Probabilistic VNIR Hyperspectral Screening of Pesticide Residues in Olives and Leaves

Abstract This study advances non-invasive screening of pesticide residues in olives by (i) extending visible–near-infrared (VNIR) hyperspectral imaging (HSI) from fruit to leaves (adaxial/abaxial) as easy-to-sample matrices; (ii) replacing hard-decision with probabilistic models, partial least squares discriminant analysis (PLS-DA) and extreme gradient boosting (XGBoost); and (iii) introducing a tunable reject-option via an indecision band to trade coverage for reliability. An intelligent pixel-selection pipeline (radiometric normalization, HSV masking of glare/defects, and mask-conditioned random sampling of $$\sim 10^4$$ ∼ 10 4 pixels per sample) was used. Ground-truth labels were obtained by GC–MS/MS against EU MRLs. On fruit, deltamethrin and diflufenican reached near-ceiling discrimination (AUC $$\approx 99$$ ≈ 99 –100%, F1 $$\approx 97$$ ≈ 97 –100%); on leaves, oxifluorfen and tebuconazole were best resolved (adaxial/abaxial AUC up to $$\approx 95$$ ≈ 95 –100% with F1 $$\approx 99$$ ≈ 99 %). In contrast, diflufenican shows reduced discriminability on leaf matrices, especially abaxial. Probabilistic outputs enabled an indeterminate label for low-confidence pixels identified via a model-specific indecision band, yielding high accuracy on accepted predictions while keeping the rejected share operationally manageable; an effective score (F1 $$_\text {eff}$$ eff ), defined as a coverage-penalized F1 score, summarizes this coverage–reliability balance. Overall, coupling matrix selection (fruit vs. leaf side) with probabilistic modeling and abstention delivers robust laboratory performance and a realistic pathway to field deployment at cooperative intake, where indeterminate lots can be routed to secondary handling.