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Record W4293317918 · doi:10.1016/j.lwt.2022.113904

Impact of drying methods on banana flour in the gluten-free bread quality

2022· article· en· W4293317918 on OpenAlexaff
Verónica Guadalupe-Moyano, Sócrates Palácios-Ponce, Cristina M. Rosell, Fabiola Cornejo

Bibliographic record

VenueLWT · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersSecretaría de Educación Superior, Ciencia, Tecnología e InnovaciónEscuela Superior Politécnica del Litoral
KeywordsFood scienceStarchIngredientGlutenResistant starchChemistryGluten freeWheat flourOrganolepticMathematics

Abstract

fetched live from OpenAlex

Green banana flour (GBF) is considered a functional ingredient that could improve banana world's production sustainability. The banana drying method might influence the physicochemical and nutritional properties of GBF, affecting its performance in bread-making. The study aims to determine the impact of freeze-drying and oven-drying on gluten-free banana bread's quality. Freeze-dried banana flour (FDBF) bread had higher specific volume, resilience, and less brown coloration than oven-dried banana flour (ODBF) bread. Also, FDBF bread presented higher resistant starch (RS). The slowly digested starch (SDS) was similar in both types of bread. In contrast, rapidly digested starch (RDS) was significantly higher in FDBF bread, which led to higher expected glycemic index. Even though the nutritional fractions (RS and SDS) of both gluten-free banana bread exceeded 20 g/100 g bread (db), with no significant difference concerning the drying type, the FDBF bread presented improved characteristic.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.092
GPT teacher head0.428
Teacher spread0.336 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

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