A New Method for Growth Factor Enrichment from Dairy Products by Electrodialysis with Filtration Membranes: The Major Impact of Raw Product Pretreatment
Bibliographic record
Abstract
This study is focused on fractionation of insulin-like growth factor I (IGF-I) and transforming growth factor-β2 (TGF-β2) using a new electro-based membrane process calledelectrodialysis with filtration membranes (EDFM). Before EDFM, different pretreatments were tested, and four pH conditions (4.25, 3.85, 3.45, and 3.05) were used during EDFM. It was demonstrated that a 1:1 dilution of defatted colostrum with deionized water to decrease mineral content followed by the preconcentration of GFs by UF is necessary and allow for these compounds to migrate to the recovery compartment during EDFM. MS analyses confirmed the migration, in low quantity, of only α-lactalbumin (α-la) and β-lactoglobulin (β-lg) from serocolostrum to the recovery compartment during EDFM. Consequently, the ratio of GFs to total protein in recovery compartment compared to that of feed serocolostrum solution was 60× higher at pH value 3.05, the optimal pH favoring the migration of IGF-I and TGF-β2. Finally, these optimal conditions were tested on acid whey to also demonstrate the feasibility of the proposed process on one of the main by-products of the cheese industry; the ratio of GFs to total protein was 2.7× higher in recovery compartment than in feed acid whey solution, and only α-la migrated. The technology of GF enrichment for different dairy solutions by combining ultrafiltration and electrodialysis technologies was proposed for the first time.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".