Effect of Calcium Chloride Addition on Ice Cream Structure and Quality
Bibliographic record
Abstract
The influence of calcium fortification by the addition of calcium chloride on quality parameters of ice cream based on physical properties was investigated, as was the effect of kappa-carrageenan at modifying the effects of this calcium fortification. Four ice cream mixes of conventional composition, with added kappa-carrageenan (0 or 0.025%) and added calcium chloride (0 or 4.4 g L(-1) = 40 mM of added Ca(2+)), were prepared. Modulated temperature-differential scanning calorimetry was used to investigate the effect of calcium chloride on the nucleation temperature, enthalpy of melting, and freezing point depression. The protein composition of 15.4% (wt/wt) reconstituted skim milk powder solutions with or without 4.4 g L(-1) added CaCl(2) and in the supernatant after ultracentrifugation was determined. Fat particle size distributions in ice cream were characterized by light scattering. Ice crystal sizes before and after temperature cycling were determined by cold-stage light microscopy. The results demonstrated that the addition of calcium chloride led to a substantial increase in ice crystal sizes and in fat partial coalescence, which were exacerbated by the addition of kappa-carrageenan. These results can be explained by the interaction between Ca(2+) ions and casein micelles, rather than any effects on freezing point depression. The calcium ions led to a more compact micelle, less serum beta-casein, and high fat destabilization, all of which would be expected to reduce macromolecular structure and volume occupancy in the unfrozen phase, which led to increased rates of ice recrystallization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".