Physicochemical properties of edible films formed by phase separation of zein and hydroxypropyl methylcellulose
Bibliographic record
Abstract
In this study, composite films were prepared by drying zein and hydroxypropyl methylcellulose (HPMC) solutions prepared in aqueous ethanol (70:30 ethanol:water, w/w) at different zein:HPMC (Z:H) ratios (0:1, 1:4, 2:3, 1:1, 3:2, 1:0 w/w). The drying process resulted in the phase separation of the two polymers, forming submicron zein particles dispersed in a continuous HPMC phase. As the zein content increased from 1:4 to 3:2 Z:H (w/w), the average zein particle size in the films increased from 840 to 2020 nm, with minimal changes in film thickness. Fourier transform infrared spectroscopy analysis did not reveal frequency shifts of zein and HMPC characteristic absorbance bands, suggesting minimal interaction between the two polymer phases. Neat HPMC film had the highest water vapor permeability, but this characteristic significantly ( p < 0.05) decreased as zein content increased from 1:4 to 3:2 Z:H (w/w). For films with the zein content increasing from 0:1 to 3:2 Z:H (w/w), the tensile stress decreased significantly from 62.35 to 33.65 MPa. The opacity and color increased with increasing zein concentration. The composite zein-HPMC films prepared from the controlled phase separation process could potentially be used as a carrier for the delivery of bioactive in food and edible coating applications. • Zein and HPMC phase separated during solvent evaporation. • Spherical zein particles evenly distributed in HPMC continuous matrix. • The morphologies of films were identified by SEM, AFM & confocal microscopy. • Water vapor permeability decreased with increased zein content in blend film.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".