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Record W4393952290 · doi:10.1016/j.lwt.2024.115969

Exploring the integration of orange peel for sustainable gluten-free flatbread making

2024· article· en· W4393952290 on OpenAlexaff
Nicola Gasparre, Raquel Garzón, Karina Marín, Cristina M. Rosell

Bibliographic record

VenueLWT · 2024
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersHORIZON EUROPE Framework ProgrammeEuropean Commission
KeywordsFood scienceOrange (colour)ChemistryIngredient

Abstract

fetched live from OpenAlex

In response to the growing demand for sustainable and nutritious gluten-free (GF) products, this study explore orange peel by-products as a breadmaking ingredient of GF rice or corn flatbreads. The orange peel powder exhibited remarkable water retention properties attributed to its high dietary fiber content. Partial pre-gelatinization of rice (64 g/100 g) and corn (67 g/100 g) flour allowed obtaining doughs with sufficient consistency and extensibility to obtain GF flatbreads. The inclusion of orange peel powder (0–9 g/100 g) influenced the consistency and thermal behavior of the dough. Modifying hydration levels mitigated the hardness resulting from orange peel powder substitution, leading to improved folding properties. Final products exhibited enhanced nutritional profiles, including higher ash and dietary fiber content. The optimum nutritional and technological outcomes were observed when employing the maximum substitution rate of 9 g/100 g, along with adjusted hydration (164 g/100 g for corn and 169 g/100 g for rice), with notable emphasis on the improved extensibility of the final products. Compared to rice-based flatbreads, corn-based flatbreads displayed more uniform dough consistency, lower gelatinization enthalpy, higher yellowness, and decreased extensibility due to the stiffer dough texture. This study suggests that orange peel residue holds significant promise as a nutritional and technological enhancer for gluten-free flatbreads.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.175

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 head0.124
GPT teacher head0.306
Teacher spread0.182 · 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 designOther design
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

Citations16
Published2024
Admission routes1
Has abstractyes

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