GELATION OF MIXTURES OF SOYMILK AND RECONSTITUTED SKIM MILK SUBJECTED TO COMBINED ACID AND RENNET
Bibliographic record
Abstract
ABSTRACT The present work investigated the formation of a mixed gel containing soymilk and reconstituted milk. The mixtures contained 1.4 and 2% (w/v) milk and soymilk protein, respectively. When gelation was induced by addition of glucono‐delta‐lactone, the mixtures showed a gelation point well above the isoelectric point of the milk proteins, suggesting that soy proteins play a major role in the formation of the network. When rennet was added in combination with acidification, the gels showed an earlier onset of aggregation and a higher storage modulus than the gels prepared only with acid. Confocal microscopy showed networks with mixed acid–rennet gels having more branches and compact structures with denser clusters than acid‐induced gels. These results demonstrated that by fine‐tuning the gelation of mixed soymilk and reconstituted milk, it is possible to obtain gels with unique microstructure and texture, where both proteins are contributing to the network structure. PRACTICAL APPLICATIONS Mixed protein gels are increasingly employed to develop novel high‐protein products. This work illustrates the potential to employ a mixed gelation to induce the formation of protein matrices containing aggregates of soy and milk proteins. At the ratio used in this work (2% soy protein and 1.4% milk protein), soymilk proteins determined the gelation behavior of the mixtures. The gels showed an onset of gelation at pH around 6, and in the presence of rennet the skim milk proteins also participated in the network.
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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".