Inhibitory effect of soy protein coating formulations on walnut (Juglans regia L.) kernels against lipid oxidation
Bibliographic record
Abstract
The aim of this study was to improve the fat stability in walnut ( Juglans regia L.) kernels using an edible coating treatment. Coating solutions were composed of soy protein isolate (SPI), carboxymethylcellulose (CMC) and catechin (CT). Walnuts were dipped in coating solution, dried and stored under abuse temperature condition (35 °C) for 21 days. Lipid oxidation was evaluated by peroxide and thiobarbituric acid reactive substance (TBARS) measurements. Results showed a slight decrease in peroxide values (POV) and a significant reduction of TBARS by coating treatment. The SPI–CT and SPI/CMC–CT coatings were the most effective and decreased the POV by 27 and 31%, respectively, as compared to uncoated walnut after 21 days. The SPI–CT and SPI/CMC–CT coatings also decreased the TBARS value by 16 and 26%, respectively. The incorporation of CT in SPI-based coatings resulted in a synergistic effect on the lipid oxidation preservation. The results of this study show that soy protein-based coating could be a good carrier for antioxidant molecules, and an effective preservative method for extending shelf life and improving the quality stability of oxidation-sensitive kernels.
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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) | 0.000 | 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".