Detection of residues using multivariate modelling of low-level GC-MS measurements

The chemometric analysis of low-level analytical data is hampered by the common presence of interfering compounds, by the frequent absence of measurement signals and by a non-constant measurement variability which is related to concentration level in a non-linear way. A model is presented to handle this type of data in the context of the practical problem of multivariate detection from gas chromatography/mass spectrometry (GC-MS) data. The model, based on log ratio modelling, is compared with previous approaches to parts of the problem. The basic idea behind the model is to define for the multivariate detection problem a null hypothesis for the values of log ratio measurements and to estimate variability as a function of total measured intensity. In practice it is often impossible to anticipate all kinds of interference which may occur. Therefore we propose to use expert assessments of the probability that certain expected peak ratios are generated by the analyte rather than by interferences. These expert assessments can then be used to define a proper null hypothesis for the multivariate detection test. The application of the model is illustrated for the detection of the illegal growth promoter clenbuterol in urine by selected ion-monitoring GC-MS