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Listen to this siteWednesday 6 October 2004
This research project aims to test a new Optimised Uncertainty (OU) method.
Study Duration : October 1999 to September 2002
Contractor : University of Sussex
When anyone measures the concentration of any constituent of food they will not report the 'true' value, but an estimate. It is important therefore to know the range about the measured value of concentration that contains the true value; this is called the uncertainty of the measurement.
To get a reliable value of the uncertainty, consideration must be given to the contribution made to the uncertainty by both the primary sampling of the food, as well from its chemical analysis. Once this reliable value of uncertainty is known, the concentration value can be compared with statutory thresholds, to judge the safety of the food.
There will always be a possibility of misclassifying the food, due to this uncertainty. There may a 'false positive' classification, where the measured concentration is above some threshold, but the true value is below. Alternatively, there may be a 'false negative', where the measured concentration is below the threshold, but the true value is above. In the food sector both of these errors may have financial consequences. The false positive will cause the rejection of a batch of food and a financial loss equal to its commercial value. The false negative may lead to food being sold with contamination present at a concentration above a statutory limit. This may, if detected, lead to potential health implications or financial consequences from either litigation or from loss of corporate reputation of a manufacturer or retailer. There is therefore a balance to be struck between the cost of the sampling and analysis, and of the potential financial outcomes of misclassifying the food.
The aim of this study was to test a new Optimised Uncertainty (OU) method for achieving this balance.
The broad approach in the application of the OU method, in all cases, was to specify an experimental design for primary sampling and chemical analysis for the selected food/analyte combination, that allows the uncertainty to be estimated by the method of Ramsey (1998).
This involved:
The general approach to the estimation of uncertainty from physical sample preparation involved specifying an experimental design which allowed for the estimation of uncertainty from physical sample preparation (including systematic error estimation from this source) in addition to primary sampling and chemical analysis. Calculation of measurement uncertainty included individual estimates of random error from primary sampling and both random and systematic errors from sample preparation and chemical analysis using robust analysis of variance. The general approach for the adaptation of OU method for optimisation of sample preparation processes involved the construction of a loss function that allowed for the optimisation of measurement uncertainty (including physical sample preparation) and optimal apportionment of expenditure between primary sampling, sample preparation and chemical analysis.
The first application of the OU method to aflatoxins in pistachio nuts demonstrated the feasibility of this new approach. The detailed recommendations from this first application, suggested that the measurement uncertainty could usefully be reduced by 31% (Ramsey et al. , 2001), which could be achieved, for example, by increasing the expenditure on sampling by a factor of four. This would be predicted to lower the overall expectation of loss of each batch considerably. Four further food/analyte combinations were selected with a range of contrasting properties in terms of the costs of sampling and analysis (Lyn et al. , 2002; Lyn, 2003), and demonstrated the wider applicability of the OU method across a wide range of situations. All four showed a need to reduce the uncertainty by around 20-40%, but the differing natures of the materials involved meant that different approaches were best employed in order to achieve this.
When a reduction in uncertainty is indicated, an increased amount of money should be spent on the measurement process. If either sampling or analysis contributes the most uncertainty, the extra money should be preferentially given to that process. Reductions in the money spent on each chemical analysis were indicated for 'meat in sausages' and for 'added water in milk', as this source does not contribute substantially to the overall uncertainty. The optimal amount of money to be spent on sampling varied depending on the type of food/constituent, as did that for chemical analysis.
Comparing across the food types studied, 'fat in spreadable fats' had the potential to yield the highest financial loss, even when the measurements were optimised. A further study of four metals in infant food (lead cadmium, zinc and copper), also showed how the optimisation can be applied to food analysis for several constituents in routine surveillance studies. The sample preparation process was estimated to contribute up to 20% to the total uncertainty of the estimated pesticide residue levels. Some analytes are more sensitive to the method of physical preparation than others, with the apparent amount of one pesticide (chlorothalonil) being halved as a consequence.
The overall conclusion was that not only did the OU method work well on foods, but it also provided very useful information not previously available. The contribution of sample preparation to uncertainty can be great and the adaptation of the OU method for this source of uncertainty can potentially lead to more comprehensive recommendations. This tool has the potential to allow the most appropriate sampling and analysis to be applied where it is most needed. It will result in more reliable and cost-effective analysis of foods, which is to the benefit of both the Agency and the consumer.
Data generated from this project are only initial findings using a new method. Several areas were identified where the OU method could be improved, so that it could produce more robust recommendations. Project E01055 has subsequently been procured to further challenge the findings of this project.
Ramsey, M.H., Lyn, J.A. and Wood, R. (2001). Optimised uncertainty at minimum overall cost to achieve fitness-for-purpose in food analysis. Analyst, 126, 1777-1783.
Lyn, J.A., Ramsey, M.H. and Wood, R. (2002) Optimised uncertainty in food analysis: application and comparison between four contrasting �analyte-commodity� combinations, Analyst, 127, 1252-1260.
Lyn, J.A., Ramsey, M.H. and Wood, R. (2003) Multi-analyte optimisation of infant food analysis, Analyst, 128, 379-388.
Lyn, J.A., Ramsey, M.H., Fussell, R. J. and Wood, R. (2003) Measurement uncertainty from physical sample preparation: estimation including systematic error, Analyst, 128, 1391-1398.
Lyn, J.A. (2003). Optimising uncertainty from sampling and analysis of foods and environmental samples. D.Phil. Thesis. University of Sussex.
The final report is available from the FSA Library and Information centre.
To obtain a copy, please contact the Enquiry Desk, Dr Elsie Widdowson Library and Information Services, Food Standards Agency (tel: 020 7276 8181/8182 or email:
library&info@foodstandards.gsi.gov.uk
Contact
: For any enquiries concerning this research project, please contact the relevant Programme contact or email
science@foodstandards.gsi.gov.uk
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