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Sources & Attribution

Credit where it's due. Flavormancer stands on a lot of open science and open software; this file records what we use and who to thank. Licenses are noted where known but must be confirmed before any commercial release — this is a record of provenance, not legal advice. Get an IP/OSS-license review before ship.

Software & libraries

Cheminformatics / ML (training side — Python)

  • RDKit — molecular parsing, fingerprints, descriptors, SMARTS, InChIKeys. (BSD-3-Clause.)
  • DeepChem — GNN training framework under OpenPOM. (MIT.) Python-only; the one runtime-Python piece.
  • OpenPOM (BioMachineLearning/openpom) — the message-passing GNN for odor; open reimplementation of the principal-odor-map work. (MIT — confirm.) The aroma model is theirs in spirit.
  • scikit-learn — RandomForest taste heads + sweetness-intensity regressor. (BSD-3-Clause.)
  • PyTorch — tensor/Autograd backend under DeepChem. (BSD-style.)
  • NumPy, pandas — arrays + dataframes. (BSD-3-Clause.)
  • UMAP — 2D odor/flavor-space map. (BSD-3-Clause.)
  • skl2onnx — exports the sklearn taste models to ONNX. (Apache-2.0.)
  • (thermo — evaluated for Joback boiling point, then REJECTED for accuracy; not shipped. MIT.)

Product / serving side

  • ONNX + ONNX Runtime — run taste models in-process in .NET. (MIT.)
  • ASP.NET Core / .NET — the app/API backbone. (MIT.)
  • React — frontend. (MIT.)
  • PostgreSQL + pgvector — DB + embedding/substitution search. (PostgreSQL License.)
  • FastAPI — the Track-A demo serving layer (+ aroma sidecar if needed). (MIT.)
  • Docker — single-box deployment. (Apache-2.0.)

Data sources

Taste

  • ChemTastesDB — Rojas et al., curated taste dataset (sweet/bitter/umami/sour/salty/tasteless). Zenodo, DOI 10.5281/zenodo.5747393 (and the extended record). License CC-BY-4.0 (commercially clean with attribution).
  • cosylab BitterSweet — Bagler lab (IIIT-Delhi). Code AGPL-3.0 (bind-aware: train-your-own-on-data is the safe pattern; INCLUDE_COSYLAB=False drops it for clean licensing).
  • FlavorDB — Bagler lab (IIIT-Delhi); taste + odor + natural-source associations. License CC BY-NC-SA 3.0 (NonCommercial) — incompatible with a commercial product; not used.
  • SweetenersDB v2.0 — Bouysset et al. (2020) Food Chem., building on Chéron et al. (2017); relative-to-sucrose sweetness intensity for the regressor. Released MIT by the authors' own lab (ChemSenSim, github.com/chemosim-lab/SweetenersDB) — the paywall is only on the journal article, not the authors' own data. In use.
  • BitterDB (future) — bitterness intensity, if/when added.
  • UMP442 / BIOPEP-UWM — umami references. BIOPEP-UWM is web-only; the GitHub repost Shoombuatong/Dataset-Code carries no license (all-rights-reserved) and is umami peptide data (a different class from our small-molecule head). Not used.

Aroma

  • Pyrfume + Leffingwell / GoodScents (GS-LF) odor datasets — the usual training data behind OpenPOM, but RESTRICTED and NOT USED: Leffingwell's manifest cites use restrictions (John Leffingwell & Google); GoodScents/Arctander/Flavornet (© Datu Inc.) are likewise proprietary. We exclude all of them (the demo may go to a customer / commercial use). The aroma model will use only commercial-clean open odor data (CC-BY sets like keller_2016; smaller — see DATA-SOURCES.md). The OpenPOM code is MIT.
  • keller_2016 — Keller & Vosshall (2016), BMC Neuroscience, CC-BY-4.0; ~480 molecules with naive-subject odor-descriptor ratings. The only commercially-clean odor-descriptor set — evaluated for the aroma model and found too noisy to learn from (CV-R² ≤ 0 across all 20 descriptors; see docs/AROMA.md).

Safety / regulatory (lookups — data-gated)

  • FEMA GRAS list — usual/maximum use levels for the dosing analyzer. (FEMA.)
  • EU declarable fragrance/flavor allergen annex — the labeling flags. (EU regulation.)
  • PubChem — name↔SMILES↔CID resolution. (Public domain data; confirm API terms.)

Research & methods

  • Principal odor map — Lee et al., "A principal odor map unifies diverse tasks in olfactory perception," Science, 2023. The basis for the aroma GNN (OpenPOM reimplements it; the same group founded Osmo). The single most important scientific credit here.
  • Molecular fingerprints — Rogers & Hahn, "Extended-Connectivity Fingerprints," J. Chem. Inf. Model., 2010 (Morgan/ECFP — the taste-head features).
  • Aqueous solubility (ESOL) — Delaney, "ESOL: Estimating Aqueous Solubility Directly from Molecular Structure," J. Chem. Inf. Comput. Sci., 2004.
  • Toxicological Threshold of Concern / Cramer classification — Cramer, Ford & Hall, 1978; Toxtree (EU JRC) for the validated decision tree.
  • Odor Activity Value (OAV = concentration ÷ detection threshold) — standard flavor-chemistry framework for blend balance.
  • Group contribution (Joback) — Joback & Reid, 1987. Evaluated and rejected for boiling point here (≈90 °C error on benzaldehyde); recorded so the decision is documented.
  • Documented food-process contaminants — benzene (benzoate+ascorbate), N-nitrosamines (nitrite+amines), ethyl carbamate, acrylamide (Maillard), furan, 3-MCPD/glycidyl esters, 4-methylimidazole, biogenic amines — all from established food-safety literature.

Prior art & landscape (context, not used in the build)

We are not first; the field is active and funded. Mapping it honestly:

  • Osmo — digitized smell; principal-odor-map authors; B2B fragrance/flavor.
  • Gastrograph AI / Analytical Flavor Systems — predictive sensory analytics.
  • Tastewise, Ai Palette — F&B trend/flavor AI.
  • Aromyx — taste/smell biosensors.
  • Senomyx (acquired by Firmenich) — taste/aroma receptor reverse-engineering.
  • Symrise, Givaudan, IFF, dsm-firmenich, MANE/ChemoSensoryx — flavor houses with internal AI for formulation.

Crediting the science

The work this builds on, stated plainly: the aroma model is an open reimplementation (OpenPOM) of the 2023 Science principal-odor-map work; the taste models train on open curated databases (ChemTastesDB and others); everything runs on hardware you own. Honesty about provenance is part of the credibility.