Bibliographic record
Abstract
【Objective】Through adding trisodium citrate and terasodium pyrophosphate to improve the functionality of Mozzarella cheese. 【Method】Trisodium citrate and terasodium pyrophosphate were added to curds (at 1%,3%,and 5%,wt/wt) at the dry-salting mothod of Mozzarella cheese,together with glucono-δ-lactone to maintain a constant pH. The nutrition composition,textural profile analysis,meltability,stretchability,free-oil and microstructure of Mozzarella cheese were examined to determine their effects on the functionality of Mozzarella cheese.【Result】When trisodium citrate was added,no difference in pH,fat,moisture and protein was observed,but the content of calcium and phosphonium significant decreased; when terasodium pyrophosphate was added no difference in pH,fat,moisture and content of calcium was observed,but the content of phosphonium significantly increased and protein significantly decreased. Addition of trisodium citrate and terasodium pyrophosphate,significant difference in textural profile analysis (hardness,springiness and cohesiveness),meltability,stretchability,free-oil and microstructure was observed.【Conclusion】 Addition of trisodium citrate and terasodium pyrophosphate could significantly improve the functionality of Mozzarella cheese.
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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".