Studying stirred yogurt microstructure using optical microscopy: How smoothing temperature and storage time affect microgel size related to syneresis
Bibliographic record
Abstract
A grainy texture and high syneresis are 2 defects in low-fat stirred yogurt that are often disliked by consumers. In this study, a rheometer controlling the shear rate and temperature was used to simulate the smoothing step of yogurt manufacture. Identical formulations containing whey protein isolate or whey protein concentrate were compared. After the yogurt milk underwent heat treatment, inoculation, and fermentation at 42°C, the yogurt was smoothed at 42°C (Y42) or 20°C (Y20) or during a cooling ramp from 42°C to 20°C (YR). Induced syneresis (serum expelled by centrifugation) was measured on d 3. Sizes of microgels (dense protein aggregates) were investigated on d 0, 4, and 7 by laser diffraction and by image analysis using optical microscopy. Optical microscopy was also used to characterize the reorganized protein network embedding microgels. The type of whey protein ingredient had only a slight effect on the induced syneresis of YR and Y20 treated yogurts, and the major effect came from the smoothing temperature. The Y42 treatment presented the highest induced syneresis; YR and Y20 had similar low induced syneresis values. Images showed a heterogeneous microstructure (large microgels, reorganized gel) and serum separation for Y42; the YR and Y20 networks were homogeneous. Both the image analyses and laser diffraction showed that the microgel size depended on the smoothing temperature. However, only the image analyses made it possible to identify a time dependency effect on microgel size during storage. The number of microgels >10 4 µm 2 continued to increase over time, whereas the number of microgels <10 3 µm 2 decreased. Microscopic observations were less destructive than laser diffraction and highlighted the presence of microgel aggregation during storage.
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) | 0.002 | 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".