Robust Chemometric Machine Learning Framework for Screening-Stage Triage in Antidoping Urine Analysis

We developed a robustness-validated chemometric machine-learning frameworkan integrated multivariate modeling workflowfor ITP triage using MassHunter-derived analyte-level peak-area response values for 148 GC–MS/MS analytes from 684 authentic urine casework samples, rather than raw GC–MS/MS chromatograms.

Benchmarking 10,920 machine-learning pipelines, we selected a stacking-based ensemble optimized for rare-positive screening under severe class imbalance. On a fixed external-validation hold-out set (<i>n</i> = 137), the selected Rank 1 model achieved near-perfect recall across repeated evaluations. When applied post hoc to all 684 samples, the fixed implementation flagged all 56 reference-positive samples.