Untargeted Metabolomics Reveals Distinct Soil Metabolic Profiles Across Land Management Practices
Bibliographic record
Abstract
Background/Objectives: Land management practices strongly influence soil biochemical processes, yet conventional soil measurements often overlook dynamic small-molecule variation underlying nutrient cycling and microbial activity. This study aimed to evaluate whether MS1-based untargeted metabolomics can resolve meaningful biochemical differences among soil systems under distinct land management practices. Methods: Soils from six land-use types—conventional cultivation, organic cultivation, pasture, white pine, tulip poplar, and hardwood forest—were analyzed using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Multivariate analyses, including PLS-DA, were performed to evaluate metabolic variation across systems. Both identified metabolites and unknown spectral features (MSI Level 4) were assessed, and biosynthetic class assignment of unknown features was performed using NPClassifier. Results: Metabolic features revealed clear separation between land management systems, demonstrating distinct chemical fingerprints across ecosystems. While conventional elemental ratios (e.g., C/N) showed minimal differentiation, phosphorus-related stoichiometric ratios (C/P and N/P) displayed strong land-use-dependent differences. NPClassifier superclasses highlighted unique chemical patterns, with forest soils enriched in diverse secondary metabolites, cultivated soils characterized by simplified profiles, and pasture soils dominated by microbial membrane lipids and alkaloids. Conclusions: Untargeted MS1-based metabolomics effectively distinguished soil systems under different land-use practices and revealed ecologically meaningful variation even without complete structural identification. This study demonstrates that an MS1-only workflow leveraging unknown spectral features can robustly distinguish soil systems, underscoring their value in untargeted metabolomics analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".