Development of multi-residue analysis of herbicides in cereal grain by ultra-performance liquid chromatography-electrospray ionization-mass spectrometry

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images - EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

Carbon Nanotube-Supported Dummy Template Molecularly Imprinted Polymers for Selective Adsorption of Amide Herbicides in Aquatic Products

In this study, a carbon nanotube (CNTs)-supported dummy template molecularly imprinted polymer (DMIPs) material was synthesized and utilized for the detection of amide herbicides in aquatic products via matrix solid-phase dispersion technology (MSPD). The DMIPs material was characterized, and its adsorption kinetics and isotherm were determined, the adsorption model was established, and the selective adsorption coefficient was calculated. The extract parameters of the method were optimized and successfully employed for the separation, analysis and detection of real samples, with satisfactory detection limits and linear ranges obtained. By comparing with other methods, the CNTs@DMIPs combined with MSPD technology established in our study can effectively solve false negative problems caused by insufficient destructive force, using dummy template molecules can also address the issue of false positives caused by template molecule leakage in molecular imprinting. Overall, the method is appropriate for the separation and detection of endogenous substances from highly viscous and poorly dispersed samples and is used as a routine detection tool in the aquaculture industry.

Bensulfuron-Methyl, Terbutylazine, and 2,4-D Butylate Disturb Plant Growth and Resistance by Deteriorating Rhizosphere Environment and Plant Secondary Metabolism in Wheat Seedlings

Frequent and improper use of herbicides disrupts a plant's metabolism, causing oxidative stress that degrades crop quality. However, few studies have examined the inhibitory effects of herbicides on plant growth and defense mechanisms in terms of their impact on soil quality and crop rhizosphere. Therefore, the current study investigated the detrimental impacts of six typical and multilevel herbicides on the microbial community and signal molecules in the soil as well as on the levels of hormones and secondary metabolites in wheat seedlings. Interestingly, bensulfuron-methyl, terbutylazine (TBA), and 2,4-D butylate significantly induced oxidative damage while reducing the number of phytohormones (salicylic acid and jasmonic acid) and secondary metabolites (tricin, quercetin, and caffeic acid) in the roots and leaves compared with the controls, isoproturon, fenoxaprop-p-ethyl, and pretilachlor. At twice the recommended levels (2×), they also decreased the microbial α diversity and, in particular, the abundance of Gammaproteobacteria, Alphaproteobacteria, Actinobacteria, Bacteroidia, Verrucomicrobia, Bacilli, Acidimicrobiia, Deltaproteobacteria, and Gemmatimonadetes by disrupting the level of enzymes (e.g., urease and sucrase) and metabolites (indole-3-acetic acid, salicylic acid, apigenin, 4-hydroxybenzoic acid, DIMBOA, and melatonin) in the rhizosphere soil. Overall, significant exposure to herbicides may inhibit wheat growth by disturbing the microbial composition in the rhizosphere soil and the distribution of secondary metabolites in wheat seedlings.

A Review on Recent Innovations of Pretreatment and Analysis Methods for Sulfonylurea Herbicides

Sulfonylurea herbicides (SUHs) are widely used in agriculture because of their low dosage, low cost, and high selectivity. However, due to improper use and lack of effective management, their residues pose a threat to the human health through environment and food pollution. Therefore, there is a need for simple, quick, economical, and effective methods to analyze SUHs in plant-derived foods, crops, and environmental samples. The present article presents a comprehensive review of the pretreatment and analytical technologies used for SUHs in various sample matrices, focusing on the developments since 2010. The main pretreatment methods include liquid-liquid extraction, solid-phase extraction, QuEChERS, and different microextraction methods, whereas analytical methods mainly include liquid chromatography coupled with different detectors, capillary electrophoresis, among others. In addition, the present study also compared the advantages and disadvantages of the methods and the future development is prospected.

Rapid analysis of triazine herbicides in fruit juices using evaporation-assisted dispersive liquid-liquid microextraction with solidification of floating organic droplets and HPLC-DAD

A rapid and convenient analytical procedure (evaporation-assisted dispersive liquid-liquid microextraction with solidification of floating organic droplets) is advanced for determining the concentrations of triazine herbicide residues (e.g. simazine and atrazine) in fruit juices via HPLC-DAD. The technique involves adding 1-dodecanol (low density) and dichloromethane (high density) to the test solution to act as the extraction and volatile solvents, respectively. Calcium oxide is added to generate heat to accelerate the evaporation of dichloromethane, whereupon the 1-dodecanol quickly disperses into small droplets to complete the microextraction process. Thus, there is no need to use a dispersive solvent and heating equipment is also not required. The floating 1-dodecanol is subsequently frozen using an ice bath to facilitate its separation from the sample. Under optimal conditions (250 μL of 1-dodecanol (extraction solvent), 150 μL of CH₂Cl₂ (volatile solvent), 1250 mg of CaO, and an extraction time of 60 s) the detection procedure is linear over the range 0.05-5 μg mL^-1 (with R > 0.99). The limits of detection (LOD) and quantification (LOQ) were determined to be 0.0022-0.0034 μg mL^-1 and 0.0073-0.0113 μg mL^-1, respectively. The recovery of simazine and atrazine in three fruit juices ranged between 78.5% and 96.4% with a relative standard deviation <8.2%. Therefore, the proposed approach can be effectively adopted to analyze the triazine herbicide content in fruit juices. The method has been proved to be simple, reliable, and remarkably efficient.

