Nutritional labeling and amino acid profile in high protein Chilean dairy products: a new alternative for health and sports
Bibliographic record
Abstract
INTRODUCTION: Introduction: protein is an essential nutrient that can be obtained through different food sources. In recent years, a growing development in the food industry of dairy products with higher protein content has emerged in the national market. Métodos: the present work analyzed the nutritional information, protein content, and amino acid profile of high protein yogurts. Five main brands of high-protein yogurt (PY) were selected, as was a sample of regular yogurt (RY), and one of whey protein isolate (WP), which underwent a proximal analysis and amino acid profile by high-performance liquid chromatography (HPLC). Results: it was observed that the protein content of the analyzed YP ranged between 7.2 and 15.1 g/portion, representing 6.8 % to 11 % of total content, twice the content of YBAT (3,4 %). Regarding carbohydrates and fats, these were found to range between 6.25 and 13.5 g/serving and 0.9 and 5.3 g/serving, respectively. Leucine content of the PY varied between 0.6 and 1.5 g/portion, which was higher than RY (0.3 g/portion) and lower than WP (2.2 g/portion). The sum of essential amino acids was found to be between 3 and 6.9 g/portion in PY, 1.7 g/serving y 8.5 g/serving in WP. Conclusion: these results show that PYs are a good alternative to promote a correct protein intake, similar to proteins for sports use, so their recommendation could be useful to promote consumption in different populations according to their needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".