metals (2)

12740263497?profile=RESIZE_400xIn this paper (open access) the authors developed and validated a novel sample introduction mechanism (a new configuration of Solution Glow Cathode Discharge, SGCD) to enable heated and diluted honey to be directly analysed by Optical Emission Spectroscopy (OES).  This provided a relatively low-cost and bespoke platform for the routine testing of trace metals in honey.

They measured the concentrations of five metals – Na, K, Rb, Mg and Ca – in a reference set of authentic honeys and honeys adulterated with syrups.  The paper concentrates more on the analytical technique validation than the reference database and so it is unclear how all the reference samples were sourced and prepared, and two reference results were removed from the dataset as unexplained outliers.  Nonetheless, the authors present multivariate statistics showing that the metal profile can be used as an indicator of adulteration, with syrup-adulterated honey having higher Na content and “natural” honeys having higher K, Mg and Ca content.

This could form the basis of another classification technique which, whilst being a long way from definitive, could add to that analytical arsenal in a weight-of-evidence approach to determining honey authenticity.

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Measuring the profile of trace metals in food is one approach to discriminating its geographic origin.  Analytical tools for trace-metal measurement tend to be laboratory-based with expensive capital equipment.

There has been recent research to develop point-of-use sensors for metal ions using specific chemical binders (often based on the chemistry of human olefaction) with fluorescent markers.  This paper (purchase requires) takes the work a significant step forward.  Their sensor is based upon the principle that different metal ions induce different degrees of aggregation in perylene diimide derivative based supramolecular nanoaggregates.  This enabled the construction of a multi-analyte sensor which they report as having ease of preparation, rapid response, and high sensitivity originating from large specific surface areas.  The authors report that they used the sensor to build a successful classification model of geographic origin for both drinking water and apples.

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