USE OF PASSION FRUIT PEEL FLOUR IN THE MANUFACTURING OF LOAF BREAD
Bibliographic record
Abstract
Brazil is the largest worldwide Passiflora fruit producer producing a peel waste with large potential of use. The objective of this work was to evaluate the effect of different Passiflora peel flour (PPF) concentrations in addition to loaf bread. The experiment was carried out in the Agro Industry Sector of IF Goiano Ceres from August to November of 2019. The experimental design was totally randomized with four treatments with different concentrations of Passiflora peel flour (0%, 5%, 10% and 15%) and three repetitions. Physicochemical (pH, titratable acidity, humidity, ash), microbiological analysis (total coliforms and thermotolerant coliforms) were carried out at Instrumental and Microbiologic laboratories. Sensory evaluations were performed with 50 non trained panelists to verify acceptance and purchase intention. It was observed a decrease to pH level (varying from 6.06 to 5.39) and to humidity level (from 30.65% to 27.66%) with the addition of PPF. To ash and titratable acidity levels, there was an increase in the observed values with the addition of PPF, varying from 1.04% to 1.36% and from 2.23% to 4.76%, respectively. It was observed a higher acceptance for 0% (80.31%) and 5% (78.16%) PPF treatments. In this way, we conclude that the use of PPF to enrich loaf bread is an alternative to partial substitution of wheat flour for the addition of nutrients and fibers. The microbiological analysis results indicated that all treatments are according to Brazilian legislation. Therefore, passiflora peel flour has potential to be used in loaf bread fabrication, and could be used to substitute the wheat flour up to 5%. Keywords: bakery, Passiflora edulis, functional food
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".