Effect of Soluble Solids and High Pressure Treatment on Rheological Properties of Protein Enriched Mango Puree
Bibliographic record
Abstract
The effects of high pressure treatment on the rheological properties of protein enriched mango puree were evaluated. In the first part, the original soluble solids in mango puree (28° Brix) was lowered to 26, 23 and 20° Brix, and each was supplemented with 2, 5 and 8% of whey protein to assess the influence of added protein. The samples were then evaluated by dynamic rheology. Pressure treatment resulted in a progressive increase in elastic (G') and viscous modulii (G″) as well as complex viscosity (η*). Values of G' were higher than G″ demonstrating that the product behaved more like a gel. Additional tests were carried out by simultaneously varying protein and soluble solids contents, and keeping the total solids at 28%. In this case, the effects of pressure levels and holding times were similar to previous results. However, the positive effects of higher protein were negated by the negative effects of lower soluble solids, resulting in an overall decreasing effect on rheology. The developed models effectively predicted the combined influence of protein and soluble solid concentrations on rheological parameters (R2 > 0.85). Sensory evaluation of 2% and 5% protein supplemented and pressure treated (500 MPa/3 min) mango puree yielded acceptable sensory qualities, resulting in a product with enriched protein content.
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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".