nirs4all: Canonical, content-addressed workflows for reproducible near-infrared calibration

A published near-infrared calibration is rarely a fully reproducible one. Its reported error depends on analyst decisions about signal representation, scatter correction, replicate handling, validation design, latent-variable selection, aggregation and model family. These decisions usually survive as scripts and output files rather than as one re-executable calibration object. We present nirs4all, an open-source Python system for making that object explicit. The contribution is a computer technique for chemometric reproducibility, not a new calibration algorithm. nirs4all records preprocessing and latent-variable search as canonical workflow declarations, links predictions and fitted artefacts back to the exact fold, chain and dataset hashes that produced them, and makes validation semantics such as signal type, replicate-grouped splitting and specimen-level aggregation first-class. A controller layer lets standard Python estimators and near-infrared operators share this workflow, provenance and deployment contract. On public datasets we show deterministic representation and verifiable provenance under a pinned environment, held-out partial-least-squares reporting isolated from selection, auditable replicate policy, and bounded operational cost. This is reproducibility of the declaration and lineage; strict bitwise numerical replay still requires a pinned environment or container. nirs4all is available at https://github.com/GBeurier/nirs4all (pip install nirs4all, AGPL-3.0-or-later primary public licence).