Mitigating Postfrying Degradation Factors of Fats and Oils through the Development of Bagasse-Based Adsorbent
Bibliographic record
Abstract
The quality of fats and oils is a critical aspect of the food processing industry and consumer health. Fat degradation, particularly through oxidation, impacts various quality parameters, including color, taste, flavor, aroma, quality, and appearance. To address this issue, a study was conducted using five degraded fats/oil (DF/O) commodities as the target for an adsorption process. These commodities were chosen because of their high level of degradation by-products. The study used sugarcane bagasse (SCB) to develop five different treatments of indigenous adsorbents activated with various NaOH concentrations. Analyses including iodine number, adsorbent yield, and scanning electron microscope were performed to identify the potential of the prepared concentrations. Results showed that the indigenous adsorbent created with 1.0% NaOH was the most effective. The alkali treatment had a positive impact on the samples, but the SCB 10% was found to be the most efficient in reducing the degradation value of the treated samples. The findings of this study suggest that the use of indigenous adsorbents, particularly those prepared with SCB 10%, can be an effective way to reduce fat degradation and enhance the quality of fats and oils in the food processing industry. This approach can also address consumer health concerns related to fat and oil quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".