How to enhance the acceptability of insects food—A review
Bibliographic record
Abstract
Abstract About 1 billion people worldwide suffer from hunger, so exploring new food sources is very tempting for achieving zero hunger in the world. Edible insects (EIs) may be one of the ways to solve human protein deficiency. Currently, more than 200 species of EIs are consumed by over 2.5 billion people, especially in tropical regions, as part of their regular diets. However, there is still a large rejection in various parts of the world. In this review, we systematically summarize the factors behind the rejection of EIs as well as ways to improve the acceptability of EIs as alternative protein, essential vitamins, and mineral sources. The main goal of this research is to spread the knowledge of the benefits of eating EIs, consumer perception of insects, and enhance its acceptability as an alternative food. Sensory attributes, health‐related concerns, and sustainability issues are identified as the key factors affecting consumer acceptability of EIs. Conventional processing methods, such as blanching, drying, roasting, and fermentation, have been used in treating EIs to improve the quality and safety of EIs. Nine strategies were proposed to enhance the acceptability of insects as food, such as promoting food safety, encouraging product development, addressing cultural norms, enhancing the culinary experience, collaborating with restaurants, and increasing public awareness through education. The information in this work will shed more light on the consumption of EIs and pave the way for more research in this area.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".