{
  "abstract": "Reliable and chemistry-specific presorting of mixed plastic waste is essential for safe and efficient valorization. In this study, a resolution-robust mid-infrared attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) workflow coupled with simple machine-learning classifiers is presented for poly(vinyl chloride) (PVC) screening and subclassification of three polyethylene (PE) grades: low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE), and high-density polyethylene (HDPE). A total of 770 ATR-FTIR spectra in the fingerprint region (2500–500 cm⁻¹) were collected from seven plastics: LDPE, LLDPE, HDPE, PP, PS, PET, and PVC, using commercial pellets together with post-consumer PET and discarded PVC gloves. Principal component analysis (PCA) was used to compress the high-dimensional, correlated spectral inputs into a low-dimensional score space, reducing multicollinearity and overfitting risk while preserving class-identifiable fingerprint information. The resulting PCA structure showed clear low-dimensional separability among the plastic classes and PE subclasses, supporting the use of simple classifiers rather than heavily parameterized black-box architectures. Support-vector machine (SVM) and artificial neural network (ANN) classifiers were trained on the PCA scores, with internal-test and strictly independent external-validation performances evaluated separately. For the baseline dataset (2 cm⁻¹), both classifiers achieved 100% external-validation accuracy. Even after degradation to effective spectral resolutions of 8 and 16 cm⁻¹, SVM and ANN maintained 99.68% internal accuracy and 100% external-validation accuracy across PVC screening, PE-grade subclassification, and full seven-class discrimination. These results demonstrate a resolution-robust, PCA-interpretable, and externally validated ATR-FTIR/ML workflow for practical plastic presorting.",
  "abstract_shown": true,
  "affiliations": [
    "Korea Institute of Energy Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Youngho Jeong",
      "orcid": "0009-0005-5083-9036"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dd8wkjsqjxrq9.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15000518/v3",
  "fulltext_url": null,
  "id": "dd8wkjsqjxrq9",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000518/v3",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
  ],
  "openalex": "W7212258332",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15000518/v3",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/dd8wkjsqjxrq9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15000518",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000518/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1602|Spectroscopy and Chemometric Analyses",
      "subfield": "analytical"
    }
  ],
  "title": "Resolution-Robust Mid-IR ATR-FTIR Classification of Mixed Plastic Waste with PVC Screening and Polyethylene Subclassification",
  "title_text": "Resolution-Robust Mid-IR ATR-FTIR Classification of Mixed Plastic Waste with PVC Screening and Polyethylene Subclassification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/dd8wkjsqjxrq9",
  "venue": "ChemRxiv",
  "versions": []
}