Exploring the integration of orange peel for sustainable gluten-free flatbread making
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".