Differential scanning calorimetry (DSC) study of thermal properties of mustard proteins and their application as ingredient in beef patty
Bibliographic record
Abstract
• Albumins 2S and globulins 12S MPI are main fractions. • DSC properties are affected by concentration, salts and sugars. • MPI was successfully used as ingredient in beef patty. • MPI protected beef patty against oxidation. • MPI increased beef patty cooking yield. Thermal properties of mustard protein isolate (MPI) were evaluated by differential scanning calorimetry. MPI is mainly composed of albumins 2S and globulins 12S fractions as the major protein fractions. Also, different conditions affected thermal stability of the used MPI such as the protein concentration, the heating rate, pH of the dispersing medium, addition of different amount of sugar and salts (NaCl or CaCl 2 ). Results revealed that all these conditions must be considered when MPI is used as ingredient in different food matrices, particularly in those subjected to heat treatment such as cooking. Feasibility of beef patty making by using MPI as ingredient was also studied at 1, 2 and 3 % level on meat basis. This study showed that all fresh patties looked similar as the control; indicating that the visual acceptability of the MPI-added beef patty is good. TBARS values were evaluated for fresh samples stored at 4 °C for 24 h and -20 °C after 6 months. Results showed that MPI-added patties have the lowest TBARS values. Also, MPI-added beef patty cooking yield was higher than control (78.27 ± 1.03 %) when MPI was added at 3 % level. This study demonstrated the potential of using MPI in beef patty because of its antioxidant protective effect and good technological impact on product quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".