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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".