Fortification of Cookies with Peanut Skins: Effects on the Composition, Polyphenols, Antioxidant Properties, and Sensory Quality
Bibliographic record
Abstract
Food fortification may be carried out to improve the health status of consumers. In this study, peanut skins were added at 1.3, 1.8, and 2.5% to cookies to increase their polyphenol content. Insoluble fiber was increased by up to 52%. In addition, total phenolic content and the corresponding antioxidant capacities also increased as evidenced by increases of epicatechin and procyanidin dimers A and B. In addition, trimers and tetramers of procyanidins were identified only in peanut skin-fortified cookies. Addition of 2.5% peanut skins rendered an increase of up to 30% in the total polyphenols as evaluated by high-performance liquid chromatography-diode array detection-electrospray ionization multistage mass spectrometry (HPLC-DAD-ESI-MS(n)). Sensory evaluation results demonstrated that peanut skin-fortified cookies were well accepted, which suggests that the present formulation may lend itself for commercial exploitation.
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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 | 0.000 |
| 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".