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Record W4410575448 · doi:10.1002/jat.4811

Toxicological Evaluation of the Sweet Protein Brazzein Derived From <scp> <i>Komagataella phaffii</i> </scp> for Use as a Sweetener in Foods and Beverages

2025· article· en· W4410575448 on OpenAlexaff
Jwar Meetro, Labiba Nahian, Kirt R. Phipps, Trung D. Vo, Irina Dahms, Minal Prakash Lalpuria, Hadi Omrani

Bibliographic record

VenueJournal of Applied Toxicology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsFood scienceChemistryArtificial SweetenerBiotechnologyBiologySugar

Abstract

fetched live from OpenAlex

Brazzein is a promising new protein sweetener that has gained significant attention by the food and beverage industry in recent years. Brazzein has a sweetness intensity significantly greater than that of other low- and no-calorie sweeteners currently on the global market and can provide a comparable sweetness at lower use levels within food and beverage products. In this study, a safety assessment of the sweet protein brazzein, produced from the fermentation of Komagataella phaffii, was undertaken in an in vitro digestibility study, an in silico allergenicity assessment, an in vitro reverse mutation assay, an in vitro mammalian micronucleus assay, and a 90-day oral toxicity study in rats. The results of the in silico and in vitro studies indicate that brazzein is not readily digestible, does not have allergenic potential, and does not have genotoxic or mutagenic potential. In the 90-day toxicity study, brazzein was not associated with any adverse systemic effects at up to 2000 mg/kg body weight/day, the highest dose tested. The dose of 2000 mg/kg body weight/day was concluded to be the no observed adverse effect level. The findings from the current safety assessment demonstrate that brazzein is safe for use as a sweetener in foods and beverages for human consumption.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.274
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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