kg were 65.3%-126.2%, with relative standard deviations (RSDs) of 0.7%-5.7%. The proposed method is rapid, accurate, and sensitive; thus, it is suitable for the high-throughput screening and detection of multiple pesticide residues in animal-derived foods.

The accurate detection of pesticide residues in fresh produce is essential for public health. This study evaluated the use of two-dimensional correlation spectroscopy (2D-COS), specifically applied to Raman data, combined with principal component analysis (PCA) and support vector machines (SVMs) for the direct and rapid detection of chlorothalonil pesticide residues in four different vegetable matrices. This approach significantly enhances the spectral resolution, enabling the identification of subtle chlorothalonil-specific fingerprints. Raman spectra were analyzed across the full wave range (300–2500 cm–1), with four key fingerprint regions identified through 2D-COS: 354−414 cm–1 (C–Cl stretching), 1260−1286 cm–1 (C–C stretching), 1540−1570 cm–1 (C–C stretching), and 2250−2265 cm–1 (C≡N stretching). When applied to the full spectral range, the PCA-SVM model yielded only moderate classification accuracy (accuracy = 72%, κ = 0.43). In contrast, models focused on the fingerprint regions achieved perfect classification (accuracy = 100%, κ = 1), successfully distinguishing between the spiked and control samples. The consistent detection of C–Cl and C≡N bonds in all vegetable matrices highlights their reliability as universal markers for chlorothalonil detection. These findings demonstrate the potential of Raman spectroscopy combined with targeted data analysis, as a highly sensitive and reliable method for pesticide residue screening.

This paper reports a “bottom‐up” substrate preparation method using a two‐phase interface self‐assembly technology that combines silver‐coated gold core–shell nanorods with the thermally shrinkable polystyrene (TSP) support material for surface‐enhanced Raman spectroscopy (SERS), that is, TSP‐SERS substrate. The gold nanorods with long absorption wavelength were used as the core, and the silver shells were coated to obtain the core–shell nanostructures with a stronger resonance with the wavelength of the light source. The density of the nanostructures and numbers of “hot spots” within the light spot increased via the three‐dimensional folding feature formed by thermal shrinkage. The combined effect of the two factors increases the enhancement factor by an order of magnitude to 107 after thermal contraction. The detection concentration of 4‐mercaptobenzoic acid can reach 10−9 M, and the maximum relative standard deviation is only 8.9%. In fact, the detection limit of benzimidazole on the surface of apple can reach 0.5 mg/L, and the recovery deviation is controlled within the range of 11.7%. The practical detection of benzimidazole pesticide residues showed that this method has wide application prospects in the detection of pesticide residues.

Organophosphorus pesticide residues in market foods in Shaanxi area, China

The rampant use of pesticides in agricultural practices poses significant risks to human health and the environment. To mitigate these risks, regulatory authorities enforce stringent measures to monitor and control pesticide residues in food matrices. Even trace amounts of pesticide residues in various food products, particularly fruits and vegetables, can lead to severe health implications. Consequently, considerable efforts have been directed towards the development of advanced analytical methodologies for the extraction and quantification of pesticide residues. This comprehensive study provides a thorough review of advanced techniques employed in the detection and quantification of pesticide residues in fruits and vegetables. Various instrumental approaches, including gas chromatography coupled with ECD, FPD, NPD, MSD, Q-TOFMS and liquid chromatography combined with UV, DAD, MSD and Q-TOFMS, have been extensively discussed. Each analytical technique offers distinct advantages and limitations in terms of sensitivity, selectivity and applicability to different classes of pesticides. Through an in-depth exploration of these advanced analytical methodologies, this study aims to facilitate a deeper understanding of pesticide residue detection in fruits and vegetables, thereby informing regulatory measures and promoting food safety and public health.

A new type of durian-shaped Fe3O4@Au@Ag@Au (DFAAA) multilayer core-shell composite was prepared as an efficient surface-enhanced Raman scattering (SERS) substrate. The optimization process and SERS enhancement mechanism of the substrate were further explained with finite-difference time-domain simulation. The dense and uniform spiny array on the DFAAA surface had abundant “hot spots”, greatly improving sensitivity, uniformity and reproducibility, with a Raman enhancement factor of 3.01 × 107 and storage-life of 30 d. A “flow magnetic detection method” was proposed to realize rapid and flexible detection of pesticide residues on the surface of different foods including fish and apple. The limit of detection of malachite green and thiram on the fish and apple surfaces were 0.13 and 0.18 ng/cm2, respectively. With its high SERS performance and good magnetic, the DFAAA possessed great application prospects as a facile SERS substrate for rapid and non-destructive detection of trace pesticide residues on foods.

