Impact of Storage Conditions of Yogurt Dry Ingredients on the Physicochemical Properties of the Final Product
Bibliographic record
Abstract
This study investigated the impact of storage conditions of the ingredients for yogurt production on the rheological and physicochemical characteristics of the final fermented product. The novelty is the application of a special mix of milk protein concentrate and sodium caseinate for yogurt production. Separately exposing the protein mix powder and bacteria culture to 20 °C caused considerable changes in the obtained yogurt stiffness and the incubation times required to produce the gel due to a decrease in bacterial count. Minimal changes in bacteria viability were observed after storage at 5 °C. Lower temperature and shorter storage times increased yogurt firmness, viscosity, and storage modulus, resulting in a smoother and more viscous product. A linear correlation was found between yogurt firmness and water activity. Powders stored at lower temperatures and for shorter times produced yogurt with stronger texture and better water binding. Additionally, yogurt obtained from dry ingredients stored under these conditions required shorter incubation times. Storing the starter culture at 5 °C for at least 8 weeks had no significant effects on the physicochemical properties or incubation time requited to produce the final yogurt. This work highlights the importance of storage conditions of yogurt dry ingredients in maintaining the quality of the final product.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".