{"id":"https://openalex.org/W7204870710","doi":"https://doi.org/10.48550/arxiv.2608.28874","title":"MWIR-4-Plastic: The Identification of Complex End-of-Life Industrial Plastic using Mid-wave Infrared Hyperspectral Imaging and Machine Learning","display_name":"MWIR-4-Plastic: The Identification of Complex End-of-Life Industrial Plastic using Mid-wave Infrared Hyperspectral Imaging and Machine Learning","publication_year":2026,"publication_date":"2026-08-28","ids":{"openalex":"https://openalex.org/W7204870710","doi":"https://doi.org/10.48550/arxiv.2608.28874"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2608.28874","doi":"https://doi.org/10.48550/arxiv.2608.28874","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.28874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2608.28874","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014235134","display_name":"Elias Arbash","orcid":"https://orcid.org/0009-0000-2187-9171","observed_orcids":["https://orcid.org/0009-0000-2187-9171"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arbash, Elias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136833182","display_name":"Andrea de Lima Ribeiro","orcid":"https://orcid.org/0009-0008-5641-6167","observed_orcids":["https://orcid.org/0009-0008-5641-6167"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ribeiro, Andr\u00e9a de Lima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094140106","display_name":"Filipa Sim\u00f5es","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sim\u00f5es, Filipa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039719067","display_name":"Ahmed J. Afifi","orcid":"https://orcid.org/0000-0001-6782-6753","observed_orcids":["https://orcid.org/0000-0001-6782-6753"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Afifi, Ahmed Jamal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081078859","display_name":"Aldino Rizaldy","orcid":"https://orcid.org/0009-0004-4138-7878","observed_orcids":["https://orcid.org/0009-0004-4138-7878"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rizaldy, Aldino","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019952760","display_name":"Yuleika Madriz","orcid":"https://orcid.org/0000-0003-2909-5878","observed_orcids":["https://orcid.org/0000-0003-2909-5878"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madriz, Yuleika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147452558","display_name":"Samuel T. Thiele","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thiele, Samuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012290972","display_name":"Sandra Lorenz","orcid":"https://orcid.org/0000-0001-8464-2331","observed_orcids":["https://orcid.org/0000-0001-8464-2331"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lorenz, Sandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062206243","display_name":"Margret Fuchs","orcid":"https://orcid.org/0000-0001-7210-1132","observed_orcids":["https://orcid.org/0000-0001-7210-1132"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fuchs, Margret","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074919292","display_name":"Pedram Ghamisi","orcid":"https://orcid.org/0000-0003-1203-741X","observed_orcids":["https://orcid.org/0000-0003-1203-741X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghamisi, Pedram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009310543","display_name":"Paul Scheunders","orcid":"https://orcid.org/0000-0003-2447-4772","observed_orcids":["https://orcid.org/0000-0003-2447-4772"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scheunders, Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067490750","display_name":"Richard Gloaguen","orcid":"https://orcid.org/0000-0002-4383-473X","observed_orcids":["https://orcid.org/0000-0002-4383-473X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gloaguen, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.6416000127792358,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.6416000127792358,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.3163999915122986,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10753","display_name":"Microplastics and Plastic Pollution","score":0.010599999688565731,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/plastic-recycling","display_name":"plastic recycling","score":0.6340000033378601},{"id":"https://openalex.org/keywords/hyperspectral-datasets","display_name":"hyperspectral datasets","score":0.7279999852180481},{"id":"https://openalex.org/keywords/spectral-spatial-classification","display_name":"spectral-spatial