Technology Development for Panna Cotta Enriched with Grape Skin Powder with Focus on Nutritional Value and Sustainability
Bibliographic record
Abstract
This research investigates the integration of grape skins, a by-product of the winemaking industry, into Panna Cotta formulations to enhance nutritional value, bioactive compound content, and sustainability in food production. The study addresses the underutilization of grape skins, which are rich in polyphenols, dietary fibers, and antioxidants with proven health benefits. Four Panna Cotta variants were developed by incorporating grape skin powder at 1%, 2.5%, 5%, and 7.5% concentrations, alongside a control. Physico-chemical analyses included colorimetric parameters, texture profiling, total polyphenol content, and antioxidant activity. Sensory evaluation was conducted to determine consumer acceptance, and microbiological testing ensured product safety. The results demonstrated a significant increase in polyphenol content and antioxidant capacity with higher levels of grape skin powder, with the most balanced sensory acceptance observed for the 2.5% and 5% formulations. Textural analysis revealed a correlation between powder concentration and increased firmness and elasticity. Microbiological assessments confirmed the absence of pathogenic microorganisms in all samples. The findings have implications for the development of functional foods that combine indulgence with nutritional and environmental benefits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".