{
  "abstract": "Raman mapping produces a hyperspectral cube, and turning that cube into numbers depends strongly on a chain of preprocessing choices. Two further problems appear once a laboratory holds many maps rather than one: the corpus is analysed one file at a time, and the analysable corpus is only the subset somebody remembered to export by hand from the instrument’s proprietary format. We present RAMANSP, a compact open framework providing typed spectral containers, composable preprocessing protocols and an analysis layer, and extend it in three directions. First, a direct reader for the undocumented Horiba LabSpec 6 container, reverse engineered here and verified bit identical against the instrument’s own text export, which removes the manual export step and takes this corpus from 1 usable map to 23. Second, a cross-sample knowledge graph linking every acquisition by spectral similarity, shared unmixing endmembers and disorder-metric proximity, with each metric edge carrying the preprocessing protocol under which it was computed. Third, and as the main methodological contribution, spectral 3D Gaussian splatting: a map V (x,y,ν) is represented by a differentiable field of anisotropic ellipsoids, each localising a domain in the two spatial axes and a band along the wavenumber axis, fitted by first-order descent on closed-form gradients, entirely in NumPy on a CPU. On an anonymised corpus of 108 acquisitions the graph recovers 23 communities at modularity Q = 0.65 and flags 5 groups of byte-identical duplicates, and across 6 maps the field reconstructs held-out wavenumber channels to within 0.70 to 1.34 of each map’s own measurement noise at 8.1 to 37.7× compression, that is, essentially to the noise floor.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College Street, Toronto, Canada, M5S 3E5",
    "Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street, Toronto, Canada, M5S 3M2",
    "Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Canada, M5S 3G8"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Siddhartha Pahari"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Jainish Shailesh Solanki"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4qdyex803exrr.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008805/v1",
  "fulltext_url": null,
  "id": "4qdyex803exrr",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008805/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",
      "1204|Cultural Heritage Materials Analysis"
    ]
  ],
  "openalex": "W7213367069",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008805/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4qdyex803exrr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008805",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008805/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Reading the whole corpus: a reproducible cross-sample Raman workflow for materials characterisation, with a spectral 3D Gaussian splatting representation for hyperspectral maps",
  "title_text": "Reading the whole corpus: a reproducible cross-sample Raman workflow for materials characterisation, with a spectral 3D Gaussian splatting representation for hyperspectral maps",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/4qdyex803exrr",
  "venue": "ChemRxiv",
  "versions": []
}