Allelochemicals Identified From Sweet Potato (Ipomoea batatas) and Their Allelopathic Effects on Invasive Alien Plants
Bibliographic record
Abstract
Sweet potato [Ipomoea batatas (L.) Lam] is grown as important cash and food crop worldwide and has been shown to exhibit allelopathic effects on other plants. However, its metabolome has not been studied extensively, particularly with respect to the production of phytotoxic bioactive secondary products. In this study, the chemical composition of petroleum ether extract of sweet potato was characterized, and the morphological and physiological effects of some individual components against four invasive alien weeds Bidens pilosa L., Galinsoga parviflora Cav., Lolium multiflorum Lam., and Phalaris minor Retz. were determined. Twenty-one components were identified by GS-MS, constituting 96.08% of petroleum ether extract in sweet potato. The major components were palmitic acid (PA) (17.48%), ethyl linoleate (EL) (13.19%), linoleic acid (LA) (12.55%), ethyl palmitate (EP) (11.77%), ethyl linolenate (ELL) (8.29%) oleic acid (5.82%), ethyl stearate (4.19%), and 3-methylphenol acetate (3.19%). The five most abundant compounds exhibited strong inhibition activity against the four invasive weeds tested. The highest inhibition rates were seen for LA, followed by PA and EP, respectively. Catalase (CAT), malondialdehyde (MDA), and peroxidase (POD) content of L. multiflorum were increased by the three allelochemicals, i.e., LA, PA and EP, but superoxide dismutase (SOD), chlorophyll-a and chlorophyll-b levels declined. Overall, the combined impact of all five compounds could be quite effective in suppressing the invasive weeds of concern.
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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) | 0.001 | 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".