Development of Optimized Bioactive Nanocomposite Films Using a Central Composite Design for Control of Microbial Contamination in Stored Rice
Bibliographic record
Abstract
ABSTRACT Poly(butylene adipate‐co‐terephthalate) (PBAT) and polylactic acid (PLA)‐based nanocomposite films were developed as active packaging materials for stored rice. The composition and synergistic effect of the active formulations (AF‐1 and AF‐2), cellulose nanocrystals (CNC), and glycerol (Gly) as independent variables were tested to reach the optimal antimicrobial nanocomposite films using response surface methodology (RSM) employing a central composite design (CCD). The inhibitory capacity (IC, %) of the developed films as a dependent variable against two bacterial and three fungal strains was measured using the agar volatilization assay. The ANOVA results showed a perfect fit of the regression models for the response, with significant P values ( P ≤ 0.05) and high coefficient of determination (R 2 ) values. Incorporating the CNC, Gly, and AFs significantly improved the PBAT films’ elasticity, water barrier properties, and oxygen transmission rate (OTR) compared to the control films; however, the water and oxygen barrier properties of PLA films were compromised. The release data of AFs from the films was fitted with the Korsmeyer–Peppas model, indicating a Fickian or quasi‐Fickian diffusion mechanism ( n < 0.45). For the in situ study, the optimized bioactive PBAT‐based films with 750 Gy of γ‐irradiation synergistically reduced the bacterial and fungal load by 73–93% in stored rice after 2 months compared to the control treatments. The data confirms the potential applicability of the optimized films as promising candidates for active packaging for cereal grains and their ability to compete with traditionally used inert non‐biodegradable plastic films.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".