Physicochemical and Sensory Evaluation of Spreads Derived from Fruit Processing By-Products
Bibliographic record
Abstract
Apple, tomato, and grape pomaces, as well as an apple–grape (1:1) mixed pomace, were employed in the formulation of fruit-based spreads to valorize these underutilized by-products. The influence of pectin addition on the physicochemical and sensory properties of the spreads was also examined. All spread preparations carried the ‘high fiber’ nutrition claim. The apple pomace spread demonstrated the highest total and soluble dietary fiber contents (14.13 and 4.28%, respectively). Colorimetry showed higher L* and a* values for the tomato pomace spreads. Rheometry of the spreads revealed pseudoplastic flow and weak gel-like behavior (G′ > G″); the tomato and grape pomace spreads with pectin exhibited the highest η*, G′, and G″ values. A texture analysis (spreadability test) indicated that pectin addition affected only the mixed pomace spread, resulting in the least spreadable product. Regarding bioactive compounds, the apple pomace had the highest total phenolic content, and the grape pomace exhibited the highest antioxidant activity, both of which were also reflected in their corresponding spreads. A principal component analysis indicated a strong correlation among flavor, mouthfeel, and moisture content, which were negatively correlated with color intensity and spreadability. The apple pomace spread with added pectin was the most widely preferred by consumers due to its appealing mouthfeel, spreadability and flavor.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".