Chemical Changes and Oxidative Stability of Peanuts as Affected by the Dry‐Blanching
Bibliographic record
Abstract
Abstract The oxidative changes of peanuts subjected to the dry‐blanching process were evaluated and compared with those of their in‐shell counterparts. In general, the fatty acid profile was not influenced. The content of α‐tocopherol decreased, but the remaining tocopherol homologs were unaffected. Nonanal, an oxidation product of oleic acid, increased. However, the contents of several volatile compounds with potential antioxidant properties were also increased. The higher oxidative stability of dry‐blanched peanuts was demonstrated by accelerated tests as evaluated by peroxide value, thiobarbituric acid reactive substances (TBARS) and the induction period of cold‐pressed oils and this was confirmed by the higher antioxidant properties of oils from such sample as evaluated by the DPPH radical scavenging activity. These results were further confirmed during long‐term storage of dry‐blanched and in‐shell peanuts. The decrease of tocopherols in peanuts due to dry‐blanching did not negatively influence their oxidative stability. In fact, dry‐blanched peanuts showed higher stability as compared with in‐shell peanuts; therefore, we suggest that loss of tocopherol might be less important than the generation of several volatile antioxidant compounds as well as possibly Maillard reaction products upon the dry‐blanching process. These results may be of practical interest to the peanut and peanut oil industries.
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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) | 0.001 | 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) | 0.001 | 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".