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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".