How the presence of residual lipids in a yellow mealworm protein concentrate affects its foaming properties?
Bibliographic record
Abstract
The use of whole and visible insects is poorly accepted in Western countries, and this remains a significant challenge for product development. However, using insect-based protein-rich ingredients, like protein concentrate, can improve levels of consumer approval. The residual lipid content in insect protein concentrates can influence their techno-functional properties. Our study therefore aimed to evaluate the impact of the residual lipid content on the protein structure and foaming properties of a mealworm protein concentrate. Our results showed that the protein content increased from 78.01 to 84.82 % after using chloroform-methanol for lipid removal. The particle size distribution shifted from a bimodal to a unimodal pattern, and the surface hydrophobicity decreased from 267.02 to 48.91 after completely removing lipids by chloroform-methanol, with no noticeable impact on the protein profile. The foaming capacity improved, resulting in the formation of a firm and fluffy foam with high stability over time. These results highlight the importance of controlling the residual lipid content in mealworm protein concentrates to enhance their techno-functional properties. The next steps will entail comprehensively characterizing the lipid profile and exploring the various mechanisms contributing to the techno-functional properties.
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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 | 0.001 |
| 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 | 0.001 |
| 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".