Bibliographic record
Abstract
The changes in functional properties of soybean protein isolates(SPI) before and after freezing were investigated to reveal the effect of cryopreservation conditions, namely sample concentration, freezing temperature, freezing time on functional properties of SPI including water-holding capacity, oil-holding capacity, emulsifying property and texture property. The results showed that reduced SPI concentration could lead to an initial increase and then a final decrease in water-holding capacity, oil-holding capacity and emulsifying property. When SPI concentration was 1:12, the waterholding capacity, oil-holding capacity, emulsion stability, hardness and elasticity achieved maximum levels. When freezing temperature was-18 ℃, the water-holding capacity, oil-holding capacity, emulsion stability, hardness and elasticity reached maximum levels; however, when freezing temperature was-20 ℃, the best emulsion stability was observed. With the prolongation of freezing time, the emulsion stability of SPI gradually decreased, reaching the highest level when freezing time was 3 days; after 2 days of freezing, the highest water-holding capacity was achieved. Compared with unfrozen samples, the functional properties of frozen SPI were obviously weakened, indicating poorer water-holding capacity, oilholding capacity, emulsifying property and texture property.
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 | 0.001 |
| 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".