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Risk-Off Method: Improving Data Quality Generated by Chemical Risk Analysis of Milk

Risk-Off Method: Improving Data Quality Generated by Chemical Risk Analysis of Milk

Walter Coelho Pereira de Magalhães Junior, Marcelo Bonnet, Leandro Diamantino Feijó, Marilde Terezinha Prado Santos
Copyright: © 2013 |Pages: 24
ISBN13: 9781466638860|ISBN10: 1466638869|EISBN13: 9781466638877
DOI: 10.4018/978-1-4666-3886-0.ch020
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MLA

Junior, Walter Coelho Pereira de Magalhães, et al. "Risk-Off Method: Improving Data Quality Generated by Chemical Risk Analysis of Milk." Small and Medium Enterprises: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 353-376. https://doi.org/10.4018/978-1-4666-3886-0.ch020

APA

Junior, W. C., Bonnet, M., Feijó, L. D., & Santos, M. T. (2013). Risk-Off Method: Improving Data Quality Generated by Chemical Risk Analysis of Milk. In I. Management Association (Ed.), Small and Medium Enterprises: Concepts, Methodologies, Tools, and Applications (pp. 353-376). IGI Global. https://doi.org/10.4018/978-1-4666-3886-0.ch020

Chicago

Junior, Walter Coelho Pereira de Magalhães, et al. "Risk-Off Method: Improving Data Quality Generated by Chemical Risk Analysis of Milk." In Small and Medium Enterprises: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 353-376. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3886-0.ch020

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Abstract

Here the Risk-Off Method is presented as a contribution to improve the quality of data and information using milk chemical safety as a model, as overseen by the National Plan for Control of Residues and Contaminants (PNCRC) of the Brazilian Ministry of Agriculture, Livestock and Supply (MAPA). In particular, Small and Medium Enterprises (SMEs), which notably lack internal expertise, could benefit from the Risk-Off method, given that SMEs worldwide contribute significant amounts of food to meet global needs. This study develops an innovative tool to help countries provide robust and transparent chemical safety guarantees for their food products. Creating a flexible base platform to appropriately pre-classify results generated by laboratory testing of food samples, the method pre-processes data undergoing the process of Knowledge Discovery in Databases – KDD, producing systemic intelligence deriving from effective, proactive assessment and management of chemical safety risks in foods, a complex issue of increasingly global concern.

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