Nowadays, detection of pesticide residues is generally conducted in the laboratory using large-scale equipment such as liquid chromatography mass spectrometry, etc. These detection methods usually require a complicated pre-treatments and professional operators, which are time-consuming and expensive. Surface-enhanced Raman scattering (SERS) technology makes trace detection of pesticide residues implementable, but currently the devices for SERS on the market are all large-scale and rapid detection of pesticide residues has not been achieved yet. In this paper, a novel SERS system integrated with a cellphone is designed for the detection of pesticide residues. In the current system, the SERS chip is inserted directly onto the, and measurement of pesticide residues can be conducted by one-click through the cellphone application. We achieved successfully detection of 12 kinds of pesticides with characteristic Raman spectra and the limit of detection was less than 10 ppm, which makes rapid and on-site detection of pesticide residues feasible in future point of care test applications.

The pesticide and veterinary drug residues brought by large-scale agricultural production have become one of the issues in the fields of food safety and environmental ecological security. It is necessary to develop the rapid, sensitive, qualitative and quantitative methodology for the detection of pesticide and veterinary drug residues. As one of the achievements of nanoscience, quantum dots (QDs) have been widely used in the detection of pesticide and veterinary drug residues. In these methodology studies, the used QD-signal styles include fluorescence, chemiluminescence, electrochemical luminescence, photoelectrochemistry, etc. QDs can also be assembled into sensors with different materials, such as QD-enzyme, QD-antibody, QD-aptamer, and QD-molecularly imprinted polymer sensors, etc. Plenty of study achievements in the field of detection of pesticide and veterinary drug residues have been obtained from the different combinations among these signals and sensors. They are summarized in this paper to provide a reference for the QD application in the detection of pesticide and veterinary drug residues.

Aiming at the organophosphorus pesticide residues in tea, a method integrating solid-phase microextraction and liquid chromatography-mass spectrometry was investigated for the detection of organophosphorus pesticide residues in tea. Firstly, the conditions in the process were optimized; then the method was established to analyze the organophosphorus pesticides in tea; finally, the method feasibility was verified by using actual sample determination. In terms of the results, the standard curve correlation coefficients of the solid-phase microextraction and liquid chromatography-mass spectrometry methods used in the study were greater than 0.99, while the average recoveries ranged from 72 to 109% with the relative standard deviations (RSDs) of less than 9.0%. This indicates that the method has a good linear range, low detection limit and high recovery. In the application validation in real samples, the concentrations of organophosphorus pesticide residues in tea were within the safe range. The method was validated to be suitable for the monitoring and control of organophosphorus pesticide residues in tea with low cost and simplified sample preparation.

The problem of pesticide residue contamination has attracted widespread attention and poses a risk to human health. The current traditional pesticide residue detection methods have difficulty meeting rapid and diverse field screening requirements. Microfluidic technology integrates functions from sample preparation to detection, showing great potential for quick and accurate high-throughput detection of pesticide residues. This paper reviews the latest research progress on microfluidic technology for pesticide residue detection. First, the commonly used microfluidic materials are summarized, including silicon, glass, paper, polydimethylsiloxane, and polymethyl methacrylate. We evaluated their advantages and disadvantages in pesticide residue detection applications. Second, the current pesticide residue detection technology based on microfluidics and its application to real samples are summarized. Finally, we discuss this technology's present challenges and future research directions. This study is expected to provide a reference for the future development of microfluidic technology for pesticide residue detection.

A new analytical method for gas chromatography (GC) or GC-mass spectrometry (MS) using the direct sampling technique is described. This direct sampling technique, which bypasses the conventional complicated sample pretreatment process, is applicable to cases of fast detection of pesticide residues in foods and large-scale screening of samples by portable GC in field detection. By a direct sampling technique, the vegetable sample is ground into paste, and 30 mg is placed directly into the evaporating chamber for GC-MS identification and quantitation (by full-scan mode). The GC column used is an HP-5 (30.0-m x 250-microm x 0.25-microm, 5% phenyl methyl siloxane). Chlorpyrifos, bromophos, fenpropathrin, gamma-666, and pp'-DDT are chosen to represent organophosphorus, pyrethrins, and organochlorine pesticides because they are chief objects of the detection of pesticide residues in vegetables. Rape, a common and mass-consumed vegetable in China, is chosen as the sample in this study. The detection limits for these pesticides by the full-scan mode are all below the maximum pesticide residue limit of vegetables set by the Ministry of Agriculture of China, and the reproducibility of this method is acceptable. This analysis method is proven to be simple, quick, and reliable and is suitable for multipesticide residues analysis of vegetables. It can also be used in the analysis of vegetable components and signal chemicals.