A Study on the Bioavailability of Lactoferrin under Pasteurisation at Different Conductivities and Solid Contents
Bibliographic record
Abstract
Lactoferrin is a key functional ingredient in several nutraceutical products. During product formulation, lactoferrin often undergoes pasteurisation. However, the heating involved in standard pasteurisation processes can induce structural changes in lactoferrin, thereby impacting its bioavailability. In response to this challenge, this study aims to safeguard the structural integrity of lactoferrin during pasteurisation by exploring optimised conditions for pasteurisation. The results show that lactoferrin preserves its bioavailability and iron binding ability after pasteurisation when pasteurisation is conducted on samples with conductivity below 1 mS. It was found that lactoferrin solutions with a solid content of 4% and conductivity below 1 mS showed resistance to heat effects, resulting in higher bioavailability (94%). However, cloudiness and precipitation were observed in samples with conductivity of 2mS and above. The chromatographic results showed that samples pasteurised at higher conductivities exhibited a shoulder peak adjacent to the main lactoferrin peak, indicating the structural changes in lactoferrin. Based on the outcomes from this study, it is recommended that the suitable conditions for lactoferrin pasteurisation involve using a lactoferrin solution with a solid content of 1–4% and a conductivity below 1 mS.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".