Studies on Mitigating Lipid Oxidation Reactions in a Value-Added Dairy Product Using a Standardized Cranberry Extract
Bibliographic record
Abstract
A standardized whole cranberry extract (WCE) was used to stabilize a model sunflower-casein emulsion prototype for future formulation activities with a fresh cream cheese product. The WCE contained total organic acids (20% w/w) and polyphenols (5%), the latter consisting of total anthocyanins (10%, w/w) and proanthocyanidins (12% w/w). Antioxidant capacity of the WCE was determined by ORAC, (hydrophilic ORAC = 348.31 ± 33.45 µmol of Trolox equivalents/g; lipophilic ORAC = 11.02 ± 0.85 µmol of Trolox equivalents/g). WCE was effective at stabilizing the model emulsion at a level of 0.375% (w/w), yielding a final pH of 5.6. Generation of initial lipid peroxidation products, hexanal and pentanal was inhibited by 92.4% ± 3.9% and 66.6% ± 5.3% (n = 3), respectively, when emulsions containing WCE were incubated at 50 °C for 90 h. This information was useful for formulating a fresh cream cheese product containing WCE to produce value-added potential and good self-life. The standardized WCE gave a final pH of 5.6 for the cheese premix and also significantly (P < 0.05) lowered both the PV and CD after 28 and 21 days at 4 °C storage, respectively, compared to untreated control. We conclude that there are important functional role(s) for cranberry constituents when presented as a standardized ingredient for producing value-added, stable fresh dairy products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".