Effect of Dielectric Properties of a Solvent-Water Mixture Used in Microwave-Assisted Extraction of Antioxidants from Potato Peels
Bibliographic record
Abstract
The dielectric properties of a methanol-water mixture were measured at different temperatures from 20 to 80 °C at two frequencies 915 MHz and 2450 MHz. These frequencies are most commonly used on industrial and domestic scales respectively. In this study, the dielectric properties of a methanol-water mixture were found to be dependent on temperature, solvent concentration, and presence of plant matrix. Linear and quadratic equations were developed to establish the dependency between factors. At 2450 MHz, the dielectric constant of methanol-water mixtures was significantly affected by concentration of methanol rather than by temperature, whereas the dielectric loss factor was significantly affected by temperature rather than by methanol concentration. Introduction of potato peel led to an increase in the effect of temperature on the dielectric properties of the methanol fractions. At 915 MHz, both the dielectric properties were significantly affected by the increase in temperature and solvent concentration, while the presence of potato peel had no significant effect on the dielectric properties. Statistical analysis of the dissipation factor at 915 and 2450 MHz revealed that both temperature and solvent concentration had a significant effect on it, whereas introduction of potato peels at 915 MHz reduced the effect of temperature as compared to 2450 MHz. The total phenolic yield of the microwave-assisted extraction process was significantly affected by the solvent concentration, the dissipation factor of the methanol-water mixture and the extraction time.
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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 | 0.001 |
| 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".