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FTIR-based machine learning identification of virgin and recycled polyester for textile recycling in industry 4.0

2026-03-18Artigo de investigação Acesso aberto
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datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorBarbosa, Maria Inês
dc.contributor.authorTeixeira, Ana Margarida
dc.contributor.authorSousa, Maria Leonor
dc.contributor.authorRibeiro, Pedro
dc.contributor.authorSousa, Clara
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2026-04-14T11:38:20Z
dc.date.available2026-04-14T11:38:20Z
dc.date.issued2026-03-18
dc.description.abstractAdvances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation.eng
dc.identifier.doi10.3390/pr14060964
dc.identifier.eid105034360198
dc.identifier.other25cb53e7-0c22-479b-ac09-3e8e42942064
dc.identifier.urihttp://hdl.handle.net/10400.14/57514
dc.identifier.wos001725487100001
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectIndustry 4.0eng
dc.subjectClassificationeng
dc.subjectInfrared spectroscopyeng
dc.subjectMachine learningeng
dc.subjectPolyester fiberseng
dc.subjectTextile industryeng
dc.titleFTIR-based machine learning identification of virgin and recycled polyester for textile recycling in industry 4.0
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume14
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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