Valorization of hemp hearts oils by advanced extraction techniques and their comparative physicochemical characterization
Bibliographic record
Abstract
Industrial hemp hearts were subjected to three different extraction techniques-advanced extraction techniques like microwave and ultrasound extraction and primitive extraction technique like mechanical cold pressing in order to extract oils. The extraction yield varied between 41.0 ± 2.1% (w/w) and 54.0 ± 2.7% (w/w). Linoleic acid (LA) was reported to be the major polyunsaturated fatty acid (PUFA) followed by α−Linolenic acid (ALA). The ratio of LA to ALA was found to be close to 3:1 which is considered optimal for nutrition. The ratio of unsaturated fatty acids (UFA) to saturated fatty acids (SFA) was in the range of 6.7 to 9.1. Several minor compounds like ɣ-Sitosterol, stigmasterol, rhodoxanthin, carotene, and methyl cholate as well as antioxidants like β-Carotene and ɣ-Tocopherol have been identified in the oils. The physicochemical properties of the oils like the acid value, iodine value, and oxidative stability indicated that they have higher quality and that the level of unsaturation was very high. Furthermore, free radical scavenging activity of the oils revealed that the oils exhibited 94% of activity in the first 2 h. A perfect combination of unsaturated fatty acids and saturated fatty acids, an appropriate medley of different antioxidants not only qualifies hemp seed oil as an excellent source of nutrition but also as an important plant-based (vegetable) oil for the higher value nutraceutical and pharmaceutical industries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".