Analysis of cruciferin content in whole seeds of <scp><i>Brassica napus</i> L</scp>. by <scp>near‐infrared</scp> spectroscopy
Bibliographic record
Abstract
Abstract Globally, there is an increasing demand for sources of plant‐based protein. While Brassica napus L. is an important oilseed crop worldwide, there is also interest in improving its ability to serve as a valuable source of plant‐based protein. Cruciferin, a seed storage protein that makes up 60% of the protein found in mature seeds of B. napus, is of interest for human consumption as a source of protein and as an ingredient in food products due to its functional properties. Existing methods for quantification of cruciferin protein are often time consuming and destroy the seed. This study explored the potential for the measurement of cruciferin protein content in whole seeds of B. napus by near‐infrared spectroscopy (NIRS), to allow for efficient and non‐destructive screening of breeding material. An enzyme‐linked immunosorbent assay (ELISA)‐based reference method was utilized to assess cruciferin content in a diverse population of B. napus. Scanning of whole seed samples produced spectra that were used to develop NIRS calibration equations. Statistical analysis of the calibration results indicated that the NIRS equations developed are poorly suited for prediction of cruciferin content.
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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".