Multi-Elemental Analysis of Edible Insects, Scorpions, and Tarantulas from French (Online) Market and Human Health Risk Assessment Due to Their Consumption: A Pilot Study
Bibliographic record
Abstract
Edible insects are becoming increasingly popular as protein alternatives to traditional animal-based products. As such, information on their elemental composition is important to ensure they are safe for human consumption. This article describes the development and validation of a rapid, reliable method for the simultaneous determination of 19 elements (Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Pb, Se, Sr, and Zn) in edible insects by inductively coupled plasma mass spectrometry (ICP-MS) following closed vessel microwave digestion. The method was validated using three insect certified reference materials, namely black soldier fly larvae meal (BFLY-1), cricket flour (KRIK-1), and mealworm powder (VORM-1). The method was applied to analyze twelve different (whole) insect species. The maximum amount of each sample was calculated for As, Cd, and Pb with respect to their provisional tolerable daily intake values established by the Food and Agricultural Organization/World Health Organization. Most of the samples, except for scorpions and tarantulas, were safe to consume at large doses (1000-10,000 insects per day). Furthermore, most of the samples contained high levels of Fe, K, Na, and Zn, providing a preliminary overview of the nutritional profile of these novel protein alternatives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".