Fast detection of leaf pigments and isoprenoids for ecophysiological studies, plant phenotyping and validating remote‐sensing of vegetation
Bibliographic record
Abstract
Rapid developments in remote‐sensing of vegetation and high‐throughput precision plant phenotyping promise a range of real‐life applications using leaf optical properties for non‐destructive assessment of plant performance. Use of leaf optical properties for assessing plant performance requires the ability to use photosynthetic pigments as proxies for physiological properties and the ability to detect these pigments fast, reliably and at low cost. We describe a simple and cost‐effective protocol for the rapid analysis of chlorophylls, carotenoids and tocopherols using high‐performance liquid chromatography ( HPLC ). Many existing methods are based on the expensive solvent acetonitrile, take a long time or do not include lutein epoxide and α‐carotene. We aimed to develop an HPLC method which separates all major chlorophylls and carotenoids as well as lutein epoxide, α‐carotene and α‐tocopherol. Using a C 30 ‐column and a mobile phase with a gradient of methanol, methyl‐tert‐butyl‐ether ( MTBE ) and water, our method separates the above pigments and isoprenoids within 28 min. The broad applicability of our method is demonstrated using samples from various plant species and tissue types, e.g. leaves of Arabidopsis and avocado plants, several deciduous and conifer tree species, various crops, stems of parasitic dodder, fruit of tomato, roots of carrots and Chlorella algae. In comparison to previous methods, our method is very affordable, fast and versatile and can be used to analyze all major photosynthetic pigments that contribute to changes in leaf optical properties and which are of interest in most ecophysiological studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".