Assessment of the Oxidative Stability of Flaxseed-Enriched Lasagna Using the Rancimat Method
Bibliographic record
Abstract
The oxidative stability of flaxseed-enriched lasagna during thermal processing was quantified using the Rancimat method. Lasagna was processed at high temperatures (120 and 180C) and oxidation was assessed from the electric conductivity of a water solution dissolving volatile secondary oxidation products. The oxidative stability of the lasagna was defined as the time to increase the conductivity by 5 μS/cm. The oxidative stability of the lasagna was higher than pure flaxseed oil, showing the protective effect of the lasagna matrix against the development or diffusion of oxidation products. Rolling the lasagna multiple times in the sheeter and reducing the processing temperature increased resistance to oxidation. The oxidative stability of the lasagna was independent of the extraction of flaxseed oil prior to enrichment. For both flaxseed enrichment levels (10 and 20%), the oxidative stability at 120C was approximately 10 times longer than the time required to dry lasagna at this temperature. Practical Applications The enrichment of lasagna with flaxseed improves its nutritional quality because of flaxseed omega-3 fatty acid, lignan and fiber content. However, the oxidation of flaxseed lipids during enriched lasagna processing is a major concern because of their high degree of unsaturation. This work validates the use of the Rancimat method to determine the processing parameters with a significant impact on flaxseed lipids oxidative stability and indicates that the number of passes in the sheeter, the processing temperature and the flaxseed enrichment level should be carefully considered to limit flaxseed lipids oxidation during processing.
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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 | 0.001 | 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 | 0.001 |
| 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".