Nitrogen-bearing toxins & the environment : food-safety monitoring systems for the quality assurance of vegetable protein products
Bibliographic record
Abstract
The multitude of food recalls in 2007 clearly demonstrated that total nitrogen-content (ΣN) determination by means of Near Infrared Spectroscopy (NIRS) can be deceived, and should no longer be regarded as a complete quality assurance program for nutritive-protein evaluations. Furthermore, contemporary Canadian-employed analytical tools are precariously limited in their ability to effectively assure a product where there is no a priori knowledge of the environmental toxin(s) involved. In light of these challenges, this study explored a number of new analytical techniques used to assess and furthermore assure the quality of Vegetable Protein Products (VPPs). Using LC/MS/MS/MS technologies, a combination of VPP-based samples were analyzed for the presence of nitrogen-bearing environmental toxins. Of the 117 test-runs, involving an assortment of matrices, Melamine (MEL) and Cyanuric Acid (CYA) were positively identified (> 1 PPM) in 22 and 17, respectively. Subsequent HPLC-UV Amino-Acid-Profiling further confirmed the adulteration of those materials contaminated with Melamine-and-Related-Compounds (MARC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".