Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues

W J de Boer, H van der Voet, W G de Ruig, J A van Rhijn, K M Cooper, D G Kennedy, R K P Patel, S Porter, T Reuvers, V Marcos, P Munoz, J Bosch, P Rodriguez, J M Grases

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Abstract

GC-MS data on veterinary drug residues in bovine urine are used for controlling the illegal practice of fattening cattle. According to current detection criteria, peak patterns of preferably four ions should agree within 10 or 20% from a corresponding standard pattern. These criteria are rigid, rather arbitrary and do not match daily practice. A new model, based on multivariate modeling of log peak abundance ratios, provides a theoretical basis for the identification of analytes and optimizes the balance between the avoidance of false positives and false negatives. The performance of the model is demonstrated on data provided by five laboratories, each supplying GC-MS measurements on the detection of clenbuterol, dienestrol and 19 beta-nortestosterone in urine. The proposed model shows a better performance than confirmation by using the current criteria and provides a statistical basis for inspection criteria in terms of error probabilities.

Original languageEnglish
Pages (from-to)109-114
Number of pages6
JournalAnalyst
Volume124
Issue number2
Publication statusPublished - Feb 1999

ASJC Scopus subject areas

  • Analytical Chemistry

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