{
  "abstract": "Chromatographic peak detection is the bottom-most step of the data-processing chain, yet almost every classical detector defines its decision variables in units of signal amplitude, so those variables co-vary with baseline drift, injection amount, and detector gain, and a single-pass decision is made once against an unchanged trace. We reframe detection not as a better criterion but as a change in the computational organization of observation, and we do so in three ordered layers. Layer 1 — Observation-Guided Computation (OGC) is a sequential computational framework — an analytical data-interpretation principle rather than a new peak detector — built around the Observation Deviation (OD) measure established in our companion work13. OGC does not introduce another peak criterion or redefine OD; it embeds OD as an observation measure into an observe–detect–reconstruct–flatten–reobserve loop, so the observation condition is updated after each detected structure. Within a working window the trace is normalized to a unitless observation space, scored by OD against a single dimensionless threshold, and all final boundaries are re-anchored on the raw signal. OGC is deliberately independent of any particular candidate generator: it can be composed from any apex finder, and six heterogeneous engines all converge to a common performance plateau. Under one frozen configuration OGC reaches interval-coverage F1 = 1.000 on three of four synthetic benchmark sets with zero false positives, converts six heterogeneous apex-finder engines into region locators that all converge to F1 ≈ 1.000 (lifting bare derivative and moving-average detectors, unusable at F1 ≤ 0.090, by up to +0.967), is invariant to four orders of magnitude of dynamic range, and identifies high-confidence observable structures on two real GC–MS chromatograms where no global prominence fraction could serve both. Layer 2 — OGC-Net asks whether the representation of chemical observation can itself be learned: it algorithm-unrolls OGC's recursive loop into a differentiable K = 5-round network of 160,516 parameters. It runs a 150-point segment in 13.9 ms (batch 1) to 0.92 ms (batch 128) on CPU and, evaluated under the identical matching protocol, beats the frozen deterministic OGC by +0.116 peak-level F1 precisely on the harder, deployment-identical windowed distribution (0.6944 vs 0.5782). Layer 3 — OGC-Web delivers OGC-Net as a single offline, zero-backend HTML file whose forward pass is a pure-JavaScript port with base64-embedded weights, reproducing the peak-level agreement with the OGC reference client-side. Our central message is that chromatographic observation can be quantified and computationally organized as a sequential process, and that learning the framework — not fitting output labels — is the stronger objective when a deterministic gold-standard algorithm already exists.",
  "abstract_shown": true,
  "affiliations": [
    "Nanyang Technological University",
    "Shimadzu Asia Pacific",
    "ChemoPower Technology Pte Ltd"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhang Zhi Xi"
    },
    {
      "affs": [
        1
      ],
      "name": "Chua Chun Kiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Hua-Jun Zhang",
      "orcid": "0000-0002-3947-0750"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a2k14gwrqfh3v.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008492/v1",
  "fulltext_url": null,
  "id": "a2k14gwrqfh3v",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008492/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",
      "1607|Analytical Chemistry and Chromatography"
    ]
  ],
  "openalex": "W7211941331",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008492/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/a2k14gwrqfh3v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008492",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008492/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Analytical Chemistry and Chromatography",
      "subfield": "physical"
    }
  ],
  "title": "Observation-Guided Computation: Sequential Reobservation for Robust Chromatographic Peak Detection",
  "title_text": "Observation-Guided Computation: Sequential Reobservation for Robust Chromatographic Peak Detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/a2k14gwrqfh3v",
  "venue": "ChemRxiv",
  "versions": []
}