Implementation of biological active compound BIO-AP-IRGA: chemical features of enriched yogurt and cottage cheese
Bibliographic record
Abstract
The imbalance of micro- and macronutrients is a significant health issue, often due to poor nutrition, vitamin deficiencies, and disruptions in mineral metabolism. To address these challenges, biologically active supplements (BASs) are gaining popularity. In Kazakhstan, the BIO-AP-IRGA dietary supplement, derived from dried saskatoon berry, chokeberry, and whey, was developed to improve local food products. The study explored integrating BIO-AP-IRGA into yogurt and cottage cheese, assessing the impact on their nutritional and chemical properties. Standard analytical methods were used to evaluate the fortified products. The results showed that the addition of BIO-AP-IRGA significantly increased the carbohydrate content, nearly doubling it in yogurt and increasing it by eight times in cottage cheese. Additionally, the polyphenol content increased in both yogurt and cottage cheese; yogurt showed a slight increase, while cottage cheese saw nearly a twofold rise. However, the amino acid content decreased with the increasing amount of BIO-AP-IRGA, showing an inverse relationship. These findings highlight the physicochemical changes that occur when BIO-AP-IRGA is incorporated into dairy products. The supplementation improves the nutritional quality of yogurt and cottage cheese, particularly by enhancing polyphenol and vitamin C levels. This innovation presents an opportunity for the food industry to develop functional dairy products with potential health benefits, addressing nutrient deficiencies and promoting better health
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".