Antimicrobial properties of grape seed extracts and their effectiveness after incorporation into pea starch films
Bibliographic record
Abstract
Summary Chemical analysis and antimicrobial nature of grape seed extracts (GSE) and their Reisling Vitis vinifera L. application as fortificants for edible starch films were investigated. GSE possessed an antioxidant activity of 17.18 ± 1.29 mmol TROLOX equivalents gextract−1 and total phenolic content of 327.58 ± 7.24 mmol gallic acid equivalents gextract−1 mainly attributed to their flavonoid and phenolic acid composition determined by high‐performance liquid chromatography accomplished to a diode array detector and a electrospray ionisation mass spectrometer in negative mode (HPLC‐DAD/ESI‐MS). GSE inhibited the growth of Gram‐positive food‐borne pathogens while Gram‐negatives were not inhibited. After GSE were incorporated into pea starch films, thickness of enriched films increased and the puncture and tensile strength decreased compared to control films. Furthermore, migration of phenolic compounds from the films to different food simulants, aqueous, acidic and alcoholic solution was determined according to 89\109\EEC directive. A higher particle migration in acidic simulants was found. Finally, the effect of GSE incorporated pea starch films was tested in vitro with pork loins infected with Brochothrix thermosphacta. GSE films reduced the bacterial growth in 1.3 log colony forming units mL−1 after 4 days incubation at 4 °C.
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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) | 0.001 | 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".