{
  "abstract": "Spectral classification is conventionally performed on band intensities, yet ratios between bands have long served as diagnostic indices in vibrational spectroscopy, from lipid-to-protein markers of membrane composition to amide ratios reporting protein secondary structure. Their use, however, has remained confined to a handful of expert-selected pairs, leaving most of the ratio information contained in a spectrum unexamined. Here I present FRACTAL (Full Ratio Combinatorial Analysis for Classification and Analytical Interpretation), which computes every pairwise log-intensity ratio among detected peaks to form a complete ratiometric fingerprint, grounded in compositional data analysis. Each component is a contrast between two assigned bands and is invariant to the multiplicative factors that scale spectral intensities. Applied to serum Raman spectra from a total of 103 donors (53 with COVID-19, 50 healthy controls), FRACTAL screened all 105 ratios among 15 reproducible peaks and identified 38 discriminant contrasts; a ten-ratio fingerprint then matched full-spectrum performance across six classifier architectures spanning linear, kernel-based and ensemble methods (mean internal AUC 0.89–0.93) and generalized to a withheld 20-donor cohort, where every classifier exceeded an AUC of 0.92 and all six converged on the same donor-level prediction accuracy at the median-AUC seed. The retained ratios map onto aromatic amino acid, acute-phase protein and protein–lipid contrasts independently reported in COVID-19. FRACTAL establishes exhaustive ratiometric fingerprinting as a compact, robust and interpretable representation for spectral classification and spectroscopic marker discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Materials Science and Engineering",
    "Massachusetts Institute of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Lamyaa M. Almehmadi",
      "orcid": "0000-0002-2572-9733"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cat4gk9gc8nwz.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010325/v1",
  "fulltext_url": null,
  "id": "cat4gk9gc8nwz",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010325/v1",
  "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",
      "1304|Spectroscopy Techniques in Biomedical and Chemical Research"
    ]
  ],
  "openalex": "W7221724290",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010325/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/cat4gk9gc8nwz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010325",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010325/v2"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Spectroscopy Techniques in Biomedical and Chemical Research",
      "subfield": "biophysics"
    }
  ],
  "title": "FRACTAL: A Ratiometric-based Fingerprinting Framework for Robust Spectral Classification and Ratio-based Spectroscopic Marker Discovery",
  "title_text": "FRACTAL: A Ratiometric-based Fingerprinting Framework for Robust Spectral Classification and Ratio-based Spectroscopic Marker Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/cat4gk9gc8nwz",
  "venue": "ChemRxiv",
  "versions": []
}