Comparison of Several Methods for the Extraction of DNA from Potatoes and Potato-Derived Products
Bibliographic record
Abstract
Eight methods were compared for the extraction of DNA from raw potato tubers, and nine methods were evaluated for the extraction of DNA from dehydrated potato slices, potato flakes, potato flour, potato starch, and two ready-to-eat potato snack foods. Extracts were assessed for yield using a fluorescence-based DNA quantification assay. Real-time amplification of an endogenous gene, sucrose synthase (sus), was used to assess extract and template quality. A CTAB-based method extracted the highest DNA yields from the tuber material. An in-house method, which utilized the Kingfisher magnetic particle processor, yielded the highest template quality from the tubers. For most of the tuber samples, the Kingfisher and CTAB methods recovered the highest levels of amplifiable sus. DNA yields for potato-derived foods generally decreased with the extent that the product had been processed. The methods that utilized the magnetic particle processor delivered the highest template quality from one of the snack products that was particularly high in fat. For most of the remaining processed products, the levels of amplifiable target DNA recovered were roughly correlated with total DNA recovery, indicating that overall yield had greater influence over sus amplification than template quality. The Wizard method was generally the best method for the extraction of DNA from most of the potato-derived foods.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".