Exploring feature selection of St John's wort grown under different light spectra using <sup>1</sup> H‐NMR spectroscopy
Bibliographic record
Abstract
INTRODUCTION: Nuclear magnetic resonance (NMR) spectroscopy combined with multivariate statistical analysis can provide tools to help detect differences in plant chemistry when grown under varying conditions. Hypericum perforatum, or Saint John's wort, plants are a suitable model to explore methods of discrimination between early stage plants grown in different conditions. OBJECTIVES: The purpose of this work was to develop a method for identifying differences in chemical profiles between young Hypericum perforatum plants grown under different lighting conditions. MATERIAL AND METHODS: H-NMR. A multivariate analysis method of the NMR data was developed in an effort to determine variations in chemical profiles. RESULTS: The method identified specific metabolites as drivers of difference between the plants grown under different light conditions. STOCSY (statistical total correlation spectroscopy) and quantification of highlighted metabolites supported the findings of the multivariate analysis. Glutamine, sucrose and fructose were found to be chemical markers of light quality in this study. CONCLUSION: NMR metabolomics using a medium field instrument could find differences in plant chemistry when grown in different conditions. This method could easily be extended to benchtop instruments and be used for crop monitoring and growth condition optimisation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".