Optimization of Enzymatic Hydrolysis of Sacha Inchi Oil using Conventional and Supercritical Carbon Dioxide Processes
Bibliographic record
Abstract
Abstract Sacha inchi (Plukenetia volubilis) oil has high polyunsaturated fatty acids content. The hydrolysis of this oil is an efficient way to obtain desirable free fatty acids (FFA). The optimization of parameters was carried out according to the maximum production of FFA using two enzymatic hydrolysis processes. The effect of enzyme concentration (5–40 % based on weight of oil), temperature (40–60 °C), and oil:water molar ratio (1:5–1:70) were studied for the conventional enzymatic hydrolysis process, while pressure (10–30 MPa) and oil:water molar ratio (1:5–1:30) were studied for the enzymatic hydrolysis in supercritical carbon dioxide (SC‐CO2) media. The hydrolysis in SC‐CO2 media resulted in higher production of FFA (77.98 % w/w) at 30 MPa and an oil:water molar ratio equal to 1:5 compared to the conventional process (68.40 ± 0.98 % w/w) at 60 °C, oil:water molar ratio equal to 1:70, and 26.17 % w/w, enzyme/oil. The only significant parameter on the production of FFA for conventional enzymatic hydrolysis was enzyme concentration, while for the hydrolysis in SC‐CO2 media both pressure and the molar ratio of oil:water were significant. Lipid class analyses showed that with both methods, FFA, monoglycerides, and diglycerides content in the final product increased compared to pure oil, while triglycerides content decreased. Fatty acid composition analysis showed that the content of fatty acids in the FFA form were similar to their triglyceride form.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".