The Compositional and Functional Attributes of Commercial Flours from Tropical Fruits (Breadfruit and Banana)
Bibliographic record
Abstract
The objective of this study was to compare the compositional and functional properties of tropical flour sources (two breadfruit flours (type A and type B) and a banana flour) with a more traditional flour source (wheat flour). Macro-nutrient composition, pH, water and oil holding capacity, bulk density, particle size, solubility, swelling power, pasting properties, and thermodynamics (gelatinization and retrogradation) were determined. All flours evaluated were similar in their composition with high levels of carbohydrates (greater than 82.52 g/100 g on a dry-matter basis), with most of the carbohydrate content comprised of starch (greater than 67.02 g/100 g). The tropical fruit flours had greater (p < 0.05) water holding capacity than wheat flour. Breadfruit flour B had the lowest (p < 0.05) bulk density, while banana flour had the greatest (p < 0.05) bulk density. The swelling power of the tropical flours was greater (p < 0.05) than the wheat flour. The viscosity of the tropical flours was higher than wheat flour but decreased significantly when temperature was held at 130 °C. These results indicated that the two breadfruit flours and banana flour have great potential for application in processed food products, and have similar compositional attributes to a more traditional flour.
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 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 | 0.001 | 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) | 0.001 | 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".