Volatile Fingerprinting (SPME-GC-FID) to Detect and Discriminate Diseases of Potato Tubers
Bibliographic record
Abstract
Volatiles from Russet Burbank potatoes inoculated with Erwinia carotovora subsp. carotovora, E. carotovora subsp. atroseptica, Pythium ultimum, Phytophthora infestans, or Fusarium sambucinum were monitored by sampling the head space 3, 4, and 5 days after inoculation, using a solid phase microextraction (SPME) fiber to trap and gas chromatography with flame ionization detector (GC-FID) to fingerprint volatiles. Noninoculated (NON) potatoes served as the control. Volatile fingerprints varied among diseases. Within a disease, the fingerprints varied with time since inoculation and among blocks. In general, more volatiles were observed on the fourth and fifth day after inoculation than on the third day. The amount of volatile compounds produced (peak area) within a disease group increased with incubation time; however, the variation among blocks was much higher. The amount of volatiles produced, in general, was associated with disease severity. Disease-specific volatiles were observed. The F. sambucinum chromatogram had two unique peaks at retention time (RT) = 14.1 and 17.3 min. P. infestans produced few peaks and the profile was quite similar to NON. In contrast, E. carotovora subsp. carotovora, E. carotovora subsp. atroseptica, and Pythium ultimum produced many peaks, and the P. ultimum was different from the bacteria, in that the chromatogram peaks at RT = 4.04 and 8.76 min were absent. Instead, it produced a distinct peak at RT = 1.71 min. E. carotovora subsp. carotovora and E. carotovora subsp. atroseptica couldn't be discriminated based on unique peaks; however, they varied in concentration of volatiles produced. E. carotovora subsp. carotovora produced more of RT = 2.0 min and less of RT = 2.3 and 2.44 min than E. carotovora subsp. atroseptica. A back-propagation network (using neural networks) was developed to classify volatile profiles into six disease-groups. Cross-validation classification probabilities were NON = 71, E. carotovora subsp. carotovora = 71, E. carotovora subsp. atroseptica = 71, P. ultimum = 67, Phytophthora infestans = 46, and F. sambucinum = 75%.
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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".