1-Methylcyclopropene Counteracts Fry Color Darkening Attributable to Carbon Dioxide and Ethylene Interaction
Bibliographic record
Abstract
The fry color of potatoes ( Solanum tuberosum L.) stored for processing remains an important quality characteristic that can be affected by many factors, including ethylene gas from various sources and the interaction of very low concentrations of ethylene gas (less than 1 μL·L −1 ) and accumulated CO 2 . Because previous studies show that pretreatment with 1-methylcyclopropene (1-MCP) can substantially reduce fry color darkening attributable to applied ethylene, we hypothesized that 1-MCP could also reduce fry color darkening attributable to the interaction of ethylene and CO 2 . Trials were conducted over two storage seasons, using ‘Russet Burbank’ tubers, either untreated or treated with 0.5 μL·L −1 ethylene gas ± 2 kPa CO 2 and ± 1-MCP. Tubers exposed to ethylene gas had darker fry color than untreated tubers, whereas the fry color of tubers exposed to ethylene plus CO 2 was darker still. However, the fry color of tubers pretreated with 1-MCP was as light as that of the untreated tubers. This provides a potential new tool for the potato industry to manage potato fry color of stored processing potatoes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".