Optimised uncertainty in food analysis: application and comparison between four contrasting ‘analyte–commodity’ combinations

Abstract

The optimised uncertainty (OU) methodology is applied across a range of analyte–commodity combinations. The commodities and respective analytes under investigation were chosen to encompass a range of input factors: measurement costs (sampling and analytical), sampling uncertainties, analytical uncertainties and potential consequence costs which may be incurred as a result of misclassification. Two types of misclassification are identified—false compliance and false non-compliance. These terms can be used across a wide range of foodstuffs that have regulations requiring either minimum compositional requirements, maximum contaminant allowances or compositional specifications. The latter refers to foodstuffs with regulations that state an allowable tolerance around the compositional specification, i.e. the upper specification limit (USL) and the lower specification limit (LSL). The traditional OU methodology has been adapted so that it is applicable in these cases and has been successfully applied in practice. The Newton–Raphson method has been used to determine the optimal uncertainty value for the two case studies in which analyte concentration is assessed against a ‘single threshold’ regulatory requirement. This numerical method was shown to give a value of the optimal uncertainty that is practically identical to that given by the previously used method of visual inspection. The expectation of financial loss was reduced by an average of 65% over the four commodities by the application of the OU methodology, showing the benefit of the method.