Development of a Matrix-assisted laser desorption ionization high resolution mass spectrometry for the quantification of Camalexin and Scopoletin in Arabidospis thaliana
Bibliographic record
Abstract
RATIONALE : Understanding plant defense mechanisms against pathogens is critical for agricultural productivity and crop protection. This study focuses on the quantification of camalexin and scopoletin, essential phytoalexins in Arabidopsis thaliana , using advanced mass spectrometry techniques. Accurate measurement of these compounds can provide insights into plant resistance and support agricultural research. METHODS : Camalexin and scopoletin were quantified using matrix-assisted laser desorption ionization high-resolution mass spectrometry (MALDI-HRMS). The matrix and solvent conditions were optimized to enhance sensitivity and accuracy. MS/MS experiments confirmed the identification with high mass accuracy (mass error < 5 ppm). RESULTS : The method demonstrated high linearity for scopoletin (R 2 = 0.9992) and camalexin (R 2 = 0.9987) over concentration ranges of 0.16-5 µM and 0.31-5 µM, respectively. The limits of detection (LOD) were 0.16 µM for camalexin and 0.04 µM for scopoletin, while the limits of quantification (LOQ) were 0.31 µM for camalexin and 0.16 µM for scopoletin. The average relative standard deviation was 1.43% for scopoletin and 2.46% for camalexin, with average relative errors of 3.91% and 4.11%, respectively. CONCLUSIONS : This study presents a precise, and accurate method for the quantification of key phytoalexins in Arabidopsis thaliana . The developed MALDI-HRMS approach significantly contributes to the understanding of plant defense mechanisms and offers potential applications in agricultural and biotechnological research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".