Application of Mass Spectrometry Imaging in Evaluating the Spatial Distribution of Aminoacids and Sugars in Basil Leaves upon Long-Time Exposure to Cadmium
Bibliographic record
Abstract
Basil samples (Ocimum basilicum Lameaceae) were exposed to cadmium and analyzed on porous PTFE membrane, and TLC plate substrates by desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for amino acids and sugars identification. The TLC plate was the best substrate for analysis of the basil leaves, with high-definition images, small extract scattering, low mass deviations, and excellent reliability in the spatial distribution of the analytes. DESI-MSI analysis identified 13 images of ions putatively annotated as amino acids and sugars with high accuracy (mass deviation between -1.97 to 1.42 ppm) in contaminated and non-contaminated leaves. In general, the amino acids and sugars (proline, histidine, glutamine, arginine, homoarginine, theanine, hexose sugars, and disaccharides) accumulated preferably in basil leaves as a defense mechanism against exposure to cadmium. Asparagine, tyrosine, glutamic acid, and phenylalanine were inhibited when exposed to the toxic element. The images obtained in this study demonstrated the spatial distribution and accumulation of amino acids and sugars in basil leaves as a response to cadmium contamination, confirming that DESI-MSI is a valuable and promising tool for metabolomics studies in plants exposed to toxic metals.
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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) | 0.001 | 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".