Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 343235
Title Development of databases for use in validation studies of probabilistic models of dietary exposure to food chemicals and nutrients
Author(s) Leclerq, C.; Arcella, D.; Armentia, A.; Boon, P.E.; Kruizinga, A.G.; Gilsenan, M.B.; Thompson, R.L.
Source Food Additives and Contaminants 20 (2003)suppl. 1. - ISSN 0265-203X - p. S27 - S35.
Department(s) RIKILT - Business Unit Safety & Health
Publication type Refereed Article in a scientific journal
Publication year 2003
Keyword(s) official inspection - additives - budget - japan
Abstract The data currently available in the European Union in terms of food consumption and of food chemical and nutrient concentration data present many limitations when used for estimating intake. The most refined techniques currently available were used within the European Union FP5 Monte Carlo project to estimate, as accurately as possible, the intake of food additives, pesticide residues and nutrients. Databases of 'true' intakes of food additives (based on brand level food consumption records and additive concentration data), pesticide residues (based on duplicate diet studies) and nutrients (based on biomarker studies) have thus been generated. These kind of estimates are rarely repeatable because the databases generated and used to calculate them require an extraordinary expenditure of time and resources. The databases created served the purpose of estimating as accurately as possible 'true' chemical intakes for assessing the validity of additive, nutrient and pesticide probabilistic models.
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