Machine Processing of Milk in Dairy Production
Bibliographic record
Abstract
For fermented milk products, consistency plays a leading role: it provides a quality product and shapes consumer demand. There have been numerous studies of the effect of the technological process on the properties of sour cream, kefir, etc. However, these studies were performed after the introduction of ferment. Thus, the effect of parameters of thermal and mechanical treatment during the primary stage on the physical and mechanical properties of fermented milk products remains understudied. The research objective was to confirm the following hypothesis: the parameters of homogenization during the primary stage affect the consistency of the fermented milk products. A set of experiments made it possible to expose the dependence of the rheologic properties of sourmilk products from the regime parameters of homogenization. The research featured initial mix for kefir and sour cream production. The milk was preheated to 45C in an Alfa-Laval pasteurizer and separated in an Alfa-Laval separator. The fat-free milk (1% of fat for kefir production) was heated in a pasteurizer to 55–60C and homogenized at 8–16 mPa. The homogenizing device of the manometer was additionally equipped with a phase separator delimiter of the S-homogenizer type. The acidity and viscidity for the sour cream and kefir were measured at different pressure values. The power expenses on homogenization depended on the pressure and the volume of milk. The pressure of homogenization proved to be a meaningful factor and affected the fermentation process. It rendered a substantial influence on the consistency and taste qualities of the fermented milk product. The experiment defined the optimal temperature of fermentation for kefir production. The optimization of pressure decreased the energy consumptions by 4.4 kW/h (24.4%) per ton. Solar thermal collectors were used to preheat the milk before homogenization, which decreased the specific energy consumption by 10.5 kW/h per ton. The new parameters lower the prime cost of the dairy products and raise their competitiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".