Scientific production on enzymatic hydrolysis of bovine whey and its bioactive peptides: A bibliometric approach
Bibliographic record
Abstract
The increasing interest in the health benefits of whey-derived bioactive peptides highlighted the need for a comprehensive understanding of their production and applications. This study analyzed the global research landscape on the enzymatic hydrolysis of bovine whey and its bioactive peptides from 2004 to 2024. A bibliometric approach was used to identify key themes, trends, and collaborative efforts. Data were collected from 183 documents across 80 sources, and thematic and Multiple Correspondence Analysis (MCA) maps were employed to categorize research themes and reveal central clusters. The analysis demonstrated a 12.74% annual growth in publications, with significant contributions from countries such as China, Canada, India, and Brazil. Key journals like “LWT-Food Science and Technology” and “Food Chemistry” were identified as leading sources. The functional properties of whey-derived peptides, including antioxidant, antihypertensive, antidiabetic, antithrombotic, and hypocholesterolemic effects, were highlighted, underscoring their potential applications in the food and pharmaceutical industries. The collaborative nature of the research was evident, with an average of 4.92 co-authors per paper and international collaborations accounting for 26.78% of the documents. The findings emphasized the therapeutic potential of bioactive peptides and the need for continued exploration of novel technologies and applications. It was concluded that the enzymatic hydrolysis of whey proteins remains dynamic and interdisciplinary, with promising avenues for future research and development in enhancing health outcomes and innovative food solutions.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.150 | 0.219 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".