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Record W4403498488 · doi:10.5937/ffr0-52510

The effects of the addition of lyophilized berry fruits on the leavening properties of dough and volume properties of bread

2024· article· en· W4403498488 on OpenAlexaboutno aff
Anna Kolesárová, Tatiana Bojňanská, Miriam Solgajová, Andrea Mendelová, Jana Kopčeková, Adriana Kolesárová

Bibliographic record

VenueFood and Feed Research · 2024
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsLeavening agentBerryFood scienceVolume (thermodynamics)ChemistryHorticultureFermentationBiologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

This study examined the effects of addition of pulverized lyophilized fruits (chokeberries, black elderberries, blackcurrants, Saskatoon berries) mixed with wheat flour (in amounts of 5%, 10% and 15%) on the quality of dough and bread made from this mixed flour. A reofermetometer F4 was used to evaluate the fermentation of the experimental doughs and a Volscan was used to evaluate the volume of the experimental bread. The dough with 15% addition of black elderberries had the best ability to form fermentation gases, and the dough with 15% addition of Saskatoon berry had the lowest. Doughs supplemented with chokeberry and blackcurrant produced a significantly increased total volume of CO2, but also lost a significant amount of gas during fermentation. The best bread volumes were achieved with the application of elderberry in all investigated amounts, and with the addition of Saskatoon berries in amounts of 5% and 10%. The sensory analysis showed that breads with 5% and 10% fruit additions had the best overall appearance, colour, and textural properties. In the evaluation of the taste properties, breads with the addition of chokeberry, elderberry and Saskatoon berry in the amount of 5% were rated the best.

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)0.001
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 score0.027
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.061
GPT teacher head0.285
Teacher spread0.224 · 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
Published2024
Admission routes1
Has abstractyes

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