Bioactive Components in <i>Moringa Oleifera</i> Leaves and Their 3D Excitation-Emission Matrices, IR and Raman Spectra
Bibliographic record
Abstract
Background Moringa oleifera has long been cultivated in various parts of the world and has proven beneficial properties and applications . This study aimed to explore the potential of non-destructive techniques (Raman and infrared spectroscopy) to determine the qualitative metabolic composition of Moringa oleifera leaves. Methods Gas chromatography was used to study the extract's chemical composition. Infrared, Raman, and fluorescence spectroscopy were used to analyze the leaf extract's optical properties and chemical groups. Results Selective fluorescence excitation at different wavelengths was employed to obtain the excitation-emission matrices of the ethanol and methanol extracts of the plant. The excitation-emission matrices reveal two distinct regions: • The first was in the 450–600 nm range. • The second was in the 650–775 nm range. Infrared spectroscopy identified peaks in the 3450–3200 cm −1 region, associated with hydroxyl groups, and around 2900 cm −1 , linked to aliphatic C-H and CH 2 vibrations, characteristic of fatty acids in the leaves of Moringa oleifera . The peak at 1625 cm −1 is related to the C = O stretching vibrations (lignin structures). Peaks coresponding to aromatic rings and others were also observed. Raman spectroscopy detected vibrations of methyl and methylene groups, C-C stretching in acyclic residues, C-C aldehyde vibrations, etc. Conclusion The results demonstrated that using these three techniques in tandem provides excellent results for the qualitative composition of plants. Excitation-emission matrices can be used in future studies to obtain fingerprints of plant extracts from different regions of the world by excitation in the blue part of the visible spectrum.
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 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 | 0.000 | 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) | 0.001 | 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".