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Record W3199679864 · doi:10.1111/ijfs.15348

Chemical leavening and other baker’s yeast substitutes: overview of patents filed between 1833 and 2019

2021· article· en· W3199679864 on OpenAlexaff
Pierre Gélinas

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2021
Typearticle
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsCegep de Saint Hyacinthe
Fundersnot available
KeywordsLeavening agentYeastFood scienceSodium bicarbonateChemistryTasteXylitolSugarBusinessFermentationBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Summary The aim of this study was to review general trends in the patent literature on baker’s yeast substitutes, including chemical leavening and gas injection. Overall, 494 unique inventions were described in patents filed over about 200 years, between 1833 and 2019. About two‐thirds of patented inventions targeted acid‐reacting materials, and the rest was on non‐specific protection of active ingredients (13%), gas injection such as in the aerated bread process (12%), gas‐releasing agents like sodium bicarbonate (9%) and miscellaneous dough improvers, including flavouring agents and nutrients (4%). Drawbacks included off‐taste and potentially non‐healthy residues such as aluminium. Most baker’s yeast substitutes were dry chemical preparations, so‐called baking powders, that mainly allowed easy and cheap production of miscellaneous fat‐ and sugar‐rich foods such as cakes, and probably contributed to the popularity of the latter. Applications included ready‐to‐use dry bakery mixes and refrigerated doughs. Yeast substitutes were originally developed for bread in England and perfected later for fat‐rich bakery foods, mainly in the United States. where inventors owned 59% of total patents. Specific aspects of baker’s yeast substitutes will be fully covered in companion articles.

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)0.000
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 score0.040
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.319
Teacher spread0.278 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Food Science & TechnologySame topicMicrobial Metabolites in Food BiotechnologyFrench-language works237,207