Development of pullulan‐based nanocomposite films reinforced with starch nanocrystals for the preservation of fresh beef
Bibliographic record
Abstract
Abstract BACKGROUND Incorporation of polysaccharide‐based nanofillers is an effective strategy to fabricate bio‐nanocomposite films with preferable mechanical, barrier, and surface hydrophobicity properties compared to pure biopolymer films. The objective of this research is to investigate the influence of starch nanocrystals obtained from native (NSNC) and waxy rice starch (WSNC) on the physical–chemical properties of pullulan‐based nanocomposite films and their preservation performance on fresh beef. RESULTS Continuous SNCs network structure was observed for pullulan‐10% SNCs nanocomposite films, whereas the percolation network of SNCs was destroyed and became no longer continuous with increasing SNCs concentration up to 20% in pullulan films. Among the tested films, pullulan‐10% SNCs films showed the highest TS values, lowest WVP and OTR values, due to the formation of percolating SNCs network in pullulan matrix. It is noteworthy that the WVP and OTR values of pullulan‐10% WSNC films were significantly lower than that of pullulan‐10% NSNC films, probably due to higher hydrophobicity and crystallinity of WSNC compared with NSNC. Beef pieces coated with pullulan‐SNCs films had higher L * and a * values, lower TVB‐N, TBARS, and TVC values during 7 days' storage at 4 °C compared with samples coated with pullulan films. CONCLUSION Pullulan‐SNCs nanocomposite films, especially pullulan‐WSNC films, could be potentially used as a coating material for fresh beef due to their desirable oxygen and water barrier properties. © 2022 Society of Chemical Industry.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".