Statistical validation for the profiling of heroin by associating simulated postcut samples with the corresponding...

Area of Science:

• Forensic Chemistry
• Chemometrics
• Statistical Analysis

Background:

• Clustering techniques are vital for validating unknown samples in forensic science.
• Optimizing statistical methods requires reliable datasets that mimic real-world sample variations.

Purpose of the Study:

• To demonstrate optimized statistical validation using simulated heroin samples.
• To evaluate data pretreatment and clustering methods for forensic sample analysis.

Main Methods:

• Preparation of simulated heroin samples with controlled analyte concentrations.
• Application of principal component analysis (PCA) and discriminant analysis (DA) for data pretreatment.
• Exploration of hierarchical cluster analysis (HCA) with various linkage methods and distance measures.

Main Results:

• The Ward-Manhattan method demonstrated superior discrimination between unrelated sample links.
• Related samples were clustered closely on dendrograms, indicating successful identification.
• The Ward-Manhattan method achieved similar discriminative performance on 90 unknown case samples.

Conclusions:

• Ward-Manhattan is a robust method for clustering forensic samples, enhancing statistical validation.
• Simulated data provides a valuable tool for optimizing and validating analytical methodologies in forensic chemistry.