Moisture Sorption Behavior, and Effect of Moisture Content and Sorbitol on Thermo-Mechanical and Barrier Properties of Pectin Based Edible Films
Bibliographic record
Abstract
The moisture sorption behavior of pectin films formulated with different sorbitol content was evaluated and films with different equilibrium moisture contents were obtained. Different models were used to describe the moisture sorption isotherms (MSI) of pectin films, sorbitol and pectin powder. Based on changes observed in MSI, sorbitol was found to strongly interact with pectin polymers. Incorporation of sorbitol in pectin films resulted in lower equilibrium moisture contents at low to intermediate water activities (aw), but much higher moisture contents at aw > 0.53. Estimates of monolayer moisture values (1.53 3.81 g H2O kg-1 solids) were obtained by the application of Guggenheim-Anderson-DeBoer (GAB) model. A differential mechanical analyzer (DMA) was used for mechanical properties of formulated films while a differential scanning calorimeter (DSC) was used for thermal properties and glass transition temperature (Tg). With both DMA and DSC tests, the strong plasticizing action of water and sorbitol was evident. Tg vs. moisture content data were successfully fitted to the Fox empirical model. Multi-frequency DMA measurements provided estimates for the apparent activation energy (Ea) of the second glass transition in the range of 131-184 kJ/mol; the values for Ea decreased with increasing sorbitol concentration. Water vapor permeability (WVP) and mechanical properties of the films were also analyzed under varying sorbitol and moisture contents. Increasing moisture or addition of sorbitol to pectin films increased the elongation at break, but decreased the tensile strength, modulus of elasticity and Tg, and increased WVP of the films.
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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 | 0.001 |
| 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".