Вивчення впливу соків з дикорослої сировини Північного Казахстану на хімічний склад сироваткових напоїв
Bibliographic record
Abstract
In deep processing technologies whey is a more "valuable" product than cheese, cottage cheese. Even though whey has a low energy value among dairy products, it is at the same time very valuable biologically. Recently, the consumption of berries has increased markedly everywhere. This growth is explained by the growing attention of the population to health and the appearance on the market of many canned products "saturated with berries". In addition, there are many scientific studies concerning the composition of biologically active components in the composition of berries. Thus, scientific developments to produce new foods enriched with berries are of crucial importance for berry producers, food processors and consumers. The scientific novelty of this study is to investigate the possibility of using wild plant raw materials of Northern Kazakhstan (chokeberry and saskatoon berry) in milk beverages’ technology, that will be described for the first time. This berries despite their rich chemical composition, are rarely used in the food industry. The study describes the nutritional value and chemical composition of whey drinks enriched with juice from saskatoon berries, black chokeberry. In our study it is proposed thermosaltic coagulation as a primary treatment for whey. The comparative analyses of natural whey and treated one shows the expediency and benefit of using thermosaltic coagulation. At the same time, juices from wild berries increase the biological and nutritional value of whey drinks Thus, the described advantages are confirmed with assays and confirm the expediency of using this combined technology in the production of drinks from whey with berry juices. The obtained research results will be used to develop a new technology to produce juice drinks based on whey and will also be described in a patent for a utility model for the production of beverages from whey
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".