Role of Solid Fat Content in Oxidative Stability of Low-Moisture Cracker Systems
Bibliographic record
Abstract
Lipid oxidation is a major pathway for the chemical deterioration of low-moisture foods. Little is known about how the physical properties of the fat used in crackers impact lipid oxidation kinetics. Fully hydrogenated soybean fat + interesterified soybean oil, fully hydrogenated soybean fat + sunflower oil, fully hydrogenated soybean oil, and soybean oil and interesterified fat alone were formulated to have varying solid fat content (SFC) at 55 °C but the same linoleic acid and tocopherol contents, so the fats had similar susceptibility to oxidation. A fluorescence probe showed that lipid mobility increased with decreasing SFC in both cracker doughs and fat blends, suggesting the probe could be used to monitor SFC directly in foods. Decreasing SFC decreased oxidation in crackers. Crackers made from interesterified fat (13.7% SFC) were more oxidatively stable (hexanal lag phase = 33 days) than crackers made from fat blends (hexanal lag phase = 24 days). These results suggest that blended fats result in regions of liquid oil high in unsaturated fatty acids within a food product prone to oxidation. Conversely, interesterified fats where unsaturated and saturated fatty acids are more evenly distributed on the triacylglycerols are more stable. Thus, interesterified fats could allow for the formulation of products higher in unsaturated fatty acids to improve nutritional profiles without sacrificing shelf life.
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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) | 0.001 | 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".