Model prediction of herbicide residues in soybean oil: Relationship between physicochemical properties and processing factors

The distribution and processing factors (PFs) of herbicides in cold-/hot-pressed soybean samples (n = 3) were studied on the laboratory scale. The hot-pressing process was found to have a significant effect on herbicide degradation in soybean samples. Specifically, for highly water-soluble pesticides with pK_ow > 2 in soybean oil, the PF values were generally > 1. Nonlinear curve fitting revealed that the PFs of herbicides in soybean oil were positively correlated with their octanol-water partition coefficients, but negatively correlated with their water solubility and melting points. A principal component analysis confirmed the dominant parameters among the herbicide PFs during soybean oil production. Using the physicochemical parameters of pesticides, the developed multiple linear regression model gave a fitting accuracy of ≥0.80 for predicting the theoretical PF values of pesticides in soybean oil products (0.39 < RMSE < 0.58). Thus, this model may be applicable for safety risk assessments and establishing maximum residue limits for pesticides in processed products.

Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry

Waterfowl populations have been decreasing in Europe for the last years and pollution appears to be one of the main factors. This study was conducted to develop a single sensitive and robust analytical method for the monitoring of 2 fungicides, 15 herbicides, 3 insecticides and 24 transformation products in wild bird eggs. One of the major challenges addressed was the characterization of chemicals with large logP range (from -1.9 to 4.8). A total of 11 different extraction parameters were tested in triplicate to optimize the extraction protocol, on generic parameters, buffer addition and use of clean-up steps. Quantification was based on matrix-match approach with hen eggs as reference matrix (34 analytes with r²>0.99). Particular attention was payed to matrix effects (-28% on average), quantification limits (0.5 to 25 ng.g^-1 dry mass / 0.2 to 7.5 ng.g^-1 fresh mass) and extraction yields (46 to 87% with 25 analytes up to 70%) to ensure the relevance of the method and its compatibility with ultra-trace analysis. It led to a simple solid/liquid low temperature partitioning extraction method followed by LC-MS/MS. Analysis of 29 field samples from 3 waterfowl species revealed that eggs were slightly contaminated with pesticides as only one egg presented a contamination (terbutryn, herbicide, 0.7 ng.g^-1) and confirmed the relevance of the method.

Zirconium metal-organic framework assisted miniaturized solid phase extraction of phenylurea herbicides in natural products by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

The zirconium metal-organic framework (Zr-MOF) was used as a novel and effective adsorbent material for the enrichment of five phenylurea herbicides (fenuron, monuron, diuron, linuron and pencycuron) in natural products. The target analytes were determined by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Some crucial experimental parameters, such as type of adsorbents, amount of adsorbent, type of eluent solvents and adsorption capacity were investigated and optimized. Under the optimum extraction conditions, the enrichment factors of fenuron, monuron, diuron, linuron and pencycuron were 90, 128, 148, 204 and 295 times, respectively. A good linearity was obtained in different concentration levels of target analytes with the determination coefficients (r²) larger than 0.993. In addition, the limits of detection varied from 0.05 to 0.36 ng/mL and the recoveries of the analytes were in the range of 85.19-99.13 %. The results demonstrated that the proposed miniaturized solid-phase extraction procedure coupled with Zr-MOF could become an effective tool to analyze phenylurea herbicides and would have the vast application prospect for the extraction of pesticide residue and more organic pollutants from Hawthorn, Dendrobii Officinalis Caulis and Salviae Miltiorrhizae Radix et Rhizoma samples.

A magnetic graphene-like MoS2 nanocomposite for simultaneous preconcentration of multi-residue herbicides prior to UHPLC with ion trap mass spectrometric detection

