Determination of sulfonylureas in cereal samples with electrophoretic method using ionic liquid with dispersed carbon nanotubes as electrophoretic buffer

Exposure to herbicides mixtures in relation to type 2 diabetes mellitus among Chinese rural population: Results from different statistical models

BACKGROUND

Although it has been reported that herbicides exposure is related to adverse outcomes, available evidence on the associations of quantitatively measured herbicides with type 2 diabetes mellitus (T2DM) and prediabetes is still scant. Furthermore, the effects of herbicides mixtures on T2DM and prediabetes remain unclear among the Chinese rural population.

AIMS

To assess the associations of plasma herbicides with T2DM and prediabetes among the Chinese rural population.

METHODS

A total of 2626 participants were enrolled from the Henan Rural Cohort Study. Plasma herbicides were measured with gas chromatography coupled to triple quadrupole tandem mass spectrometry. Generalized linear regression analysis was employed to assess the associations of a single herbicide with T2DM, prediabetes, as well as indicators of glucose metabolism. In addition, the quantile g-computation and environmental risk score (ERS) structured by adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) were used to estimate the effects of herbicides mixtures on T2DM and prediabetes.

RESULTS

After adjusting for covariates, positive associations of atrazine, ametryn, and oxadiazon with the increased odds of T2DM were obtained. As for prediabetes, each 1-fold increase in ln-transformed oxadiazon was related to 8.4% (95% confidence interval (CI): 1.033, 1.138) higher odds of prediabetes. In addition, several herbicides were significantly related to fasting plasma glucose, fasting insulin, and HOMA2-IR (false discovery rates adjusted P value < 0.05). Furthermore, the quantile g-computation analysis showed that one quartile increase in multiple herbicides was associated with T2DM (OR (odds ratio): 1.099, 95%CI: 1.043, 1.158), and oxadiazon was assigned the largest positive weight, followed by atrazine. In addition, the ERS calculated by the selected herbicides from AENET were found to be associated with T2DM and prediabetes, and the corresponding ORs and 95%CIs were 1.133 (1.108, 1.159) and 1.065 (1.016, 1.116), respectively. The BKMR analysis indicated a positive association between mixtures of herbicides exposure and the risk of T2DM.

CONCLUSIONS

Exposure to mixtures of herbicides was associated with an increased risk of T2DM among Chinese rural population, indicating that the impact of herbicides exposure on diabetes should be paid attention to and measures should be taken to avoid herbicides mixtures exposure.

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.

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.

Residue analysis of orthosulfamuron herbicide in fatty rice using liquid chromatography-tandem mass spectrometry

In the present study, orthosulfamuron residues were extracted from fatty (unpolished) rice and rice straw using a modified QuEChERS method and analyzed using liquid chromatography–tandem mass spectrometry. The matrix-matched calibration was linear over the concentration ranges of 0.01–2.0 mg/kg with determination coefficient (R²) ⩾ 0.997. The recovery rates at two fortification levels (0.1 and 0.5 mg/kg) were satisfactory and ranged between 88.1% and 100.6%, with relative standard deviation (RSD) <8%. The limit of quantitation, 0.03 mg/kg, was lower than the maximum residue limit, 0.05 mg/kg, set by the Ministry of Food and Drug Safety in the Republic of Korea. The developed method was applied successfully to field samples harvested at 116 days and none of the samples were positive for the residue.

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

A rapid and sensitive method was developed for the determination of 50 herbicides in cereal grain by ultra-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS). Using acetonitrile effectively extracted 22 kinds of triazine and other basic herbicides, and using 90:10 v/v acetonitrile-phosphate buffer (pH = 7.5) effectively extracted other 28 herbicides. Chromatographic separation was achieved using gradient elution with acetonitrile-water as a mobile phase for 22 triazine and phenylurea herbicides and with 5mM ammonium acetate aqueous solution containing 0.1% formic acid-acetonitrile as a mobile phase for other 28 herbicides. Using matrix-matched standard calibration curve effectively reduced the indirect matrix effects, ensured accurate quantification for these herbicides. The response was linear over two orders of magnitude with a correlation coefficients (r(2)) higher than 0.992. The limits of quantification for the herbicides varied from 0.2 to 25.6 μg kg(-1). The intra- and inter-day precisions (relative standard deviation, RSD) were 2.2-9.3% and 5.7-17.1%, respectively. The recovery varied from 61.6% to 110% with the RSD of 1.6-11.8%. Analyzing soybean, corn and wheat samples from 17 counties evaluated this method. The developed and validated method has high sensitivity, satisfactory recovery and precision, can ensure the multi-class multi-residue analysis at low μg kg(-1) level for the most herbicides in cereal grain.