Chemical profiling and separation of bioactive secondary metabolites in Maca (<scp><i>Lepidium peruvianum</i></scp>) by normal and reverse phase thin layer chromatography coupled to desorption electrospray ionization‐mass spectrometry
Bibliographic record
Abstract
Abstract Maca is a Peruvian tuberous root of the Brassicaceae family grown in the central Andes between altitudes of 4000 and 4500 m. The medicinal plant is a nutraceutical with important biological activities and health effects. In this study, we report a rapid high‐performance thin layer chromatography (HPTLC)‐(−)desorption electrospray ionization (DESI)‐mass spectrometry (MS) method to profile and separate intact glucosinolates without prior biochemical modifications from the hydromethanolic extracts of two phenotypes, red and black Maca ( Lepidium peruvianum ) seeds. In the first stage of the plant's life cycle, aromatic glucosinolates were the main chemical constituents whereby six aromatic, three indole, and one aliphatic glucosinolate were tentatively identified. At the seedling stage, glucolepigramin/Glucosinalbin was the most predominant precursor, rather than Glucotropaeolin, which is mainly found in hypocotyls and roots. These findings lead us to suggest that glucolepigramin/glucosinalbin play a major role as active precursors in the biosynthetic pathways of other secondary metabolites in the early stages of plant development. Between red and black Maca seeds, only minor differences in the relative abundances of glucosinolates were observed rather than different plant metabolites. For the first time, we report six potential plant antibiotics, phytoanticipins: glycosylated ascorbigens and dihydroascorbigens from Maca seeds. We also investigated a targeted reverse phase C 18 functionalized TLC‐DESI‐MS method with high sensitivity and specificity for Brassicaceae fatty acids in Maca seeds and health supplements such as black Maca root lyophilized powder and tinctures. The investigation of secondary metabolites by normal and reverse phase TLC‐DESI‐MS methods, described in this study, can aid in their identification as they begin to emerge in later stages of development in plant tissues such as leaves, hypocotyls, and roots.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".