Statistical Validation for the Profiling of Heroin by Associating Simulated Postcut Samples with the Corresponding Precut Sample

Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol–caffeine–dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward–Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward–Manhattan.