Turmeric powder and its derivatives from Curcuma longa rhizomes: Insecticidal effects on cabbage looper and the role of synergists
Bibliographic record
Abstract
Curcuma longa has well-known insecticidal and repellent effects on insect pests, but its impact on Trichoplusia ni is unknown. In this study, the compound ar-turmerone, extracted and purified from C. longa rhizomes, was identified, and its insecticidal effects, along with turmeric powder, curcuminoid pigments and crude essential oil were evaluated against this important agricultural pest. The role of natural (sesamol and piperonal) and synthetic [piperonyl butoxide (PBO)] synergists under laboratory and greenhouse conditions were also evaluated. The concentration of ar-turmerone in C. longa rhizomes harvested was 0.32% (dwt). Turmeric powder and its derivatives caused 10-20% mortality in third instar T. ni at a very low dose (10 μg/larva). Addition of PBO increased toxicity of turmeric powder and its derivatives (90-97% mortality) in most binary combinations (5 μg of turmeric powder or its derivatives +5 μg of PBO), but neither piperonal nor sesamol were active as synergists. The compound ar-turmerone alone and the combination with PBO reduced larval weight on treated Brassica oleracea in the laboratory and in greenhouse experiments, compared with the negative control. The compound ar-turmerone could be used as a low cost botanical insecticide for integrated management of cabbage looper in vegetable production.
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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) | 0.001 | 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".