The structural modifications and techno-functional properties of yellow mealworm protein concentrate is influenced by the presence of residual lipids
Bibliographic record
Abstract
Abstract In this study, we compared the impact of partial and total defatting by using hexane and chloroform-methanol respectively, on the lipid and protein profiles, protein structure, and techno-functional properties of a yellow mealworm protein concentrate to address the influence of residuals lipids. The results showed significant changes in the particle size distribution (bimodal versus monomodal), surface hydrophobicity (49.35 vs 21.74 a.u.), and secondary structure of the proteins following defatting. Phospholipids accounted for 60% of the residual lipids content. Although residual lipids after Soxhlet extraction did not notably influence protein solubility except at pH 6 (49.74% after total defatting compared to 59.28% after partial defatting), they negatively impacted foaming properties (foam stability of 100% vs 65% after 30 min) and induced higher emulsifying properties (3.23 vs 2.57 for emulsion stability index) of the mealworm protein extract. Consequently, the residual lipids content after Soxhlet extraction of mealworm protein ingredients appears as an important parameter to consider in determining techno-functional properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".