Volatile Compound Profiles of Raw and Roasted Peanut Seeds of the Runner and Virginia Market-types
Bibliographic record
Abstract
The unique flavor of peanuts that develops during roasting is the primary driving force for the consumption of peanut products. Although rarely consumed raw, the raw state of the peanut contains the precursors involved in the transformations that lead to the distinct flavor development in roasted peanuts. Volatile compounds extracted from the headspace above raw and roasted peanut samples of the runner and virginia market types by solid phase microextraction were characterized using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. The roasting treatment and peanut market-type each had a significant impact on the types and concentrations of small molecular weight compounds found. Among 361 sample components detected, 290 compounds were found to be significantly different between the raw and roasted treatments (p < 0.05). The roasted samples contained pyrazines, pyrroles, thiazoles, and furans. Alcohols were the primary compounds found in the raw peanut samples. Additionally, 107 compounds were found to differ significantly between roasted runner and virginia-type peanuts. Virginia-type peanuts contained higher levels of linoleic acid oxidation products, such as 2-octenal, hexanal, and 1-octen-3-one. More significant distinctions in volatile compounds were recognized between runner and virginia market types than previously observed. In total, this study reported 119 volatile compounds that have not previously been reported in roasted peanuts, including 11 furans, seven pyrroles, five pyridines, and 12 pyrazines.
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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 | 0.000 |
| 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) | 0.001 | 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".