Variation for caffeic acid and phenolic content in different plant parts of Solanum xanthocarpum Schrad. and Wendl. – a commercially important dashmool species
Bibliographic record
Abstract
Abstract Background Environmental factors have profound effect on quantity vis-a-vis quality of phytochemicals in medicinal plants.Solanum xanthocarpumSchrad. and Wendl. is among the 10 dashmool species which is utilized in more than hundreds of Ayurvedic preparations including ‘Dashmoolarishta’. Phenolics are the pharmacologically valuable compounds. Therefore, the present study was undertaken to assess the total phenolic (TP) and Caffeic acid (CA) contents in four different plant parts i.e., leaves, fruits, stem and roots ofS. xanthocarpumsampled randomly from different locations of Madhya Pradesh, a central Indian state. Methods Plant samples were collected from 99 places of 29 districts falling in 11 agroclimatic regions of Madhya Pradesh through random sampling. UV-VIS spectrophotometer and HPTLC were used to determine TP and CA contents, respectively. Phytochemical screening was carried out using standard methods. Results Preliminary phytochemical screening indicates the presence of alkaloids, cardiac glycosides, flavonoids, phenols, steroids and terpenoids in all plant parts. Quantification of TP and CA contents revealed that both varied significantly between agroclimatic zones as well as within plant parts ofS. xanthocarpum. Results revealed that among analysed plant parts, roots and stem harbored highest content of CA while fruits and leaves had the highest TP content. Among agroclimatic regions, accessions of Satpura plateau can be considered rich in CA and TP contents for fruits (0.030%; 28.70 mg CE/g), leaves (0.058%; 27.90 mg CE/g) and roots (0.161%; 5.17 mg CE/g). For stem, highest CA (0.100%) and TP (13.23 mg CE/g) contents were observed in samples of Malwa Plateau and Central Narmada Valley, respectively. Conclusion We conclude that agroclimatic regions have significant effect on studied phytochemicals and Satpura plateau agroclimatic zone may be targeted for conservation and sustainable utilization of this valuable dashmool species if the target plant parts are fruits, leaves and roots. While, Malwa Plateau and Central Narmada Valley zones may be targeted for stem. Further, fruits and roots may be utilized for extraction of TP compounds and CA respectively.
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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 | 0.001 |
| 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) | 0.001 | 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".