An optimised uncertainty (OU) methodology is described, that balances the uncertainty of measurements on food against the cost of the measurements and the other expenditure that may arise as a consequence of the possible misclassification of the food. Measurement uncertainty from the sources of primary sampling and chemical analysis is estimated using an existing technique, which is based on the taking of duplicated samples and duplicated analyses. The input information required for the OU method is the actual costs of sampling and analysis, and the expected costs that could arise from either the ‘false positive’ or ‘false negative’ classification of batches of food. A loss function is then constructed that calculates the ‘expectation of loss’ which will arise for a given uncertainty of measurement. This function has a minimum value of cost at an optimal value of uncertainty, which can be estimated numerically. Application of this OU method to a case study on the determination of aflatoxin levels in pistachio nuts has demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum value, the costs increased due to increasing probability of expenditure on consequences such as unnecessary rejection of the batch, potential litigation or loss of corporate reputation because of undetected contamination. A second stage of the OU method optimises the division of the expenditure on the measurement between that on sampling and that on analysis. The technique is demonstrated as a useful new approach for judging the fitness-for-purpose of chemical measurements in the food industry. Several areas for further development of the technique are identified. By matching the expenditure on the measurement against that caused by the misclassification of the food, the OU method has the potential to reduce overall expenditure whilst ensuring an appropriate reliability of measurement.