Development and validation of a GC–MS method for soybean organ-specific metabolomics
Bibliographic record
Abstract
Field mold (FM) can easily deteriorate the preharvest soybean in the field, and Fusarium moniliforme is demonstrated as the dominant pathogenic fungi. Metabolomics is a powerful tool to reveal the resistance mechanism in response to microbial infection. Therefore, in this research, the Design of Experiment (DOE) model was developed to optimize the extraction solvent combinations for metabolomic study of soybean seed and pod based on gas chromatography–mass spectrometry (GC–MS). Combined with the number of extracted peaks and the peak area of common substances, the extraction efficiency of different solvent was analyzed by multivariate statistical analysis. The result showed that isopropanol/water/methanol (1:1:1 and 1:1:4, v/v/v) mixture was highly efficient for metabolites extractions of soybean seed and pod, respectively. Additionally, the potential metabolites and pathways concerned in FM resistance were explored by the optimized extraction solvent system based on metabolomics analysis. Amino acid metabolism in soybean seed was disturbed by F. moniliforme and metabolic pathways related to energy conversion in soybean pod strongly responded to fungal infection. This study constructs a GC–MS-based metabonomic method for soybean metabolites; comparative analysis of organ-specific metabolomics for soybean fruit could be further applied in soybean metabolomics researches.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".