Edible insects as foods: mapping scientific publications and product launches in the global market (1996-2021)
Bibliographic record
Abstract
Edible insects are gaining interest for their health and environmental merits as human food. Within this framework, the main objectives of this research are to fill the gap between market trends and scientific research about the status of edible insects in foods, suggest a roadmap for future research and boost product launches. For these reasons, an attempt has been made to review the progress of scientific documents related to edible insect foods and to detect the prominent trends in insect-based foods during the period 1996-2021. By putting the findings of these searches together, we were able to observe that scientific publications have increased exponentially since 2015 – similar to product launches but at a higher speed. Europe was found to be the most prolific region in terms of publications and food product numbers due to increased awareness of the benefits of insects. Market data offered insights into the main selling countries, food applications and insect ingredients. In the future, food formulators will still have to find innovative solutions to offer insect-based foods with pleasant flavours and textures and, in turn, contribute to healthy and sustainable gastronomy. Ensuring safety and setting a clear legislative framework will further organise the sector and thus boost edible insects as a future food commodity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.035 | 0.054 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".