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Opt."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>Hyperspectral imaging captures rich spectral information for polymer identification, but its dimensionality is a barrier to practical optical sensing. We propose a SHapley Additive exPlanations (SHAP)-based band-selection framework that identifies a minimal, physically interpretable set of wavelengths for compact multispectral sensors built from discrete bandpass filters. From 146 bands, 10 wavelengths were reproducibly identified across five image-wise cross-validation folds derived from 10 independently acquired scenes (173,355 labeled pixels; PET, HDPE, PP, and PS), at 88% mean pairwise selection overlap against \u223c30% for sequential forward selection. The selected bands cluster in three C\u2013H absorption windows near 1148\u20131218, 1368, and 1648\u20131683\u00a0nm. Random forest and a 1D-CNN reached 99.55\u00b10.30% and 99.70\u00b10.14% on the 10-band subset, retaining 99.9% of full-spectrum accuracy; because the 60 fragments, cut from approximately 20 parent objects, were re-imaged across scenes, these estimates reflect generalization to new acquisitions rather than to unseen specimens. Performing selection on spectrally aggregated channels yields a directly realizable specification of five 20\u00a0nm channels at 1146, 1205, 1365, 1665, and 1681\u00a0nm, achieving 99.24\u00b10.32% with perfectly reproducible selection across folds, and grouping pixel predictions into objects by majority vote gives 100% object-level accuracy. Accuracy remains above 99% under 5% per-band Gaussian noise. These results indicate a pathway to hardware-efficient multispectral NIR sensing for plastic sorting, bound by the four polymer classes examined, the reused-sample protocol, laboratory illumination, and the single sensor platform.<\/jats:p>","DOI":"10.1364\/ao.606168","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T15:00:18Z","timestamp":1788879618000},"page":"9805","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SHAP-guided dimensionality reduction for hyperspectral imaging in plastic waste sorting"],"prefix":"10.1364","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1166-2619","authenticated-orcid":true,"given":"Mukesh Kumar","family":"Singh","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01ft5vz71","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul","family":"Thakur","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01ft5vz71","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ao-65-28-9805-R1","doi-asserted-by":"publisher","first-page":"e1700782","DOI":"10.1126\/sciadv.1700782","type":"journal-article","volume":"3","author":"Geyer","year":"2017","journal-title":"Sci. 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