Comprehensive Characterization of Raw and Processed Quinoa from Conventional and Organic Farming by Label-Free Shotgun Proteomics
Bibliographic record
Abstract
Quinoa is widely recognized for its exceptional nutritional properties, particularly its complete protein content. This study, for the first time, investigates the effects of processing methods (boiling and extrusion) and farming conditions (conventional and organic) on the proteomic profile. Following a label-free shotgun proteomics approach, a total of 1796 proteins were identified and quantified across all quinoa samples. Regarding processing, both boiling and extrusion produced protein extracts with lower total protein content, with the number of identified proteins decreasing from 1695 in raw quinoa to 957 in processed quinoa. Boiling led to a reduction in protein diversity and expression, while extrusion, which involves high temperatures and pressures, specifically decreased the abundance of high molecular mass proteins. Concerning cultivation practices, organic farming was associated with a broader protein diversity, especially proteins related to translation (28 vs 5%), while conventional farming showed a higher abundance of catalytic and enzymatic proteins (67 vs 46%). These findings highlight the distinct proteomic changes induced by different processing methods and farming conditions, offering valuable insights to manage quinoa's nutritional, bioactive, and functional properties across various production practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".