2026-10-12T14:12:52Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33989633 2026-09-24T19:14:24Z category_7 category_12 category_734 category_106 category_133 portal_63 item_type_3 month_year_09_2026

Robust Chemometric Machine Learning Framework for Screening-Stage Triage in Antidoping Urine Analysis Hana Park (2827127) Junghyun Son (2297224) Medicine Cell Biology Biological Sciences not elsewhere classified Science Policy Plant Biology severe class imbalance response attribution patterns based ensemble optimized area response values applied post hoc adjusted analysis showed validated chemometric machine learning framework  fixed implementation flagged prioritizing suspicious samples conventional manual review learning pipelines fixed external 920 machine review time positive samples expert review 684 samples validation hold throughput data stage triage set (< related pattern providing scalable n </ ms chromatograms ms analytes level peak interpretable support independent association feature coverage determined solely derived analyte consistent sport confirmatory follow benchmarking 10 analytically complex 56 reference 137 ), Initial testing procedure (ITP) screening in antidoping requires rapid interpretation of high-throughput data under analytically complex and operationally demanding decision conditions. 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. SHAP analysis showed chemically meaningful analyte-response attribution patterns that were not determined solely by feature coverage. Covariate-adjusted analysis showed no independent association with sex, while exploratory sport analysis revealed no consistent sport-related pattern. This framework reduced estimated screening-review time by >99.8% relative to conventional manual review, providing scalable, interpretable support for prioritizing suspicious samples for expert review and confirmatory follow-up in routine regulatory antidoping laboratories. 2026-09-24T00:00:00Z Dataset Dataset 10.1021/acs.analchem.6c02186.s005 https://figshare.com/articles/dataset/Robust_Chemometric_Machine_Learning_Framework_for_Screening-Stage_Triage_in_Antidoping_Urine_Analysis/33989633 CC BY-NC 4.0