Insect processing for food and feed: A review of drying methods
Bibliographic record
Abstract
Production of insects for food and feed purposes is rapidly emerging in Europe, filling an important niche of locally supplied protein and fat sources with improved environmental sustainability. Processing of insect biomass is becoming of utmost importance to fulfill the requirements for safe edible biomass and find efficient ways to reduce potential biological and chemical hazards. Current methods of insect biomass processing, well-developed, and established in food and feed industry, rely on thermal treatment (blanching, boiling, drying, cooling, freezing, freeze drying), mechanical (grinding, pressing, milling), and fractionation processes (extraction, purification, separation, centrifugation). This article summarizes and reviews recent activities performed by different interdisciplinary research groups dealing with insect drying. The diverse techniques for insect drying are discussed with the objective of identifying the ones with the highest economic, environmental, and social potential. Moreover, the quality attributes of insects dried with different methods (starting from simple sun drying and finishing with Pulsed Electric Fields enhanced lyophilization) are analyzed. Finally, selected legal aspects concerning usage of dried insects as food are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".