High‐performance thin‐layer chromatography/desorption electrospray ionization mass spectrometry imaging of the crude extract from the peels of <i>Citrus aurantium</i> L. ( <i>Rutaceae</i> )
Bibliographic record
Abstract
RATIONALE: Citrus aurantium L. is a plant belonging to the Rutaceae family, whose extracts are extensively used in weight management products and as thermogenic agents. Here we present two methodologies to analyse the extracts obtained from the peels of Citrus aurantium L. that usually require multiple sample preparation and detection steps. METHODS: Polar compounds of the crude extract from the peels of Citrus aurantium L. (Rutaceae) were investigated by direct infusion electrospray ionization mass spectrometry (ESI-MS) and high-performance thin-layer chromatography (HPTLC) coupled to desorption electrospray ionization mass spectrometry (DESI-MS). ESI-MS was performed in both positive and negative ion modes. Molecular imaging of the HPTLC plates was used for the direct analysis of the phytocompounds present in the crude extract from the peels of Citrus aurantium L. by DESI-MS imaging. RESULTS: Characteristic mass spectra with many diagnostic ions were obtained from the extract analysis, allowing a fast and reliable identification of these species. Tandem mass spectrometry (MS/MS) was employed to confirm the identity of specific metabolites. CONCLUSIONS: HPTLC/DESI-MS imaging is a relatively fast, versatile, and efficient technique for natural product analysis, since many more ions are observed than with the direct infusion ESI-MS. The MS/MS technique provided information about the component structures, revealing the presence of important bioactive components. The application of DESI-MS imaging may contribute to the improvement identification and characterization of pharmacologically active compounds in phytochemistry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".