Accuracy of a method based on atomic absorption spectrometry to determine inorganic arsenic in food : Outcome of the collaborative trial IMEP-41

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Accuracy of a method based on atomic absorption spectrometry to determine inorganic arsenic in food: outcome of the collaborative trial IMEP-41

A collaborative trial was conducted to determine the performance characteristics of an analytical method for the quantification of inorganic arsenic (iAs) in food. The method is based on (i) solubilisation of the protein matrix with concentrated hydrochloric acid to denature proteins and allow the release of all arsenic species into solution, and (ii) subsequent extraction of the inorganic arsenic present in the acid medium using chloroform followed by back-extraction to acidic medium. The final detection and quantification is done by flow injection hydride generation atomic absorption spectrometry (FI-HG-AAS). The seven test items used in this exercise were reference materials covering a broad range of matrices: mussels, cabbage, seaweed (hijiki), fish protein, rice, wheat, mushrooms, with concentrations ranging from 0.074 to 7.55 mg kg(-1). The relative standard deviation for repeatability (RSDr) ranged from 4.1 to 10.3%, while the relative standard deviation for reproducibility (RSDR) ranged from 6.1 to 22.8%. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Novel metrics and experimentation insights for dynamic frequency selection in wireless LANs

The rapidly increasing popularity of IEEE 802.11 WLANs has created unprecedented levels of congestion in the unlicensed frequency bands, especially in densely populated urban areas. Performance experienced by end-users in such deployments is significantly degraded due to contention and interference among adjacent cells. In this paper, we develop novel metrics and insights that we use for dynamic frequency selection, incorporating the various features that affect interference. The proposed scheme features a novel client feedback mechanism, which enables nodes of the cell, as well as nodes belonging to different cells, to contribute to interference measurements. Furthermore, we incorporate a traffic monitoring scheme that makes the system aware of prevailing traffic conditions. We design a distributed protocol, through which messages containing the information above are passed by the stations to the access-points, where the frequency selection is performed in a dynamic form. The proposed algorithm is implemented in the Mad-WiFi open source driver and is validated through extensive testbed experiments in both an indoor RF-Isolated environment, as well as in a interference-rich, large-scale wireless testbed. Results obtained under a wide range of settings, indicate that our algorithm improves total network throughput, up to a factor of 7.5, compared to state-of-the-art static approaches. © 2011 ACM