Development and Characterization of Soymilk Okara Gels Modified by Citric Acid
Bibliographic record
Abstract
Okara, a valuable resource rich in fiber (53 – 58 %), protein (25 – 28 %), and lipids (9 – 11 %), also contains significant isoflavones and essential minerals, offering nutritional and health benefits. Recent research aims to enhance its utilization in high-fiber tofu, however, the incorporation of okara in soymilk for the gelation results in a heterogeneous, gritty network structure due to its high water absorbing capacity and large particle size. The present study aimed to explore the effects of citric acid (CA) modified okara materials on the characterization of soymilk okara gels as well as gain understanding of the underlying mechanisms of okara modification and soymilk gelation. Initially, okara was modified with CA solutions at concentrations ranging from 2.5 to 10 %, followed by the heat-induced gelation with soymilk at 95 °C. With the modification of okara by 2.5 % CA solution, soymilk okara gels exhibited increase in level of homogeneity and hydrophobic protein-protein interactions, indicating an improvement in the compatibility between okara and soymilk gel. This finding might be attributed to the extensive okara fiber degradation, release of pectin and formation of amphiphilic protein-polysaccharides conjugates via Maillard reaction, as confirmed by TGA and FTIR. When increasing the CA concentration from 2.5 to 5 and 10 %, the soymilk okara gels exhibited even higher levels of homogeneity, accompanied with the reduction in viscoelasticity by around 4 times and in hydrophobic protein-protein interactions from 62.8 to 37.2 mg protein/g sample. Concurrently, the water holding capacity (72 – 80 %) and mechanical properties including true fracture stress (1400 – 1700 Pa) and Young’s modulus (7000 – 10000 Pa) remained similar. Additionally, at the micro-scale, soymilk okara gels modified by CA exhibited a particulate protein matrix, compared to fine-stranded structure observed in gels without CA, which might be attributed to the unreacted CA’s effect on protein during the gelation process.
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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) | 0.000 | 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 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".