Differentiation between closely-related Impatiens spp. and regional biotypes of Impatiens glandulifera using a highly-simplified and inexpensive method for MALDI-TOF MS
Bibliographic record
Abstract
Matrix-assisted laser-desorption and ionisation time-of-flight mass spectroscopy (MALDI-TOF MS) is a powerful tool for the characterisation and/or identification of protein-containing samples. Several MALDI-TOF MS sample-preparation methods are currently available but few of these are well suited to the analysis of plant material. We have recently developed a simple, rapid, and relatively-cheap method for MALDI-TOF MS that is applicable to plant material (in addition to microbial and insect material), and our aim in this study was to distinguish between closely-related plant species and/or between regional biotypes within an invasive weed species using this method with a view to optimising the selection of biological control agents that can be used for weed management. We have employed a combination of principal-component analysis and closest-relatedness diagrams derived from MALDI-TOF MS spectral-comparison data to discriminate between the closely-related Impatiens spp. Impatiens noli - tangere , Impatiens parviflora , Impatiens scabrida , Impatiens balsamina , and two regional biotypes of the invasive weed Impatiens glandulifera . We have also developed a method for sample discrimination based upon comparison between blind-test MALDI-TOF MS spectra and reference-sample spectra. Using this latter method, we have been able to discriminate on the basis of the acid-soluble-protein mass spectra generated between four regional biotypes of I. glandulifera that differ in their susceptibility to the biological control agent Himalayan balsam rust ( Puccinia komarovii var. glanduliferae ) using mature leaf material. Using younger leaves, discrimination was not possible between these four regional biotypes. MALDI-TOF MS analysis is able to discriminate between closely-related plant species within the genus Impatiens and between regional biotypes of I. glandulifera . Because of this, MALDI-TOF MS holds great promise for improving weed biological control, a management technique which uses highly-specific co-evolved natural enemies for the control of an invasive non-native plant species, through the optimal matching of biological control agents with susceptible target species/regional biotypes.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".