Безпечність швидкозаморожених соків із м’якоттю
Bibliographic record
Abstract
Problem definition. Fresh fruits and vegetables are an essential and indispensable source of biologically and physiologically active substances. However, during the cultivation plant raw materials collect contaminants of chemical nature, because of more intensive farming techniques and environmental disasters. At the same time, during storage fresh fruits and vegetables can be exposed to bacteriological damage, which causes quantitative and qualitative losses and accumulation of mycotoxins. Goal of research is to study the safety of quick-frozen juices with pulp.Methods and materials. Objects of research – quick-frozen juices with pulp received from variety of melon Amal, watermelon Khersonskyi, apples Golden Delicious, carrot Canada, celery Giant, and beetroot Bordeaux. To increase consumption value, improve organoleptic qualities, to stabilize color and consistency of quick-frozen juices we proposed to blend apple, carrot and celery juices and to add natural polysaccharide xanthan gum and ascorbic acid.Results of research. Coliform bacteria and pathogenic microorganisms, including Salmonella, were not found. It was discovered that the freezing process is a determining factor for reducing the number of mold fungi and yeasts, which are non-persistent to low temperatures. Radioactive nuclides 137Cs і 90Sr in the juices were not detected. Toxic elements, such as cadmium, mercury and arsenic also were not found. The levels of lead, cuprum and zinc were within the limits of the permissible concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".