TECHNOLOGY FOR THE PRODUCTION OF SAUSAGE PRODUCT WITH A PLANT EXTRACT FROM FERMENTED ST. JOHN'S WORT
Bibliographic record
Abstract
This article discusses the technology for producing a sausage product using an aqueous plant extract of fermented St. John's wort. Extracts of medicinal plants, when added to food products, can have a number of useful properties on finished products, such as antioxidant, functional, flavoring, and act as a natural dye. Today, this is a new trend in the production of meat products. The composition of the sausage included the following components: first-grade beef, chicken fillet, skimmed milk, chicken eggs, beetroot powder, and an extract of fermented St. John's wort. Fermented St. John's wort has significant advantages over unfermented – its beneficial properties are enhanced. Several extract formulations were prepared, showing that the ratio of water to St. John's wort of 80:20 yielded higher values of vitamin B6 and flavonoid content. These indicators justify its use as an antioxidant component. The functional properties of the sausage product are given by beetroot powder, which is a source of dietary fiber, minerals, vitamins, and is also a natural dye. Standard research methods were used to analyze the chemical composition, and statistical data processing was carried out using Excel. The production technology of the experimental cooked sausage followed a traditional technological scheme. The final product was analyzed for chemical composition and physicochemical properties. The results of the study showed that the prototype had preferential values of indicators compared to the control and longer shelf life - 40 days.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".