Effect of nanopesticides (azoxystrobin and bifenthrin) on the phenolic content and metabolic profiles of strawberries (<i>Fragaria</i> × <i>ananassa</i>)
Bibliographic record
Abstract
Abstract BACKGROUND Nanoencapsulation has opened promising fields of innovation for pesticides. Conventional pesticides can cause side effects on plant metabolism. To date, the effect of nanoencapsulated pesticides on plant phenolic contents has not been reported. RESULTS In this study, a comparative evaluation of the phenolic contents and metabolic profiles of strawberries was performed for plants grown under controlled field conditions and treated with two separate active ingredients, azoxystrobin and bifenthrin, loaded into two different types of nanocarriers (Allosperse® polymeric nanoparticles and SiO2 nanoparticles). There were small but significant decreases of the total phenolic content (9%) and pelargonidin 3‐glucoside content (6%) in strawberries treated with the nanopesticides. An increase of 31% to 125% was observed in the levels of gallic acid, quercetin, and kaempferol in the strawberries treated with the nanoencapsulated pesticides compared with the conventional treatments. The effects of the nanocarriers on the metabolite and phenolic profiles was identified by principal component analysis. CONCLUSION Overall, even though the effects of nanopesticides on the phenological parameters of strawberry plants were not obvious, there were significant changes to the plants at a molecular level. In particular, nanocarriers had some subtle effects on plant health and fruit quality through variations in total and individual phenolics in the fruits. Further research will be needed to assess the impact of diverse nanopesticides on other groups of plant metabolites. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 | 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".