Foliar-Selenium-Induced Modulation of Volatile Organic Compounds in Rice Grains: A Comparative Study of Sodium Selenite and Nano-Selenium
Bibliographic record
Abstract
Rice aroma is influenced by many factors, including selenium (Se) fertilizer. In this study, we investigated the effects of different Se species on the volatile organic compounds (VOCs) in three indica rice varieties over 2022 and 2023 by forliar spray. The VOCs were analyzed using HS-SPME-GC-MS. The results showed that both Se nanoparticles (SeNPs) and sodium selenite (Na2SeO3) significantly increased the contents of most VOCs in all three varieties, with SeNPs exhibiting a more pronounced effect. PCA and OPLS-DA revealed distinct clustering of the VOCs based on Se treatments and rice varieties. By variable importance in projection (VIP) analysis with FDR correction, Na2SeO3 yielded 7 markers, whereas SeNP treatment identified 18. Every marker detected under Na2SeO3 was fully encompassed within the SeNPs set. Three-factor ANOVA indicated that there are significant interaction effects among Se species, rice variety, and planting year. Additionally, the effect sizes were evaluated in the key VOCs to quantify the effect of Se species, rice variety, and planting year. The findings highlight Se fertilizers to enhance rice aroma and suggest selecting appropriate Se species and rice varieties for aroma improvement.
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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".