Nutrition, Sensory Evaluation, and Performance Analysis of Trans fat-Free, Low Alpha-Linolenic Acid Frying Oils
Bibliographic record
Abstract
The FDA now requires the labeling of trans fats, a decision that has encouraged foodservice operators to eliminate trans fats from foods and reformulate deep-fat frying oils in order to make them trans fat-free. This study evaluated performance, sensory, and nutrition characteristics of trans fat-free oils used to cook French fries during a 10-day controlled degradation session. Nutritional analyses and fatty acid profiles were conducted on the oils before degradation, and a sensory evaluation of fries cooked in different oils was conducted to determine liking of the fries. Results indicated a preference for French fries fried in canola oils, while the 0.05% low alpha-linolenic (ALA) soybean oil had the highest stability and lowest oil usage. All the low ALA oils in the study provided a healthy, inexpensive, and stable option for foodservice operators to consider when choosing trans fat-free oils; therefore, foodservice operators must determine which is more important—stability, usage, nutrition, or taste preference—when selecting the right oil for their operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".