Cultivated Winter-Type Lunaria annua L. Seed: Deciphering the Glucosinolate Profile Integrating HPLC, LC-MS and GC-MS Analyses, and Determination of Fatty Acid Composition
Bibliographic record
Abstract
Lunaria annua L. (Brassicaceae) is an ornamental plant newly identified in Europe as a promising industrial oilseed crop for its valuable very-long-chain monounsaturated fatty acids (MUFAs), especially erucic acid (EA) and nervonic acid (NA). L. annua seeds were obtained from annual winter-type plants selected and cultivated in Northern France. Using a systematic multiple-method approach, we set out to determine the profile and content of glucosinolates (GSLs), which are the relevant chemical tag of Brassicaceae. Intact GSLs were analyzed through a well-established LC-MS method. Identification and quantification were performed by HPLC-PDA of desulfo-GSLs (dGLs) according to the official EU ISO method. Moreover, GSL structures were confirmed by GC-MS analysis of the related isothiocyanates (ITCs). Seven GSLs were identified, directly or indirectly, as follows: 1-methylethyl GSL, (1S)-1-methylpropyl GSL, (Rs)-5-(methylsulfinyl)pentyl GSL, (Rs)-6-(methylsulfinyl)hexyl GSL, (2S)-2-hydroxy-4-pentenyl GSL, 2-phenylethyl GSL, and 1-methoxyindol-3-ylmethyl GSL. In other respects, the FA composition of the seed oil was determined. Results revealed cultivated L. annua seed to be a source of NA-rich oil, and presscake as a valuable coproduct. This presscake is indeed rich in GSLs (4.3% w/w), precursors of promising bioactive molecules for agricultural and nutraceutical applications.
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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 | 0.000 | 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".