Response to the letter to the editor by Vinderola et al. (2025) Trends in Food Science & Technology 165, 105289
Bibliographic record
Abstract
This letter responds to the correspondence by Vinderola et al. (2025) regarding our article “Commercial and regulatory frameworks for postbiotics: an industry-oriented scientific perspective for non-viable microbial ingredients conferring beneficial physiological effects” ( Trends in Food Science & Technology , 163, 105130). We clarify that our work did not propose a new definition of postbiotics, but rather a sector-specific handling taxonomy designed for foods and dietary supplements, harmonizing existing academic definitions with regulatory practice. We reaffirm that our framework complements, rather than replaces, academic definitions, and provides a practical tool for classification, traceability, and labeling of non-viable microbial ingredients. We further address the rationale for the use of the term “beneficial physiological effects,” and the inclusion of cell-free fermentates as a pragmatic, historically consistent component of the postbiotic category. Finally, we invite a multi-stakeholder dialogue to align scientific, industrial, and regulatory perspectives on postbiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".