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2.
Environ Pollut ; : 115121, 2020 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-33139099

RESUMO

Polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/Fs), as the secondary environmental pollutants of the widely used brominated flame retardants (BFRs), possess the similar physicochemical and toxic properties as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). However, studies on human body exposure to them are extremely limited. In this study, forty human milk samples collected in Shanghai were measured for 13 PBDD/F congeners using gas chromatography-high resolution mass spectrometry (GC-HRMS), to investigate their exposure level and characteristics, potential source and corresponding health risks to breastfed infants. The results showed no PBDDs but three PBDF congeners including 2,3,7,8-TBDF, 1,2,3,4,6,7,8-HpBDF and OBDF (mean concentration (detection rates) are 3.2 pg/g (72.5%), 9.5 pg/g (100%) and 28 pg/g (67.5%), respectively) were detected. The average toxic equivalent quantity (TEQ, 0.42 pg/g lw) presented the highest concentration level compared to other regions reported. The contribution of PBDFs to the total TEQ of PBDD/Fs and PCDD/Fs is 6.8%. The correlation between PBDD/Fs and age or dietary habits was not observed, which normally existed in their chlorinated analogues-PCDD/Fs. Significant correlations were observed between PBDFs and highly brominated polybrominated diphenyl ethers (PBDEs) (especially for BDE 183 and BDE 209). The correlation between PCDD/Fs and PBDFs was not observed except 2,3,7,8-TBDF. The high PBDFs exposure in Shanghai may originate from the emission of PBDEs and/or non-PBDE BFRs in environment, according to the consistency of the environmental data previously reported. The average estimated dietary intakes (EDI) for breastfed infants is 2.0 pg TEQ/kg·bw/day (0.13-13 pg TEQ/kg·bw/day), within the range of the tolerable daily intake (TDI) for TCDD (1-4 pg TEQ/kg·bw/day) suggested by the World Health Organization (WHO). However, given the high toxicity of PBDD/Fs, the potential health risks of these pollutants for breastfed infants should be of concern.

3.
Food Chem ; 320: 126576, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32200175

RESUMO

A rapid and high-throughput method using both GC-MS/MS and UPLC-Q-Orbitrap systems was applied for pesticide multi-residues analysis in food samples. Strategies based on QuEChERs extraction, intelligent data mining tools with in-house/online database, and in-silico fragment prediction system were introduced to screen and identify target/untargeted features. Full-scan combined with data-independent-acquisition modes was evaluated in real sample in an attempt to improve and facilitate the pesticide screening process, of which the results showed that FS-vDIA provided equal detection rate (100%) and far less false positive results than FS-AIF did. The proposed methodology was evaluated in analysis of pesticide multi-residues in several proficiency test samples provided by EURL, and exhibited a high detection rate (>90%) of various pesticide residues with satisfactory recoveries (70-130%) without reporting false positive results. The method was also applied in China's national surveys from 2016 to 2019, and results showed its high performance in pesticide analysis in different food matrices.


Assuntos
Análise de Alimentos/métodos , Contaminação de Alimentos/análise , Resíduos de Praguicidas/análise , Cromatografia Gasosa/métodos , Cromatografia Líquida de Alta Pressão/métodos , Simulação por Computador , Frutas/química , Espectrometria de Massas em Tandem/métodos , Verduras/química
4.
Chemosphere ; 271: 129447, 2020 Dec 27.
Artigo em Inglês | MEDLINE | ID: mdl-33476874

RESUMO

Computational QSAR models have gradually been preferred for retention time prediction in data mining of emerging environmental contaminants using liquid chromatography coupled with mass spectrometry. Generally, the model performance relies on the components such as machine learning algorithms, chemical features, and example data. In this study, we evaluated the performances of four algorithms on three feature sets, using 321 and 77 pesticides as the training and validation sets, respectively. The results were varied with different combinations of algorithms on distinct feature sets. Two strategies including enhancing the complexity of chemical features and enlarging the size of the training set were proved to improve the results. XGBoost, Random Forest, and lightGBM algorithms exhibited the best results when built on a large-scale chemical descriptors, while the Keras algorithm preferred fingerprints. These four models have comparable prediction accuracies that at least 90% of pesticides in validation set can be successfully predicted with ΔRT <1.0 min. Meanwhile, a blended prediction strategy using average results from four models presented a better result than any single model. This strategy was used for assisting identification of pesticides and pesticide transformation products in 120 strawberry samples from a national survey of food contamination. Twenty pesticides and twelve pesticide transformation products were tentatively identified, where all pesticides and two pesticide transformation products (bifenazate diazene and spirotetramat-enol) were confirmed by standard materials. The outcome of this study suggested that retention time prediction is a valuable approach in compound identification when integrated with in silico MS2 spectra and other MS identification strategies.

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