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Record W6986932889

Retrogradation in some tropical root starches and its influence on vital gluten/starch composite bread quality.

2015· other· en· W6986932889 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Nairobi Research Archive (University of Nairobi) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRetrogradation (starch)StarchOrganolepticAmyloseComposite numberGlutenMoisture
DOInot available

Abstract

fetched live from OpenAlex

A Differential Scanning Calorimeter (DSC) was used to study starch retrogradation in 60 percent moisture gels of native root starches of cassava, sweet potato, arrowroot and taro, as well as soft white spring (SWS) and hard Canadian western red spring (CWRS) wheat starches at 24 degrees C, and related to retrogradation as determined by a compressimeter in composite bread crumbs containing 15 percent vital gluten and 85 percent starch. Bread quality was further examined through loaf volume, mass and panel organoleptic assessment of crumb grain, texture and chewability. Root starches retrograded more rapidly and strongly than most wheat starches. The higher the amylose content and solubility, the more prone to retrogradation the root starches were found to be. All starch pastes developed weak retrogradation endotherms between 50 and 60 degrees C. A second set of stronger retrogradation endotherms were developed by cassava, sweet potato and arrowroot starches between 75 and 100 degrees C after the 6th day of storage. DSC endotherms and crumb compressibility proved that cassava starch retrograded most rapidly and intensively, followed by sweet potato, arrowroot, taro and finally the wheat starches. The extent of retrogradation in starch had a corresponding negative influence on the quality of the vital gluten/starch composite bread in comparison to pure wheat flour bread. (AS).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0060.002
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.053
GPT teacher head0.302
Teacher spread0.250 · 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.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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