Effects of Viscozyme L on the Yield of Oil Obtained from Fresh Avocado Fruit Pulp (Persea americana Mill.) by Three-Phase Partitioning Extraction Method
Bibliographic record
Abstract
The applicability of Viscozyme L in extraction for recovery of avocado oil from fresh fruit pulp was studied. The effects of enzyme concentration, incubation temperature and time on the yield of avocado oil extracted by enzyme-assisted three-phase partitioning (EATPP) extraction method were investigated and the physicochemical properties of the final oil were evaluated. The highest oil yield obtained by EATPP using Viscozyme L was 39.79 ± 1.02 % on the dry basis of material, significantly higher than that extracted by TPP without enzyme (18.5 ± 0.76 %). The best conditions were 1.5 % (v/w) enzyme concentration, incubation at 50 °C for 1 h. Meanwhile, the yield of oil extracted by Soxhlet method was 58.10 ± 0.65 %, obviously higher than EATPP. However, EATPP gave better oil in terms of quality. The free fatty acid value of oil extracted by EATPP (0.87 ± 0.08 %) showed no significant difference compared to the oil extracted by Soxhlet method. Besides, oil extracted by EATPP had lower peroxide value (6.75 ± 0.29 meq O2/kg oil) and higher total phenolic content (63.60 ± 2.73 mg GAE/100 g oil) than that by Soxhlet method. The fatty acid compositions of the oil extracted by EATPP mainly consist of palmitic acid, palmitoleic acid, oleic acid, and linoleic acid. The extraction of oil from fresh avocado fruit pulp utilizing EATPP can be used as an efficient method to reduce the avocado loss, thus saving cost, and increasing the product varieties from this kind of fruit.
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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 | 0.000 | 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".