Yacon Prebiotic Functional Beverages, the Sensory, Antioxidant Profiles, and Shelf Stability
Bibliographic record
Abstract
The increasing awareness on overall health of consumers has driven a shift from fruit juices and carbonated beverages to functional beverages. This research aimed to incorporate yacon concentrate to the formulation of functional beverages to improve the health-related properties. Using yacon concentrate as main ingredients, three functional prototypes have been developed: yacon-collagen, yacon-blackcurrant, and yacon-vitamin c. Sensory evaluation for yacon-collagen and yacon-blackcurrant beverages was conducted by a 9-point hedonic scale. Antioxidant activities of three yacon beverages were evaluated using the CUPRAC, DPPH, and FRAP assays. Yacon-collagen and yacon-blackcurrant beverages were sensory acceptable with ratings above the centre point of the scores (all ratings > 5, n = 50) on four sensory attributes (appearance, sweetness, flavor, overall liking). The antioxidant capacity of yacon-collagen, yacon-blackcurrant, yacon-vitamin c, and yacon concentrate were 1941mg/2300mg/1891mg/1193mg TE/100g (CUPRAC), 1943mg/2404mg/2122mg/1365mg TE/100g (DPPH), and 1219mg/2614mg/2990mg/992mg TE/100g (FRAP). The antioxidant capacity of yacon-blackcurrant and yacon-vitamin c were much higher than that of yacon concentrate because blackcurrant and vitamin c enhanced the antioxidant capacity. The development of yacon functional beverages with acceptable taste, verified health-related properties, applicable shelf-life, as new dietotherapy applications of yacon concentrate, could provide more healthier food products for consumers to exercise healthier food choices.
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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) | 0.003 | 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".