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Wheat Sourdough Breadmaking: A Scoping Review

2024· review· en· W4391214361 on OpenAlexafffund
Yamina De Bondt, Celine Verdonck, M. Brandt, Luc De Vuyst, Michael G. Gänzle, Marco Gobbetti, Emanuele Zannini, Christophe M. Courtin

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2024
Typereview
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Alberta
FundersStaatssekretariat für Bildung, Forschung und InnovationKU LeuvenVlaamse regeringEuropean CommissionHORIZON EUROPE Framework ProgrammeFonds Wetenschappelijk OnderzoekCanada Research ChairsVrije Universiteit BrusselAgentschap Innoveren en Ondernemen
KeywordsLeavening agentFood scienceQuality (philosophy)StandardizationBiotechnologyHealth benefitsBread makingFermentationBiologyComputer scienceMedicineTraditional medicine

Abstract

fetched live from OpenAlex

Using sourdough in breadmaking can enhance bread's shelf-life and flavor compared to exclusive baker's yeast use and is believed to increase its nutritional quality and healthiness. Previous research established insight into the microbial ecology of sourdough, but the link between leavening agent use, processing, and bread quality remains elusive. However, such knowledge is key for standardization, research on the health benefits, and the definition of sourdough bread. In this systematic scoping review, we analyzed 253 studies and identified large variations in the type and amount of leavening agent, fermentation conditions, and bread quality (specific loaf volume and acidification). The interrelation between these elements and their effect on the extent of fermentation is discussed, together with issues preventing proper comparison of breadmaking procedures. With this review, we want to contribute to the dialogue concerning the definition of sourdough-type bread products and the research into the health benefits attributed to them.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.384
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2024
Admission routes2
Has abstractyes

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