Chemotaxonomic Profiling of Canadian Alternaria Populations Using High-Resolution Mass Spectrometry
Bibliographic record
Abstract
Alternaria spp. occur as plant pathogens worldwide under field and storage conditions. They lead to food spoilage and also produce several classes of secondary metabolites that contaminate the food production chain. From a food safety perspective, the major challenge of assessing the risk of Alternaria contamination is the lack of a clear consensus on their species-level taxonomy. Furthermore, there are currently no reliable DNA sequencing methods to allow for differentiation of the toxigenic potential of these fungi. Our objective was to determine which species of Alternaria exist in Canada, and to describe the compounds they make. To address these issues, we performed metabolomic profiling using liquid chromatography high-resolution mass spectrometry (LC-HRMS) on 128 Canadian strains of Alternaria to determine their chemotaxonomy. The Alternaria strains were analyzed using principal component analysis (PCA) and unbiased k-means clustering to identify metabolites with significant differences (p < 0.001) between groups. Four populations or ‘chemotypes’ were identified within the strains studied, and several known secondary metabolites of Alternaria were identified as distinguishing metabolites, including tenuazonic acid, phomapyrones, and altenuene. Though species-level identifications could not be concluded for all groups through metabolomics alone, A. infectoria was able to be identified as a distinct population.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".