Arsenic contamination in drinking water and foods is a prevalent concern across the world. Routine testing of inorganic arsenic ensures food safety but requires a cost effective, rapid high throughput, and simple detection method. The objective of this work is to develop a green method using X-Ray fluorescence spectroscopy (XRF) to analyze inorganic arsenic (iAs) in food and their interaction with emerging food contaminants: microplastics and titanium dioxide nanoparticles. XRF measures the secondary X-ray that is characteristic of each element emitted by the sample.
In a prior study, we developed an approach that combines the Gutzeit method and elemental analysis using XRF for arsenic detection in food. This approach is based on a commercial mercury bromide strip to capture arsine gas. Concerning the high toxicity of mercury bromide, we explored the feasibility of using a greener chemical, silver nitrate, to replace mercury bromide. This would benefit the safety of the operating personnel and reduce chemical hazard impact on the environment. In addition, organic acids and zinc nanoparticles were explored for iAs detection. Optimization of various reagents was done to maximize the efficacy of iAs capture and detection. The result demonstrated the greener method has a lower quantification (3.40 µg/L) compared to the original method based on mercury bromide (16.2 µg/L) due to less elemental interferences in the XRF spectrum. The standard curves of water and apple juice were compared, no significant difference was found, suggesting matrix interference is minimal. The spiked apple juice with 0 to 133 µg/L iAs had a good recovery ranging from 85-99% with an average relative standard deviation below 20%, indicating decent reproducibility.
Other than iAs detection, we also explored the XRF to study the iAs and their interaction between microplastics and titanium dioxide nanoparticles, which are considered emerging contaminants of public concerns that may serve as vectors for pollutants and potentially enhances toxicity effects. We developed a screening method to quantify the adsorption under different conditions. The result showed iAs adsorption is highly dependent of particle size and surface morphology. In conclusion, this study demonstrates the feasibility and great potential of XRF quantification of inorganic arsenic in food matrices in a cost-effective and reliable manner and the capability of rapidly quantifying the interaction with emerging contaminants such as microplastics and titanium dioxide nanoparticles.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2236 |
| Date | 28 June 2022 |
| Creators | Lin, Helen |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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