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Record W3048449748 · doi:10.1021/acs.jafc.0c02703

Comparison of the Functionality of Exopolysaccharides Produced by Sourdough Lactic Acid Bacteria in Bread and Steamed Bread

2020· article· en· W3048449748 on OpenAlexafffund
Dan Xu, Ying Hu, Fengfeng Wu, Yamei Jin, Xueming Xu, Michael G. Gänzle

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsSteamed breadFood scienceLeuconostoc mesenteroidesLactic acidSteamingChemistryFermentationFermentation in food processingLeuconostocWeissellaLactobacillus sakeiChewinessBacteriaLactobacillusBiology

Abstract

fetched live from OpenAlex

Exopolysaccharides (EPSs) produced by lactic acid bacteria improve the quality of bread; however, their functionality in steamed bread is unknown. This study aimed to compare the impact of EPS produced during sourdough fermentation on the quality of bread and steamed bread. Sourdoughs were fermented with EPS-producing Fructilactobacillus sanfranciscensis, Weissella cibaria, and Leuconostoc mesenteroides; Latilactobacillus sakei LS8 and chemically acidified sourdough were prepared as controls. EPS production generally enhanced the specific volume, improved the texture, and reduced the staling rate of bread. The effect of EPS on steamed bread quality was more pronounced when compared to its effect on bread quality. Remarkably, the beneficial effects of F. sanfranciscensis bread quality were largely independent of EPS formation and may relate to gluten modifications rather than EPS production. In conclusion, the direct comparison of sourdough and EPS functionality in steaming and baking provides novel insights for the optimization of commercial (steamed) bread production.

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)0.000
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 score0.075
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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

Citations63
Published2020
Admission routes2
Has abstractyes

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