A novel approach for simultaneous determination of E/Z-fluoxastrobins in vegetables and fruits by UHPLC-DAD

Pesticides in honey: bibliographic and bibliometric analysis towards matrix quality for consumption

Abstract Honey is a matrix noted for its wide consumption as a sweetener and its anti-inflammatory, antioxidant, and antimicrobial properties; however, its physicochemical quality can be compromised by the presence of toxicants such as pesticides. This review aims to gather recent information on pesticides in honey from the approach to their detection, understanding, and adverse effects on human health. A bibliographic and bibliometric analysis was carried out in academic databases limited to the last five and thirty years, respectively, comprising the keywords “honey”, “pesticides” and their types of pesticides or the agrochemical compound directly. It was found that there are about 30 pesticides detected in honey, in which organochlorine, organophosphate, and neonicotinoid compounds stood out for their concentrations concerning Maximum Residue Levels (MRL). Their physicochemical alteration was not well explored beyond slight variations in brightness and manganese concentration, and its consumption may have repercussions on human reproductive health. It was also determined that there was limited development on the scientific subject seeing that it is important to explore and investigate more on the issue due to the great impact of honey as a product of high consumption at a global level.

Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson–Kessel noise clustering algorithm

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson–Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson–Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage.

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health.

Simultaneous determination of the herbicide bixlozone and its metabolites in plant and animal samples by liquid chromatography-tandem mass spectrometry

Tracing the herbicide bixlozone and its metabolites in food is necessary to assess their risks to human health. In the study, a rapid and effective analytical method using the quick, easy, cheap, effective, rugged, and safe method for the simultaneous determination of bixlozone and its metabolites (2,4-dichlorobenzoic acid, 3-hydroxy-propanamide-bixlozone, and 5'-hydroxy-bixlozone) in plant and animal samples (tomato, cucumber, apple, wheat flour, meat, milk, and egg) was developed based on ultra high performance liquid chromatography-tandem mass spectrometry. The method was validated based on the linearity (R²  > 0.99), sensitivity (limit of quantification = 0.01 mg/kg), recovery (70.2-115.1%), and precision (intraday 1.2-17.6%, interday 0.3-16.0%). Detection was achieved within 6.0 min. The method is reliable for the determination of four target compounds in all seven matrices. The satisfactory validation criteria and successful application show that the proposed methodology is suitable for the detection of four target compounds in real matrices.

Electrochemical Characterization of Mancozeb Degradation for Wastewater Treatment Using a Sensor Based on Poly (3,4-ethylenedioxythiophene) (PEDOT) Modified with Carbon Nanotubes and Gold Nanoparticles

Mancozeb is a worldwide fungicide used on a large scale in agriculture. The active component and its main metabolite, ethylene thiourea, has been related to health issues. Robust, fast, and reliable methodologies to quantify its presence in water are of great importance for environmental and health reasons. The electrochemical evaluation of mancozeb using a low-cost electrochemical electrode modified with poly (3,4-ethylene dioxythiophene), multi-walled carbon nanotubes, and gold nanoparticles is a novel strategy to provide an in-situ response for water pollution from agriculture. Additionally, the thermal-, electrochemical-, and photo-degradation of mancozeb and the production of ethylene thiourea under controlled conditions were evaluated in this research. The mancozeb solutions were characterized by electrochemical oxidation and ultraviolet-visible spectrophotometry, and the ethylene thiourea concentration was measured using ultra-high-performance liquid chromatography high-resolution mass spectrometry. The degradation study of mancozeb may provide routes for treatment in wastewater treatment plants. Therefore, a low-cost electrochemical electrode was fabricated to detect mancozeb in water with a robust electrochemical response in the linear range as well as a quick response at a reduced volume. Hence, our novel modified electrode provides a potential technique to be used in environmental monitoring for pesticide detection.

Dispersive Solid-Liquid Extraction Coupled with LC-MS/MS for the Determination of Sulfonylurea Herbicides in Strawberries

The monitoring of food quality and safety requires a suitable analytical method with simultaneous detection in order to control pesticide and herbicide residues. In this study, a novel analytical method, referred to as “dispersive solid–liquid extraction”, was applied to monitor seven sulfonylurea herbicides in strawberries. This method was optimized in terms of the amount of C₁₈ and the volume of added water, and it was validated through satisfactory linearities (R² > 0.99), recoveries of 70% to 84% with acceptable precisions, and limits of quantification lower than the maximum residue limits for the seven sulfonylurea herbicides in strawberries. The cleanup efficiency of the dispersive solid–liquid extraction technique was compared to that of the QuEChERS- (“quick, easy, cheap, effective, rugged and safe”) based method with dispersive solid phase extraction. The recoveries of the former were found to be comparable to those involving QuEChERS C₁₈ cleanup (recoveries of 74%–87%). The method was used to determine sulfonylurea herbicide residues in ten strawberry samples. None of the samples had herbicide residues higher than that of limit of quantifications (LOQs) or maximum residue limits (MRLs). The results suggest that the dispersive solid–liquid extraction method combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) is effective for the analysis of sulfonylurea herbicide residues in strawberries.