Impacts of Protease Treatment on the Contents of Tocopherols and B Vitamins in Peanuts
Bibliographic record
Abstract
This study investigated the changes of tocopherols and B vitamins in raw peanuts as a result of protease treatment which was used to reduce peanut allergens. Raw peanut kernels were treated with Alcalase, bromelain, Neutrase and papain separately at different concentrations, vacuum dried, and then ground into paste. The paste was defatted using hexane containing 0.02% BHT to obtained crude oil and defatted peanut flour which were used for tocopherol and B-vitamin analysis, respectively. The protease treatment significantly reduced the contents of all tocopherols and B-vitamins in the peanuts in enzyme concentration-dependent manner (P<0.0001). The highest losses of α-, γ-, and δ-tocopherols were 60.87 %, 40.60 % and 36.89 %, respectively, while the maximum losses of vitamins B1, B2, B3 and B6 were 63.29 %, 44.83 %, 40.56 % and 49.59 %, respectively. Among tocopherols, α-tocopherol was the most affected while δ-tocopherol was the least affected. Among B-vitamins, B1 was the most affected and B3 the least affected. This study demonstrated that although the protease treatment approach (including enzyme treatment and drying) for peanut allergen reduction resulted in different degrees of losses in tocopherols and B vitamins in raw peanuts, the enzyme treated peanuts is still a good source of tocopherols and vitamin B3 comparing to most cooked legumes and vegetable.
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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 | 0.001 |
| 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".