Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/43596
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Type: Journal article
Title: A new risk framework for predicting chemical residue(s) - Preliminary research for PCBs and PCDD/Fs in farmed Australian Southern Bluefin Tuna (Thunnus maccoyii)
Author: Phua, S.
Davey, K.
Daughtry, B.
Citation: Chemical Engineering and Processing: process intensification, 2007; 46(5 Sp. Iss):491-496
Part of: Advances in the Application of Chemical Engineering Principles in Food Industry / Professor Xiao Dong Chen and Dr. Md Monwar Hossain (eds.)
Publisher: Elsevier Science SA
Issue Date: 2007
ISSN: 0255-2701
1873-3204
Statement of
Responsibility: 
Samuel T.G. Phua, K.R. (Ken) Davey and Ben J. Daughtry
Abstract: Chemical residues are ubiquitous and found in all foods. In South Australia, the export of Southern Bluefin Tuna (SBT) to premium markets in Japan is economically important. Although PCBs and PCDD/Fs are low at 1.07 pg/g TEQ fresh weight WHO-PCB/PCDD/F compared with 8.0 pg/g permitted [Anon., European Commission, Commission Regulation (EC) no. 199/2006 of February 3, 2006 amending Regulation (EC) no. 466/2001 setting maximum levels for certain contaminants in foodstuffs as regards dioxins and dioxin-like PCBs, Off. J. Eur. Union (2006) L32/34-L32/38], we are researching ways to manage lower concentrations through development of a predictive model for these residues in SBT fillets. The model lies within a new risk framework of five governing principles. These are based on those in Codex Alimentarius [Anon., Codex Alimentarius, Principles and guidelines for the conduct of microbiological risk assessment, CAC/GL 30 (1999) 1-6]. The first principle identifies the chemical residue(s) of interest. The second characterises adverse health effects on humans (if any) through the food chain. The third, quantifies this risk to humans. The fourth characterises quantitatively the certainty in data. The fifth is an experimental validation of the model with independent data. Three types of mathematical models for prediction of residues, a physiologically based pharmacokinetic (PBPK), a quantitative structure and relationship (QSAR), and a differential-rate form are briefly discussed. Our preliminary research suggests a practical model to assist management of low concentrations of residue(s) in SBT can be derived from our defined risk framework of governing principles. © 2006 Elsevier B.V. All rights reserved.
Description: Copyright © 2006 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.cep.2006.06.016
Description (link): http://www.elsevier.com/wps/find/journaldescription.cws_home/504081/description#description
Published version: http://dx.doi.org/10.1016/j.cep.2006.06.016
Appears in Collections:Aurora harvest
Chemical Engineering publications

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