Factors Influencing Crystallization of Erythritol in Aqueous Solutions: A Preliminary Study
Bibliographic record
Abstract
<p>Erythritol – a new zero caloric sweetener – shows high potential for developing new sugar reduced or sugar free food formulations. Since the crystallization behavior of erythritol was not investigated so far, this study focused on factors influencing erythritol crystallization in aqueous solutions using a simple gravimetric method. The general features of the course of crystallization are a linear increase until a period of 2.5 h of storage followed by a decelerating phase and a phase which represents equilibrium. Additionally, different influencing factors (supersaturation level, storage temperature, storage period and cooling rate) on the crystallization process of erythritol were investigated. It was shown that crystallization value increased with increasing supersaturation level and the progress of crystallization was almost linear from the initial induction period until equilibrium. Therefore, a first-order kinetic for erythritol crystallization was proposed, because only supersaturation level correlated to the erythritol concentration in the solution influenced the course of crystallization. Calculated crystallization rate constants increased considerably with increasing supersaturation levels. Furthermore, at the same supersaturation levels crystallization of erythritol was independent from the storage temperature. Cooling rate influenced only crystal shapes and sizes, but not the crystallization values.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".