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Record W2953991665 · doi:10.5539/jfr.v8n4p122

Effect of Whole Purple Potato Flour on Dough Properties and Quality of Steamed Bread

2019· article· en· W2953991665 on OpenAlexvenueno aff
Tianyu Zhang, Fengjie Zhang, Yanfei Cao, Haijing Zhang, Zhe Yang, Hongjun Li

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsSteamed breadFood scienceAbsorption of waterChemistryWheat flourVolume (thermodynamics)Wheat breadViscosityMaterials scienceComposite material

Abstract

fetched live from OpenAlex

WPPF was added into the wheat flour (WF) flour with different addition amount (0%~50%) to study the influence of whole purple potato flour (WPPF) on dough properties and quality of steamed bread. Result revealed that the WPPF addition significantly influenced the dough properties and quality of steamed bread. The water absorption, the maximum height of the gas release, total volume of CO2 release and the hardness of steamed bread significantly increased with the increase of WPPF addition amount, while decreased the dough stability, the maximum height of dough, the gas holding capacity and the specific volume. Moreover, peak viscosity, final viscosity and setback value had a remarkable decrease when 10% WPPF added, but increased following the addition of WPPF. Considering the sensory evaluation, the steamed bread with 20% WPPF is acceptable. Appropriate addition amount of WPPF improves the nutrition value and variety of steamed bread and did not effect on the quality of the quality of dough and steamed bread.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.138
GPT teacher head0.393
Teacher spread0.255 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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