An innovative strategy to accurately quantify protein content in insect meals
Bibliographic record
Abstract
Abstract The nitrogen-to-protein conversion factor is widely used to estimate crude protein content in edible insect meals. However, reported values vary widely across studies, raising concerns about their reliability. This study investigates using an alternative conversion factor, , applied to protein-derived nitrogen, to more accurately measure the true protein content in insect meals. Ten commercial insect meals, including five cricket-based ( Acheta domesticus , Gryllodes sigillatus ) and five mealworm-based ( Tenebrio molitor ) meals marketed in Canada were analysed. The and factors were calculated from amino acid profiles, and nitrogen distribution was determined. The non-protein and non-chitin nitrogen (N NP,NC ) was removed through protein precipitation using trichloroacetic acid and acetone. Then, protein nitrogen (N P ) was separated from chitin-bound nitrogen (N C ) through chemical deproteinisation. While values varied significantly between the commercial meals, values were consistent, averaging 5.61 for cricket meals and 5.69 for mealworm meals. Protein nitrogen constituted approximately two-thirds of the total nitrogen in the meals, while the N NP,NC fraction represented 13-24% of the total nitrogen, and varied significantly, even among products from the same species. An additional 8-17% of the total nitrogen remained uncharacterised. Using the conventional value of 6.25 resulted in a 35-46% overestimation of protein content compared with using applied to N P , while the commonly accepted insect meal-specific value of 4.76 still led to a 15-29% overestimation. These findings support using factors (5.61 for orthopterans, 5.69 for coleopterans) applied to protein-derived nitrogen as a more accurate approach to quantify proteins in insect-based ingredients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".