Effects of Maturity on the Development of Oleic Acid and Linoleic Acid in the Four Peanut Market Types
Bibliographic record
Abstract
The commercialization of high oleic peanut varieties with the fatty acids, oleic and linoleic present in a ratio greater than 9 has increased the shelf stability of many products containing peanuts significantly. With no visual traits to determine levels of the fatty acids present, mixing of the high oleic peanut types from the normal oleic types has been a problem in the peanut supply chain. This study investigated the effect of the development of the fatty acids in peanuts over their maturation with respect to the different market types (Runner, Viriginia, Spanish, Valencia) to determine if the maturation stage of the peanut could be responsible for the presence of normal oleic peanuts in lots of high oleic peanuts and thus decreasing the purity of the lots. Peanuts had different levels of the main fatty acids present as the oil content increased with maturation. Due to the presence of a natural desaturase enzyme in peanuts, oleic acid is converted to linoleic as the peanut develops resulting in a ratio of oleic acid to linoleic acid of 3 or lower in normal oleic peanuts. In peanuts from high oleic cultivars, the genes encoding for this enzyme are mutated or slow to develop. As this gene is activated in the later stages of peanut maturity, this study proves immature peanuts of the high oleic type may not have the proper ratios of oleic to linoleic to ensure shelf stability despite being from high oleic cultivars. This study describes how the concentrations of oleic and linoleic acid changed with maturation of the peanut seeds and affects the purity of individual lots of high and normal oleic types of peanuts. This effect of maturity was seen to be greater in the large seeded Virginia cultivars compared to the smaller seeded market types.
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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 | 0.001 |
| 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 | 0.001 | 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".