{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:01:49Z","timestamp":1773838909250,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Environmental Fund","award":["02\/C12-i01.01\/2022"],"award-info":[{"award-number":["02\/C12-i01.01\/2022"]}]},{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UID\/50016\/2025"],"award-info":[{"award-number":["UID\/50016\/2025"]}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["2023.15056.TENURE.059"],"award-info":[{"award-number":["2023.15056.TENURE.059"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["2023.15056.TENURE.046"],"award-info":[{"award-number":["2023.15056.TENURE.046"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Processes"],"abstract":"<jats:p>Advances 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\u201330 components) using an 80\/20 train\u2013test 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.<\/jats:p>","DOI":"10.3390\/pr14060964","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:10:04Z","timestamp":1773825004000},"page":"964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FTIR-Based Machine Learning Identification of Virgin and Recycled Polyester for Textile Recycling in Industry 4.0"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9749-9674","authenticated-orcid":false,"given":"Maria In\u00eas","family":"Barbosa","sequence":"first","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2248-0861","authenticated-orcid":false,"given":"Ana Margarida","family":"Teixeira","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"given":"Maria Leonor","family":"Sousa","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8236-2563","authenticated-orcid":false,"given":"Pedro","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7206-8771","authenticated-orcid":false,"given":"Clara","family":"Sousa","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5381-6615","authenticated-orcid":false,"given":"Pedro Miguel","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref_1","unstructured":"Textile Exchange (2025). 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