Determination of Bioactive Compounds Against Bacterial Wilt of Potato (Ralstonia pseudosolanacearum sp. nov.) in Psidium guajava and Pelargonium zonale Leaf Extracts
Bibliographic record
Abstract
Common guava (Psidium guajava) and Pelargonium (Pelargonium zonale) have shown in-vitro antibacterial activity against Ralstonia pseudosolanacearum sp. nov. in previous studies. However, their phytochemical constituents and bioactive compounds against the pathogen have not been identified. The present study investigated the phytochemical components of P. guajava and P. zonale leaf extracts by phytochemical screening and gas chromatography-mass spectrometry (GC-MS). Phytochemical screening was done using different solvents while 100 mg of the dried ethanolic extract pastes from each plant sample was subjected to GC-MS analysis. Automated mass spectral deconvolution and identification system software (AMDIS, US) was used to analyze chromatograms and spectra representing individual compounds. Compound identification was performed by comparing each of the mass spectra with the database of NIST 11 (Gaithersburg, MD, USA), Wiley 7N (John Wiley, NY, USA) and by comparing the calculated Kovats linear retention indices using retention times of n-alkane series against the values in the NIST webbook. Flavonoids, phenols, alkaloids, saponins, terpenoids and tannins were detected in both plant samples. GC-MS analysis revealed presence of 35 and 26 compounds from P. zonale and P. guajava respectively. Both P. zonale and P. guajava had 7 similar compounds with antibacterial properties; Fumaric acid, Phytol, Pyrogallol, 4-Hydroxybenzoic acid, Shikimic acid, Protocatechuic acid and 3, 4, 5-Trihydroxybenzoic acid ethyl ester but P. zonale had one additional antibacterial compound; Lactic acid. In both cases, Shikimic acid had the highest percent peak areas of 3.2% for P. zonale and 6.8% P. guajava respectively. Therefore, P. zonale and P. guajava can serve as alternative sources of active ingredients for formulation of commercial botanicals for the management of bacterial wilt of potato.
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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) | 0.000 | 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 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".