Optimization and validation of high throughput methods for the determination of 132 organic contaminants in green and roasted coffee using GC-QqQ-MS/MS and LC-QqQ-MS/MS.

Recently some major safety concerns have been raised on organic contaminants in widely consumed plants such as coffee. Hence, this study aimed to develop specifically optimized methods for determining organic contaminants, such as pesticides and polychlorinated biphenyls (PCBs), in coffee using GC-MS/MS and LC-MS/MS. QuEChERS method was used as a base extraction method, and 27 experiments were studied using design of experiments with categorical variables (extraction buffers, cleanup sorbents, and coffee roasting degree) to find the optimum method for each matrix type. The optimum method for green coffee was acetate buffer and chitosan for clean-up, while no-buffer extraction and the PSA + C18 method were ideal for light and dark-roasted coffee. The optimized methods were validated in accordance with SANTE/11312/2021. Furthermore, ten real samples (4 green, and 6 roasted) from the markets were analysed; ortho-phenylphenol was found in all the roasted coffee samples, and carbendazim was found in one green coffee sample.

Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.