Formulation, Biochemical Characterization and Shelf-Life Study of YoAlp® Whey Based Beverage Containing Fruits Juices Produced in Aosta Valley
Bibliographic record
Abstract
Background: Functional beverages are ordinary foods with components or ingredients able to provide specific health effects other than purely nutritional effect. Aim: This study aims to formulate a new functional beverage obtained from the recovery of YoAlp® whey, a bovine fermented milk obtained using autochthonous starter cultures, added with Renetta Canada/Golden Delicious-Raspberry (YWL), Renetta Canada-Aronia (YWA) and Ravèntse (YWR) fruit juices to improve the functionality of the beverage. Methods: Microbiological analysis and chemical characterization, by a proteomic approach and spectrophotometric microassay, have been conducted on beverages to investigate the expected shelf-life and potential health promoting effects. Results: The microbiological assessment confirmed the safety of the beverages and the probiotic bacteria vitality over a shelf life of seven days. Biochemical analysis performed highlight the presence of different bioactive peptides (ACE inhibitory and antioxidant), and the betacasomorphin 9 (BCM-9), marker of A2 variant of β-casein typically present in Aosta Valley cattle breeds. Moreover, high amounts of total polyphenols, which concur to the antioxidant activity values observed, have been found. Furthermore, ACE inhibitory activity is well correlated to the bioactive peptides detected. Finally, a consumer test confirmed the YoAlp® whey-based beverages appreciation. Conclusions: The production of these beverages could represent a huge opportunity for Aosta Valley’s farms to increase their income and competitiveness, creating a circular economy by recovering a by-product like whey and following sustainability and healthy trends, which are driving the food sector.
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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) | 0.001 | 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".