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

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