Volatile metabolite profiling to detect and discriminate stem‐end rot and anthracnose diseases of mango fruits
Bibliographic record
Abstract
The volatile metabolites from the headspace gas of containerised mango ( Mangifera indica ) cv. Tommy Atkins fruits, surface wounded and inoculated with the two fungal anamorphic pathogens Colletotrichum gloeosporioides and Lasiodiplodia theobromae , or non‐inoculated (controls), were profiled using a portable gas chromatograph/mass spectrometer to discriminate diseases of mango. Thirty‐four compounds were detected relatively consistently among replicates. Several of these were disease/inoculation‐discriminatory and were classified into three groups: (i) compounds unique to only one treatment; (ii) compounds common to two or more treatments, but not to all; and (iii) compounds common to all treatments, but varying in their abundance. Two compounds, 1‐pentanol and ethyl boronate, were detected in L. theobromae‐ inoculated mangoes alone, while thujol was observed only in C. gloeosporioides‐ inoculated mangoes. Discriminant analysis models based on the abundance of significant mass ions and consistent compounds correctly classified diseases/inoculations in up to 100% of cases. The disease‐discriminatory compounds and discriminant analysis models developed here have the potential to be used in the early detection of postharvest diseases of mango fruits after validation under commercial conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".