Effects of Organogel Hardness and Formulation on Acceptance of Frankfurters
Bibliographic record
Abstract
Different organogel formulations used as beef fat (BF) replacement (0%, 20%, 40%, 60%, and 80%) were utilized to optimize the mechanical properties of frankfurters. Organogels, made of canola oil (CO), included different concentrations of ethyl cellulose (EC) and sorbitan monostearate (SMS). They consisted of: 8% EC + 1.5% SMS referred to as organogel-I (OG-I), 8% EC + 3.0% SMS (OG-II), and 10% EC + 1.5% SMS (OG-III), which were found promising in a previous study when used at 100% replacement. Replacement of BF with organogels at all levels could bring down the very high hardness values (texture profile analysis and sensory) of frankfurters prepared using CO by itself, relative to the BF control. OG-I and OG-II quantity had no significant effect on hardness and springiness, being similar in many cases to the BF and lower than the CO control. Shear force values of all organogel treatments were not significantly different from one another, and were between the BF and CO controls. Smokehouse yield showed a pattern of decreasing losses with increasing organogel replacement level. Sensory analysis revealed that using CO by itself significantly increased hardness, but structuring the oil (via organogelation), brought it down to the BF control value in all OG-I and OG-II formulations. Juiciness was significantly reduced by using liquid oil but increased with raising the amount of organogels. Oiliness sensation increased with higher organogel substitution and was actually higher than the beef control. The study demonstrates the potential use of vegetable oil structuring in replacing the more saturated BF in emulsion-type meat products.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".