The effect of preultrasonic process on oil content and fatty acid composition of hazelnut, peanut and black cumin seeds
Bibliographic record
Abstract
In this study, the effect of different sonication times (10, 20, and 30 min) on oil yields, extracted by using soxhlet together with preultrasonic treatment, and fatty acid composition of seed/kernels were investigated. The sonication of samples for 30 min caused the highest increase in oil yield of hazelnut (from 62.38 to 63.60%) and black cumin (from 27.90 to 31.80%) (p < .05). The appropriate sonication time for oil yield of peanut was 10 min, with the range of 51.50%. After sonication process, the dominant fatty acid contents of all samples showed a change and the major decrease in oleic acid amount of hazelnut (from 75.20 to 74.27%) and peanut oils (from 57.10 to 56.69%) and linoleic acid content of black cumin (from 58.38 to 57.50%) were determined when samples sonicated for 30 min (p < .05). Sonication process caused a decreasing in black cumin oil, and the reduction increased with sonication time. Practical applications Ultrasound-assisted extraction method can be used as an alternative extraction method for conventional extraction. Ultrasonic-assisted extraction has some advantages as being efficiency, speed and using low temperatures, which prevents thermal damage. The ultrasound process enables to greater influence of solvent into the sample matrix and increases mass transfer. Thereby, the higher extract yield, almost 23%, provided with ultrasonic-assisted extraction in comparison to soxhlet extraction.
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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".