EVALUATION OF FACTORS AFFECTING BARRIER, MECHANICAL AND OPTICAL PROPERTIES OF PECTIN‐BASED FILMS USING RESPONSE SURFACE METHODOLOGY
Bibliographic record
Abstract
ABSTRACT Emulsion coatings were formulated and films were developed using pectin as the basic structural component. Preliminary experiments were carried out to determine the proper type and concentration of pectin, lipid and plasticizers in the film. The effects of pectin, beeswax and sorbitol concentration on water vapor permeability (WVP), mechanical properties and opacity of the films were evaluated using the response surface methodology. WVP increased by pectin and sorbitol concentration and was decreased by beeswax concentration. Mechanical properties were mainly affected by pectin and sorbitol concentration. Increasing the amount of pectin and decreasing the sorbitol concentration increased tensile strength (TS) and modulus of elasticity (EM), while elongation at break (EB) increased by increasing both pectin and sorbitol concentration. Beeswax was the most influential factor that affected opacity, which increased with increasing beeswax concentration. Models developed for WVP, EB, EM and opacity had high coefficient of multiple determination (R2) values (>0.87) and significant F values. The model for TS had a slightly lower R2 of 0.81. PRACTICAL APPLICATIONS This research examined the preparation of pectin‐based edible films, providing an understanding of the main functionalities of these coatings. This film can help retain moisture and restrict its movement. Moisture levels in foods are critical for maintaining freshness, controlling microbial growth and providing texture. This film can control water activity, preventing either moisture loss or uptake. It can protect food from physical injuries. Since so many formulations were designed, based on the use of the film one can select each type of formulation that works best for the specific application. It can potentially limit fat migration, provide a barrier to gas, help to trap flavor and aroma, and carry and present antioxidants or antimicrobials. This article will be of value to anyone working with products where edible films have potential applications and particularly to technical personnel and product developers who wish to understand the potential of edible film and coating technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".