Simulating k‐Carrageenan and Sucrose as a Model Solution for Determining Temperature‐Dependent Measurements of Thermal Conductivity and Specific Heat of Tropical Fruit Juices
Bibliographic record
Abstract
ABSTRACT Model solutions are alternatives to reduce experimental costs in the evaluation of heat transfer during the freezing of tropical fruit juices in large containers. Therefore, data of specific heat, and conductivity of a model solution 0.5% of k‐carrageenan and 10% sucrose (weight/volume in water) were obtained in the temperature range of −30°C to 25°C. The thermal conductivity was measured using the line heat source thermal probe and specific heat, with differential scanning calorimeter (DSC). These thermal properties were modeled and correlated with ice fraction predictions. The initial freezing temperature ( T fS ) of the model solution was −1.1°C and there was a great variation in the thermal conductivity in the temperature range of 0°C to −5°C during strong variations in ice formation. The Maxwell–Eucken model provided theoretical values closest to the experimental results and demonstrated a least relative difference which ranged from 3% to 11.5%. The measurement of specific heat versus temperature had the expected theoretical profile. These properties were validated by comparing them with the experimental results obtained for red guava ( Psidium guajava L ., 85.0% moisture content, T f = −1.4°C), mango ( Mangifera indica L. var. Uba, 86.5% moisture content, T f = −2.4°C), and passion fruit ( Passiflora edulis Sims F. flavicarpa Deg ., 88.9% moisture content, T f = −2.2°C) at subzero temperatures. In this temperature range, the percentage differences were less than 20%. The highest differences were near the initial freezing temperatures and the smallest percentage differences were for guava juice and the largest for mango juice.
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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".