Development and characterization of pearl millet (<scp>AHB</scp> 1200) starch nanoparticle‐based edible films: A paradigm shift in food packaging
Bibliographic record
Abstract
Abstract Pearl millet, known for its adaptability to challenging agro‐climatic conditions, emerges as a valuable candidate for biopolymer‐based packaging. The AHB 1200 cultivar, distinguished by its high starch content, provides a reliable source for biopolymer extraction. The conversion of pearl millet starch into nanoparticles by acid hydrolysis represents a cutting‐edge method to enhance biopolymeric materials. The inclusion of these nanoparticle concentrations (0.5%, 1%, 5%, and 10%) into the film results in improved mechanical characteristics, reduced water permeability, and increased biodegradability. Furthermore, the lowered water solubility and reduced water vapor transmission rate (WVTR) further underscore their positive contributions. This study comprehensively examines various film properties, encompassing WVTR from 7.23 ± 0.06 to 4.57 ± 0.08 g/m 2 /s, moisture content, solubility from 35.29 ± 0.51% to 30.09 ± 0.15%, burst strength from 1102.11 ± 0.99 g to 1535.71 ± 0.63 g, thermal stability, and biodegradability from 65.16% to 92.89%. The findings highlight the notable advancements achieved through the integration of starch nanoparticles. Practical applications Pearl millet (AHB 1200) starch nanoparticle‐based edible films offer a versatile solution for specialized food packaging needs because of their compatibility with various food types, dairy products, fresh produce, and so on. These films act as a protective barrier and maintain product freshness during storage and transit. It also has enhanced mechanical strength and tear resistance and provides vacuum‐sealed packaging, ensuring an extended shelf life. Beyond their functional benefits, these edible films present an opportunity for branding and marketing differentiation. The films made with pearl millet have an earthy, natural appearance that fits with today's customer inclination for sustainable and environmentally friendly goods. This allows manufacturers to leverage the films as a means of conveying their commitment to environmentally friendly practices, enhancing their brand image and consumer loyalty. Furthermore, the biodegradability of these films is preferred over plastic waste. They offer a viable alternative to traditional plastic packaging, aligning with global initiatives to reduce the environmental impact of packaging materials.
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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 | 0.000 |
| 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".