Detection of Leguminous Protein in Casein by Nitrogen Stable Isotopes
Bibliographic record
Abstract
Casein can legally be substituted with cheaper leguminous proteins in processed foods, such as imitation cheese, as long as the ingredients are declared. The detection of illegal admixtures of leguminous protein requires analytical procedures to identify an undisclosed adulteration of milk products. To investigate the suitability of stable isotope analyses as an alternative to more sophisticated methods, variations in d15N of soya (n = 27) and peanut (n = 10) protein, as well as casein (n = 14) were determined. Significant differences were established between d15N of casein (4.45 – 6.94‰) and soya (0.25 – 2.37‰) as well as peanut (0.70 – 2.13‰) protein. An average limit of detection for leguminous protein in casein was determined to be 2.1%, provided both components were available for additional analyses. Under practical conditions of food control, i.e., without having access to the actual ingredients, the variability of d15N resulted in an average limit of detection of 26.4%. Because protein d15N can be determined rapidly in crude food samples without elaborate sample preparation, stable isotope analysis can be used as a rapid screening method to determine the presence of higher amounts of leguminous protein in cheese and, in particular, to easily distinguish imitation cheese from cheese.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".