GELATION OF MIXTURES OF SOYMILK AND RECONSTITUTED SKIM MILK SUBJECTED TO COMBINED ACID AND RENNET
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it