High-Intensity, Low-Frequency Ultrasound Treatment as Sustainable Strategy for Innovative Biomaterials with Antioxidant Activity from Tomatoes, Hemp, and Hops By-Products
Bibliographic record
Abstract
Ultrasound is a novel green technology that has been shown to have a multitude of application.This study focused on ultrasound application as a green platform adding values from agricultural waste in two different contexts.Ultrasound was used to increase efficiency of extraction of bioactive compounds such as saponins and phenolics in tomato skin, hemp meal and hops flowers.Ultrasound was used to create stable emulsion gels from byproducts/leftovers of tomato skin, hemp meal and hops flowers to produce green biomaterials.It was found that ultrasound treatment reduced extraction time for saponin and phenolic acid during tomato skin, hemp meal and/or hops flowers extraction from 24h to 30 min.The measured TPC for tomato, hemp and hops were also respectively 87.22±21.12,147.39±16.92g, 450.32±26.47g of GAE/100g per sample for UAE extraction and for traditional extraction of respectively 89.14±11.61g,159.42±28.20 and 460.95±48.57g of GAE/100g of sample.Similar results were obtained from total saponin content.UAE and traditional extraction showed respective TSC of 1443.79±125.24vs 1337.65 mg of DE/ 100g of sample for tomato, 1511.25±136.98 vs 1618.93±58.90mg of DE/ 100g of sample for hemp meal extraction, 8037.83±885.45vs 9847.34±2063.63mg of DE/ 100g of sample for hops flower extraction.Influence of ultrasound was also shown to have no impact on antioxidative capacity of extract obtained from tomato skin, hemp meal and hops flowers.Ultrasound treatment was shown to positively impact the overall microscopic structure and qualities of bioplastic such as water activity, % moisture, hardness, cohesiveness, resilience, and springiness index.This study suggests that ultrasound can be used as sustainable non-thermal method for extraction of active saponins and phenolics, enhancing their physico-chemical characteristic in bioplastic 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) | 0.002 | 0.001 |
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".