Smoothing temperature and ratio of casein to whey protein: Two tools to improve nonfat stirred yogurt properties
Bibliographic record
Abstract
Consumers are not always ready to compromise on the loss of texture and increased syneresis that nonfat stirred yogurts display compared with yogurts that contain fat. In this study, we investigated milk protein composition and smoothing temperature as a means to control nonfat yogurt microstructure, textural properties, and syneresis. Yogurts were prepared with different ratios of casein to whey protein (R1.5, R2.8, and R3.9). Yogurts were pumped through a smoothing pilot system comprising a plate heat exchanger set at 15, 20, or 25°C and then stored at 4°C until analysis (d 1, 9, and 23). Yogurt particle size and firmness were measured. Yogurt syneresis and water mobility were determined, respectively, by centrifugation and time domain low-frequency proton nuclear magnetic resonance ( 1 H-LF-NMR). Increasing the smoothing temperature increased gel firmness and microgel (dense protein aggregates) sizes independently of the whey protein content. Also, yogurt microgel sizes changed with storage time, but the evolution pattern depended on protein ratio. Yogurt R1.5 showed the largest particles, and their sizes increased with storage, whereas R2.8 and R3.9 had smaller microgels, and R3.9 did not show any increase in microgel size during storage. Micrographs showed a heterogeneous gel with the empty area occupied by serum for R1.5, whereas R2.8 and R3.9 showed fewer serum zones and a more disrupted gel embedding microgels. Induced syneresis reduced with greater whey protein content and time of storage. This is in agreement with 1 H-LF-NMR showing less bulk water mobility with increasing whey protein content during storage. However, 1 H-LF-RMN revealed higher values of spontaneous serum separation during storage for R1.5 and R3.9 yogurts, whereas these were lower and stable for R2.8 yogurt. Microgels play an important structural role in yogurt textural attributes, and their characteristics are modulated by whey protein content and smoothing temperature. Optimization of these parameters may help improve nonfat stirred dairy gel.
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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 | 0.001 |
| 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".