Deoxynivalenol (DON) Accumulation and Nutrient Recovery in Black Soldier Fly Larvae (Hermetia illucens) Fed Wheat Infected with Fusarium spp.
Bibliographic record
Abstract
Fusarium head blight (FHB) is one of the most significant causes of economic loss in cereal crops, resulting in a loss of $50–300 million for Canadian agriculture. The infected grain (containing Fusarium-damaged kernels (FDKs)) is often both lower in quality and kernel weight, and it may be unsuitable for human and animal consumption due to mycotoxin presence. However, it still contains a considerable amount of nutrients. A method to recover the nutrients without the mycotoxins should be beneficial for the agricultural economy. In this study, our objective was to examine recovery methods of the nutrients in relation to mycotoxin accumulation in the insect. The FDKs were fermented with Aspergillus oryzae and/or Lactobacillus plantarum (solid-state fermentation (SSF)). The SSF kernels were then provided to 50 young, black soldier fly larvae (BSFL) for 12 days. Weight gain, chemical composition, and mycotoxin bioaccumulation of BSFL and spent feed were evaluated. After 12 days of insect culture, the BSFL grew 5–6 times their initial weight. While the overall weights did not significantly vary, the proteins and lipids accumulated more in SSF FDK-fed insects. During the active growth period, the larval biomass contained deoxynivalenol (DON), a mycotoxin, at detectable levels; however, by day 12, when the larvae were in the pre-pupal stage, the amount of DON in the insect biomass was nearly negligible, i.e., BSFL did not accumulate DON. Thus, we conclude that the combination of BSFL and SSF can be employed to recover DON-free nutrients from FHB-infected grain to recover value from unmarketable grain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".