Defining food sampling strategy for chemical risk assessment

Abstract : Collection of accurate and reliable data is a prerequisite for informed risk assessment and risk management. For chemical contaminants in food, contamination assessments enable consumer protection and exposure assessments. And yet, the accuracy of a contamination assessment depends on both chemical analysis and sampling plan performance. A sampling plan is always used when the contamination level of a food lot is evaluated, due to the fact that the whole lot can not be analysed, but only samples, which are drawn from the lot. An efficient sampling plan enables to take samples from a food lot, following a given protocol, with a relatively low risk of misestimating the true mean concentration of the food lot after analysis of the food samples. Sampling plan performance testing is achieved thanks to mathematical validation methods. The best fit sampling plan is the one that gives the best compromise between the lowering of the risk of misestimating the true lot concentration and the practical feasibility (not too much time consuming nor money consuming). This chapter presents two sampling plan validation strategies: a parametric method developed by Whitaker and co-workers from 1972 and a non parametric method set up by Schatzki et al. (Schatzki, 1995; Campbell et al. 2003). To our knowledge, these are the only two methods sufficiently evolved for having been applied to real situation cases for food sampling validation. These statistical methods are first explained from a theoretical point of view. Then, each one is illustrated by a practical application to a sampling plan validation for a specific chemical risk in a food commodity, thanks to workable contamination data gathered in the literature. According to us, in its general mathematical principle, the non parametric method is more appropriate to cases with contaminants distributed heterogeneously in a food lot. However, due to its ease of use, the parametric method applies best to cases where the distribution of the contaminant is homogeneous. A food contaminant is homogeneously distributed in a contaminated food lot when the contamination incidence rate for individual food items taken from the contaminated lot is high, and when the concentration levels in each food item are rather alike. Otherwise, when the contamination incidence rate is low, and when the concentrations differ greatly in each food item, this means that the contaminant is heterogeneously distributed within the food lot. For these reasons, the first sampling plan validation technique (parametric method) is applied, in this chapter, to phycotoxin contamination in shellfish lots at the cultivation zone, as it is considered as being a homogeneously distributed contaminant case. For the heterogeneously distributed contaminant case, mycotoxin contamination data for pistachios at retail stage are exploited in order to put into practice the non-parametric sampling plan validation method. Both phycotoxins and mycotoxins are natural toxins that are unsafe for human. Limits of contaminations are set by national and international safety agencies, but sampling strategies have a great influence on the detected results in food lots. The chapter will show that an optimal sampling strategy can be obtained in each of the two cases, but that they require different mathematical approaches in order to obtain reliable Operating Characteristics (OC) curves showing: - the consumer risk (risk of accepting lots at a true concentration above the contaminant's concentration threshold); - the producer risk (risk of rejecting lots at a true concentration under the contaminant's concentration threshold).
Type de document :
Chapitre d'ouvrage
Prof Zhang Zhiyong. Risk Assessment and Management, Academy Publish, pp.230-251, 2012, 978-0-9835850-0-8
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Soumis le : jeudi 13 décembre 2012 - 11:19:14
Dernière modification le : jeudi 1 mars 2018 - 14:22:38
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Nathalie Wesolek, Alain-Claude Roudot. Defining food sampling strategy for chemical risk assessment. Prof Zhang Zhiyong. Risk Assessment and Management, Academy Publish, pp.230-251, 2012, 978-0-9835850-0-8. 〈hal-00764605〉



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