Effects of Replacing Pork Back Fat with Canola and Flaxseed Oils on Physicochemical Properties of Emulsion Sausages from Spent Layer Meat
Bibliographic record
Abstract
The objective of this study was to investigate the effects of canola and flaxseed oils on the physicochemical properties and sensory quality of emulsion-type sausage made from spent layer meat. Three types of sausage were manufactured with different fat sources: 20% pork back fat (CON), 20% canola oil (CA) and 20% flaxseed oil (FL). The pH value of the CA was significantly higher than the others (p<0.05). The highest water holding capacity was also presented for CA; in other words, CA demonstrated a significantly lower water loss value among the treatments (p<0.05). CA had the highest lightness value (p<0.05). However, FL showed the highest yellowness value (p<0.05) because of its own high-density yellow color. The texture profile of the treatments manufactured with vegetable oils showed higher values than for the CON (p<0.05); furthermore, CA had the highest texture profile values (p<0.05) among the treatments. The replacement of pork back fat with canola and flaxseed oils in sausages significantly increased the omega-3 fatty acid content (p<0.05) over 15 to 86 times, respectively. All emulsion sausages containing vegetable oil exhibited significantly lower values for saturated fatty acid content and the omega-6 to omega-3 ratios compared to CON (p<0.05). The results show that using canola or flaxseed oils as a pork fat replacer has a high potential to produce healthier products, and notably, the use of canola oil produced characteristics of great emulsion stability and sensory quality.
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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) | 0.001 | 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".