Toxic Analysis of Leaf Protein Concentrate Regarding Common Agricultural Residues
Bibliographic record
Abstract
Background: Potential resilient foods which help reduce hunger are converting the ~998 million tons of agricultural residue generated each year into human edible food. Although it is possible to extract Leaf Protein Concentrate (LPC) from agricultural residues, it is not widely practiced because both toxicity and yields of the protein concentrates have not been widely investigated in the most common agricultural residues.Methods: To fill this knowledge gap, this study uses high-resolution mass spectrometry and an open-source toolchain for non-targeted screening of toxins of nine agricultural plant residues in October 2021; it included seven agricultural residues: corn/maize, wheat, barley, alfalfa, yellow pea, sunflower, canola/rapeseed, and two weeds/agricultural residues of kochia, and round leaf mallow.Results: The average yield ranged from about 7 to 14.5% for the nine LPCs investigated. According to the results, yellow pea, round leaf mallow, and canola are recommended for further investigation and scaling as they appear to be fit for human consumption based on the lack of dangerous toxins found in the analysis performed in this study.Conclusion: All the compounds identified in these samples have either been approved by international regulatory boards for safe consumption or are known to be present in common beverages. The other agricultural residues require additional quantification of the toxins identified as it will determine the actual risk for human consumption. Overall, the potential for LPC to provide more needed calories from existing agricultural practices is extremely promising, but substantial amount of future work is needed to screen LPCs in all the agricultural residues depending on harvesting, handling, and storage conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".