classification","score":0.6470000147819519},{"id":"https://openalex.org/keywords/majority-voting","display_name":"majority voting","score":0.5379999876022339},{"id":"https://openalex.org/keywords/industrial-inspection","display_name":"industrial inspection","score":0.3449999988079071}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9180999994277954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6973000168800354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5863999724388123},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4812000095844269},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3675000071525574},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2608.28874","doi":"https://doi.org/10.48550/arxiv.2608.28874","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.28874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2608.28874","doi":"https://doi.org/10.48550/arxiv.2608.28874","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.28874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Responsible consumption and production","score":1.0,"id":"https://metadata.un.org/sdg/12"},{"display_name":"Industry, innovation and infrastructure","score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4417288335","https://openalex.org/W7160631548","https://openalex.org/W4416937293","https://openalex.org/W7131361526","https://openalex.org/W4414974217","https://openalex.org/W2969671511","https://openalex.org/W4200290855","https://openalex.org/W4415641065","https://openalex.org/W4404135607","https://openalex.org/W7204569988"],"abstract_inverted_index":{"The":[0,177],"automated":[1],"sorting":[2],"of":[3,24,116,173],"shredded":[4,69,117],"black":[5,118,175],"plastics":[6,119],"from":[7,120,161],"end-of-life":[8],"(EOF)":[9],"industrial":[10,76,125,215],"waste":[11],"presents":[12],"a":[13,142,207,210],"significant":[14],"challenge":[15],"in":[16,78,214],"recycling":[17,73],"facilities,":[18],"primarily":[19],"due":[20],"to":[21,47],"the":[22,49,110,180,198],"limitations":[23],"current":[25],"sensing":[26],"and":[27,63,91,98,133,136,152,164,192,201],"analytical":[28],"approaches.":[29],"Existing":[30],"studies":[31],"predominantly":[32],"rely":[33],"on":[34,65,87],"single-point":[35],"contact-based":[36],"mid-infrared":[37],"spectroscopy":[38],"or":[39],"laboratory":[40],"hyperspectral":[41,159,211],"imaging":[42],"(HSI)":[43],"setups,":[44],"which":[45],"fail":[46],"provide":[48],"spatially":[50],"resolved":[51],"analysis":[52],"necessary":[53],"for":[54,209],"fast,":[55],"bulk":[56],"processing.":[57],"Moreover,":[58],"available":[59,113],"datasets":[60],"are":[61,80,203],"laboratory-controlled":[62],"focus":[64],"intact":[66],"rather":[67],"than":[68],"plastics,":[70,77],"hindering":[71],"further":[72],"refinement.":[74],"Black":[75],"particular,":[79],"underrepresented,":[81],"while":[82],"most":[83],"classification":[84,172],"pipelines":[85],"depend":[86],"manual":[88],"region":[89],"selection":[90],"rule-based":[92],"spectral":[93],"matching,":[94],"neglecting":[95],"spatial":[96],"information":[97],"modern":[99],"deep":[100],"learning":[101],"(DL)":[102],"methods.":[103],"To":[104,195],"address":[105],"these":[106],"gaps,":[107],"we":[108,169],"introduce":[109],"first":[111,181],"publicly":[112,204],"HSI":[114],"dataset":[115,200],"EOF":[121],"vehicle,":[122],"comprising":[123],"four":[124],"polymers":[126],"across":[127],"13":[128],"co-registered":[129],"RGB,":[130],"VNIR,":[131],"SWIR,":[132],"MWIR":[134],"scenes":[135],"their":[137],"segmentation":[138],"pipeline.":[139],"We":[140],"developed":[141],"multi-modal":[143],"spectral-spatial":[144],"framework":[145],"that":[146],"integrates":[147],"foreground":[148],"isolation,":[149],"pixel-wise":[150],"classification,":[151],"object-level":[153],"majority":[154],"voting.":[155],"By":[156],"adapting":[157],"advanced":[158],"transformers":[160],"earth":[162],"observation":[163],"incorporating":[165],"chemometric":[166],"band":[167],"selection,":[168],"achieve":[170],"accurate":[171],"complex":[174],"plastics.":[176],"study":[178],"establishes":[179],"comprehensive":[182],"benchmark":[183,208],"using":[184],"nine":[185],"processing":[186],"methods,":[187],"including":[188],"chemometric,":[189],"machine":[190],"learning,":[191],"DL":[193],"architectures.":[194],"ensure":[196],"reproducibility,":[197],"complete":[199],"methodologies":[202],"released,":[205],"establishing":[206],"object-analysis":[212],"pipeline":[213],"inspection.":[216]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-09-02T00:00:00"}