Comparison of black soldier fly larvae pre-treatments and drying techniques on the microbial load and physico-chemical characteristics
Bibliographic record
Abstract
Black soldier fly larvae (BSFL) are good candidates for upcycling wet organic residuals. Like other unprocessed raw animal products, BSFL require processing to prevent spoilage and degradation during storage and to facilitate their use as feed ingredients. In this study, hot-air drying and freeze-drying were examined as means to ensure long-term preservation. Pre-treatments of larvae, such as puncturing, blanching (40 s) and scalding (2, 4, 6 and 8 min) in boiling water reduced drying times, most likely by affecting the integrity of the wax-coated cuticle that protects the larvae against desiccation. Overall, the larvae dried two to six times faster using hot-air compared to freeze-drying, and larvae pre-treatments were proven to effectively improve drying efficacy. Pre-treating larvae in boiling water followed by a shorter drying time with hot air was effective at reducing primary and secondary oxidation as well as darkening/browning (colour lightness, L* value) compared to the untreated control (raw-thawed) larvae. The larvae pre-treatments in boiling water also led to a significant reduction in microbial load (3.21 to 4.83 log) in the dry product compared to the control. BSFL powder, produced from grinding thawed larvae that were pre-treated for 4 min in boiling water before being dried in hot air (60 °C; 6 h), had a water activity below 0.4. This led to a relatively stable product with limited colour changes over a 30-day storage period. These processing treatments also resulted in a product with no detectable Salmonella and Escherichia coli counts ranging from 100 to 1000 cfu/g. Overall, the powdered BSFL product was deemed suitable to incorporate into pelleted feed under the current regulations in Canada.
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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".