Potentials and Pitfalls on the Use of Passion Fruit By-Products in Drinkable Yogurt: Physicochemical, Technological, Microbiological, and Sensory Aspects
Bibliographic record
Abstract
Peels and seeds are the primary by-products of the passion fruit agroindustry. This study was designed to evaluate the potential of passion fruit peel and seeds flour (PFF) as a source of fiber and minerals to enhance the functional properties of drinkable yogurt. Proximate composition, mineral content, technological (pH, viscosity, color, and syneresis), and microbiological analyses (lactic acid bacteria, as well as yeast and mold counts), acceptance test, descriptive sensory analysis, and shelf life assessments were analyzed. Drinkable yogurts fortified with PFF showed higher fiber levels (both soluble and insoluble) and mineral content (potassium, magnesium, and manganese). Incorporation of PFF increased the viscosity and promoted changes in the color parameters of yogurts. During storage, the pH and the number of viable lactic acid bacteria decreased while the syneresis and yeast and mold counts increased. The shelf life of drinkable yogurts was estimated to be 21 days. Regarding sensorial aspects, drinkable yogurt fortified with 2% of PFF was considered the most viable product for market exploitation. The present contribution indicates that the use of passion fruit by-products in the development of drinkable yogurts is a viable alternative which can be explored for nutritional, technological, and sensory purposes by the food industry.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".