Untargeted Metabolomics of Plant Samples using HPLC-DAD and Gaussian Mixture Models
Bibliographic record
Abstract
Abstract/Summary Premise Plants produce millions of different chemical compounds, contributing greatly to their physiology and evolutionary trajectories. Most untargeted metabolomic methods are inaccessible, either due to upfront instrument costs or intensive technical training. More accessible methods using diode array detectors often only utilize a few wavelengths, preventing high-throughput observation of total metabolic diversity. Methods Leaves from the genera Betula , Magnolia, Rosa, and Viburnum were collected, dried and ground, extracted, and analyzed by HPLC-DAD. Chromatographic data was then processed in a curated R pipeline, and resulting resolved peaks were clustered by absorbance spectra using Gaussian Finite Mixture Models (GMMs). To assess clustering, GMM was compared to a more traditional linear discriminant analysis (LDA) method, with clusters identified through literature searches. Results Significant associations between the abundances of chemical classes and whole-metabolome alpha and beta diversity indices were recovered. In general, GMMs performed better than other classification methods like LDA, especially between classes that share common features like non-flavonoid phenolics and flavonoids. Discussion We show that our method can easily extract relevant class-level diversity of metabolite profiles among closely related species, genotypes, and ecotypes. Regardless of underlying research question, our method extends the usage of DAD beyond restricted targeted analyses and increases the accessibility of untargeted metabolomics.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".