Agrochemical contaminants in six species of edible insects from Uganda and Kenya
Bibliographic record
Abstract
Edible insects are currently promoted worldwide as an alternative animal protein source, but they are mostly still harvested from the wild where they are predisposed to contamination with agrochemicals. This study analysed six species of edible insects (Ruspolia differens, Rhynchophorus phoenicis, Schistocerca gregaria, Oryctes sp, Pachnoda ephippiata and Acanthoplus sp) collected from different habitats and/or reared in the laboratory in Kenya and Uganda for safety from agrochemical contaminants using liquid chromatography mass spectrometry. The residue levels were statistically compared with the Codex Alimentarius Commission maximum residue limits (MRLs). Residues of only nine agrochemicals were detected in the insects out of 374 chemicals which were screened. The detected agrochemicals include two insecticides (aminocarb and pymetrozine), three herbicides (atraton, methabenzthiazuron and metazachlor) and four fungicides (carboxin, fenpropimorph, fludioxonil and metalaxyl). Ruspolia differens and adult Oryctes sp were free from detectable levels of any agrochemical. Whereas the pesticides residue levels in most insect samples were within maximum residue limits, some of them notably P. ephippiata from black soldier fly larval frass, R. phoenicis from oil palm and P. ephippiata from plant compost contained 2-, 8- and 49-fold higher levels of atraton, methabenzthiazuron and metazachlor, respectively, than MRLs. These findings demonstrate that edible insects may accumulate harmful residues of agrochemicals from the environment where they breed or forage, rendering them unsafe for human consumption or feeding animals. The mechanisms for possible bioaccumulation of these agrochemicals in the insects remains to be investigated. Development of methods for farming edible insects under regulated indoor conditions to ensure their safety as sources of food or feed is recommended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".