Emulsifying properties of plasma fractionated from egg yolk using low centrifugal forces—Mayonnaise preparation
Bibliographic record
Abstract
Egg yolk can be fractionated into two fractions, plasma and granule, at high centrifugation force. This work was designed to study the influence of using low centrifugal forces (2000 to 6,000 ×g) for a short time (5 min) on the fractionation efficiency. The chemical composition and emulsifying properties (oil droplet size and stability against creaming) of fractionated plasmas were also investigated. The results showed that the separation efficiency of granules was significantly increased with increasing centrifugal force. No significant difference was found in emulsifying properties of plasmas fractionated at all centrifugal forces compared to whole egg yolk. Plasma fractionated at 5,000 and 6,000 ×g was further applied for mayonnaise preparation. Mayonnaises prepared from plasmas showed no significant difference in the rheological and colorimetric properties compared to whole egg yolk. Plasma prepared from low centrifugal force, compatible to industrial setting, maintains its original emulsifying properties and is applicable for mayonnaise preparation. Novelty impact statement Although many years of research on fractionation of egg yolk fractions, industrial application of egg yolk fractions is rare. Literature information on this field was relied on high centrifugal forces of fractionation. The conditions we applied using low centrifugation forces are thus significant to help the industry realize the potential of value-addition to egg yolk component such as phosvitin in the granule while using the plasma fraction for its emulsifying property. The obtained results suggested that plasma prepared using low centrifugal forces showed comparable rheological properties and was applicable for preparing mayonnaise, which indicates its potential in future industry application.
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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".