Effects of Killing Methods on Lipid Oxidation, Colour and Microbial Load of Black Soldier Fly (Hermetia illucens) Larvae
Bibliographic record
Abstract
Black soldier fly (BSF) larvae represent a promising alternative ingredient for animal feed. Post-production processing can, however, affect their quality. This project aimed to optimize larval killing by comparing the effects on the nutritional and microbiological quality of 10 methods, i.e., blanching (B = 40 s), desiccation (D = 60 °C, 30 min), freezing (F20 = −20 °C, 1 h; F40 = −40 °C, 1 h; N = liquid nitrogen, 40 s), high hydrostatic pressure (HHP = 3 min, 600 MPa), grinding (G = 2 min) and asphyxiation (CO2 = 120 h; N2 = 144 h; vacuum conditioning, V = 120 h). Some methods affected the pH (B, asphyxiation), total moisture (B, asphyxiation and D) and ash contents (B, p < 0.001). The lipid content (asphyxiation) and their oxidation levels (B, asphyxiation and D) were also affected (p < 0.001). Killing methods altered the larvae colour during freeze-drying and in the final product. Blanching appears to be the most appropriate strategy since it minimizes lipid oxidation (primary = 4.6 ± 0.7 mg cumen hydroperoxide (CHP) equivalents/kg; secondary = 1.0 ± 0.1 mg malondialdehyde/kg), reduces microbial contamination and initiates dehydration (water content = 78.1 ± 1.0%). We propose herein, an optimized protocol to kill BSF that meet the Canadian regulatory requirements of the insect production and processing industry.
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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".