Effect of Solvent, Preheating Temperature, and Time on the Ultrasonic Extraction of Phenolic Compounds from Cold‐Pressed Hempseed Cake
Bibliographic record
Abstract
Abstract The effect of different solvents (aqueous methanol [70%, v/v], aqueous acetone [80%, v/v], and a solvent mixture [MA] of aqueous methanol [70%, v/v] and aqueous acetone [70%, v/v] in a ratio of 1:1 [v/v]), preheating temperatures (140, 160, and 180°C), and times of exposure (5, 15, and 30 min) on the ultrasonic extraction of the main phenolic compounds from hempseed cake (Cannabis sativa) was investigated. A simplified new high‐performance liquid chromatography (HPLC) method was developed to identify and quantify the main phenolics (namely, N‐trans‐caffeoyltyramine and cannabisin B) in the extracts. Two other main compounds, numbered 3 and 4, were also detected. The results showed that the nature of the extracting solvent had a significant (P < 0.05) impact on the ultrasonic extraction of phenolic compounds. The acetone extracts exhibited the highest total phenolic content (TPC), followed by MA and methanol. The preheating temperature and time of exposure enhanced the TPC for all solvents examined. The main phenolics, N‐trans‐caffeoyltyramine, cannabisin B, and compound 3, were positively affected by the temperature and time of exposure, irrespective of the solvents used. In sharp contrast, compound 4 appeared to be thermally sensitive: increasing preheating time and temperature decreased the yields of this compound. This study demonstrated that acetone was the most effective extracting solvent and that preheating enhanced the yield of the main phenolics.
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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) | 0.001 | 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".