Current state of insect proteins: extraction technologies, bioactive peptides and allergenicity of edible insect proteins
Bibliographic record
Abstract
This review aims to provide an updated overview of edible insect proteins and the bioactivity of insect-derived peptides. The essential amino acid content of edible insects is compared with well-known protein sources to demonstrate that edible insects have the potential to cover the protein quality requirements for different groups of the population. Then the current methodologies for insect protein extraction are summarized including a comparison of the protein extraction yield and the final protein content of the resulting products for each method. Furthermore, in order to improve our understanding of insect proteins, their functional properties (such as solubility, foaming capacity, emulsifying, gelation, water holding capacity and oil holding capacity) are discussed. Bioactive peptides can be released according to various enzymatic hydrolysis protocols. In this context, the bioactive properties of insect peptides (antihypertensive, antidiabetic, antioxidant and anti-inflammatory properties) have been discussed. However, the allergens present in insect proteins are still a major concern and an unsolved issue for insect-based product consumption; thus, an analysis of cross reactivity and the different methods available to reduce allergenicity are proposed. Diverse studies of insect protein hydrolysates/peptides have been ultimately promoting the utilization of insect proteins for future perspectives and the emerging processing technologies to enhance the wider utilization of insect proteins for different purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".