Potential applications of ficin in the production of traditional cheeses and protein hydrolysates
Bibliographic record
Abstract
Using proteolytic enzymes extracted from plant materials is a promising way to ensure the sustainability of the food industry. This is particularly true for the dairy industry, especially in cheesemaking and the production of different milk protein hydrolysates for special food applications, particularly nutrition for infants, older adults, and people with food allergies. Ficin, a cysteyl protease isolated from the latex of the fig tree ( Ficus carica ), is characterized by strong enzymatic activity and can be used for milk clotting and protein hydrolysis for application in different foods. In particular, it can be used for milk protein hydrolysis to produce ingredients with reduced or eliminated allergenicity and improved bioavailability. Ficin can also be used as an active and effective replacement for calf rennet in cheesemaking, such as in traditional Cacioricotta and Teleme cheeses. It can also be used to produce protein hydrolysates with low or no allergenicity for application in infant formula and geriatric nutrition. This work provides an overview of ficin, a plant-derived protease, with an emphasis on its potential application in the production of some traditional cheeses and milk protein hydrolysates for special food applications.
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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 | 0.000 | 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) | 0.001 | 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".