Characterization and differentiation of quinoa seed proteomes by label-free mass spectrometry-based shotgun proteomics
Bibliographic record
Abstract
Quinoa seed proteins are of prime importance in human nutrition and in plant breeding for cultivar identification and improvement. In this study, proteins from seeds of black, red, white quinoa from Peru and white quinoa from Bolivia (also known as royal) were extracted, digested and analyzed by nano-liquid chromatography coupled to Orbitrap tandem mass spectrometry (LC-MS/MS). The raw mass spectra data were processed for identification and label-free quantification (LFQ) using MaxQuant/Andromeda against a specific quinoa database from The National Center for Biotechnology Information (NCBI). In total, 1,211 quinoa proteins (85 were uncharacterized) were identified. Inspection and visualization using Venn diagrams, heat maps and Gene Ontology (GO) graphs revealed proteome similarities and differences between the four varieties. The presented data provides the most comprehensive experimental quinoa seed proteome map existing to date in the literature, as a starting point for more specific characterization and nutritional studies of quinoa and quinoa-containing foodstuff.
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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 | 0.000 |
| 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) | 0.000 | 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".