A magnetic graphene-like molybdenum disulfide nanocomposite was prepared by liquid-phase exfoliation and hydrothermal synthesis. The morphology, structure, and magnetic behavior of the nanocomposite were characterized by X-ray diffraction, FTIR spectroscopy, thermogravimetric analysis, vibrating sample magnetometry, scanning electron microscopy and transmission electron microscopy. The nanocomposite was employed as a sorbent for magnetic solid-phase extraction (MSPE) of eight triazine and ten sulfonylurea herbicides from environmental water and corn samples. Specifically, this was studied with cyanazine, simetryn, atrazine, methoprotryne, ametryn, prometryn, terbutryn, dipropetryn, metsulfuron-methyl, sulfometuron-methyl, amidosulfuron, rimsulfuron, nicosulfuron, bensulfuron-methyl, halosulfuron-methyl, pyrazosulfuron-ethyl, chlorimuron-ethyl, and cyclosulfamuron. The parameters affecting extraction efficiency (sorbent amount, pH value of the sample, extraction and elution conditions) were studied and optimized. Following MSPE, the multi-residue herbicides were quantified by ultra-high performance liquid chromatography combined with ion trap mass spectrometry and electrospray ionization. The limits of detection range between 20 and 170 ng·L^-1. The extraction recoveries of eighteen herbicides from corn samples were in the range between of 64.7% and 103.1%, with RSDs of <17.6%. Graphical abstract Schematic presentation of magnetic graphene-like MoS₂ nanocomposite as an absorbent for simultaneous preconcentration of eight triazine and ten sulfonylurea herbicides in corn and water prior to ultra-high performance liquid chromatography (UHPLC) with ion trap mass spectrometry detection.

Antibiotics may increase triazine herbicide exposure risk via disturbing gut microbiota

BACKGROUND

Antibiotics are commonly used worldwide, and pesticide is a kind of xenobiotic to which humans are frequently exposed. The interactive impact of antibiotics on pesticides has rarely been studied. We aim to investigate the effects of antibiotics on the pesticide exposure risk and whether gut microbiota altered by antibiotics has an influence on pesticide bioavailability. Furthermore, we explored the mechanisms of gut microbiota affecting the fate of pesticides in the host.

RESULTS

The oral bioavailability of triazine herbicides significantly increased in the rats treated with ampicillin or antibiotic cocktails. The antibiotic-altered gut microbiota directly influenced the increased pesticide bioavailability through downregulating hepatic metabolic enzyme gene expression and upregulating intestinal absorption-related proteins.

CONCLUSIONS

Antibiotics could increase the pesticide bioavailability and thereby may increase the pesticide exposure risk. The antibiotic-altered gut microbiota that could alter the hepatic metabolic enzyme gene expression and intestinal absorption-related proteome was a critical cause of the increased bioavailability. This study revealed an undiscovered potential health impact of antibiotics and reminded people to consider the co-exposed xenobiotics when taking antibiotics.

Residue investigation of some phenylureas and tebuthiuron herbicides in vegetables by ultra-performance liquid chromatography coupled with integrated selective accelerated solvent extraction-clean up in situ

BACKGROUND

Some trace amounts of urea herbicide residues can be transferred to humans via the food chain, thereby being potentially harmful to human health. The development of a robust analytical methodology for effective sample preparation and simultaneous determination of herbicide residues in vegetable samples is required for achieving food safety.

RESULTS

The diuron-molecularly imprinted polymers (MIPs) synthesized have excellent affinity and high selectivity to phenylureas (monolinuron, isoproturon, diuron and linuron) and tebuthiuron. A novel automated procedure with better selectivity for vegetable sample treatment was developed by integrated matrix solid-phase dispersion-accelerated solvent extraction clean-up in situ. Five herbicides can be baseline separated with runtime down to 5 min by ultra-performance liquid chromatography, and good linearity was obtained with a correlation coefficient (r) of 0.9999. The limit of quantification of the method was in the range of 0.8-2.3 µg kg^-1 . Diuron residue in cherry tomato sample was found to be 40 µg kg^-1 .

CONCLUSION

The developed method has satisfactory selectivity, good linearity, high sensitivity and accuracy as well as speediness, and can ensure rapid selective extraction and sensitive multi-residue analysis at low microgram per kilogram levels of the herbicides in vegetable food. © 2018 Society of Chemical Industry.

Substructure-activity relationship studies on antibody recognition for phenylurea compounds using competitive immunoassay and computational chemistry

Based on the structural features of fluometuron, an immunizing hapten was synthesized and conjugated to bovine serum albumin as an immunogen to prepare a polyclonal antibody. However, the resultant antibody indicated cross-reactivity with 6 structurally similar phenylurea herbicides, with binding activities (expressed by IC₅₀ values) ranging from 1.67 µg/L to 42.71 µg/L. All 6 phenylurea herbicides contain a common moiety and three different substitutes. To understand how these three different chemical groups affect the antibody-phenylurea recognition activity, quantum chemistry, using density function theory (DFT) at the B3LYP/6-311++ G(d,p) level of theory, was employed to optimize all phenylurea structures, followed by determination of the 3D conformations of these molecules, pharmacophore analysis, and molecular electrostatic potential (ESP) analysis. The molecular modeling results confirmed that the geometry configuration, pharmacophore features and electron distribution in the substituents were related to the antibody binding activity. Spearman correlation analysis further elucidated that the geometrical and electrostatic properties on the van der Waals (vdW) surface of the substituents played a critical role in the antibody-phenylurea recognition process.

Selective detection of glufosinate using CuInS2 quantum dots as a fluorescence probe

This work designed a fluorescence “turn-off-on” probe to detect glufosinate.