Effects of partial substitution of milk fat for canola oil or linseed oil on the oxidative stability and volatile profile of high‐protein dairy beverages
Bibliographic record
Abstract
Background, Context or Rationale Due to the increased demand for and use of milk fat, the milk solid nonfat surplus represents a major challenge for the dairy industry. Substituting milk fat for vegetable oils to produce high‐protein dairy beverages with additional nutritional benefits could help to maximise the utilisation of all milk components. Aim(s) This study investigated the effect of total or partial (50%) substitution of milk fat for canola or linseed oil on the physical stability, fatty acid profile, peroxide value and volatiles of dairy beverages (3.25% fat, 6.8% protein) stored at 4°C for 30 days. Methods Two approaches to partial milk fat replacement were studied. The mixed emulsions approach consisted of mixing a milk fat emulsion with a canola or linseed oil emulsion. In the fat blending approach, milk fat and vegetable oil were blended together prior to homogenisation. Experiments were done according to a factorial design, repeated independently three times and analysed statistically using ANOVA. Major Findings When milk fat was substituted for 100% of the linseed or canola oil, lipid oxidation was markedly increased during processing and storage. A major change in the volatile profile was observed, with significant increase in alcohol, aldehyde and ketone contents. Substituting 50% of milk fat for vegetable oils significantly improved the resistance to oxidation when compared with total replacement of the milk fat, and reduced the amount of undesirable volatiles. Compared with the mixed emulsions approach, fat blending approach further reduced oxidation and total volatiles by 18% and 11%, respectively. The presence of milk fat within each lipid droplet contributes to a better protective effect. Scientific or Industrial Implications This study proposes a simple approach to partly replace milk fat and promote the use of milk solid nonfat. Results will support the dairy industry in the development of innovative beverages having a more balanced fatty acids’ profile and enhanced oxidative stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".