Effect of Biopolymers on Structure and Ice Recrystallization in Dynamically Frozen Ice Cream Model Systems
Bibliographic record
Abstract
Ice crystal growth and microstructure of sugarsolutions prepared with stabilizers (carboxymethyl cellulose [CMC], xanthan gum, locust bean gum [LBG], and gelatin) with or without milk solids-nonfat (MSNF) after freezing in a scraped surface heat exchanger and temperature cycling (5 cycles from -6 degrees C to -20 degrees C) were studied. Ice crystal growth was calculated from brightfield microscopic images acquired from samples before and after cycling. Freeze-substitution and low-temperature embedding (LR-Gold resin) were sample preparation techniques utilized for structure analyses by light microscopy and transmission electron microscopy. Differential staining for carbohydrates and proteins allowed the identification of stabilizer gel-like structures in LBG, gelatin, and gelatin/MSNF solutions. In the absence of milk proteins, xanthan and LBG were the most effective at retarding recrystallization, while in their presence, only xanthan had an effect. Cryo-gelation of the LBG was observed but is not the only mechanism of stabilizer action. Thermodynamic incompatibility between biopolymers was observed to promote localized high concentrations of milk proteins located at the ice crystal interface, probably exerting a water-holding action that significantly enhanced the stabilizer effect. Qualitatively, solution heterogeneity (phase separation) was directly proportional to ice crystal growth inhibition. It is suggested that water-holding by stabilizer and proteins, and in some cases steric hindrance induced by a stabilizer gel-like network, caused a reduction in the kinetics of the ice recrystallization phenomena and promoted mechanisms of melt-regrow instead of melt-diffuse-grow recrystallization, thus resulting in the preservation of the ice crystal size and in a small span of the ice crystal size distribution.
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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) | 0.000 | 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) | 0.001 | 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".