Preparation of low-fat and shape-preserving walnut via combined carbon dioxide osmotic treatment and subcritical fluid defatting
Bibliographic record
Abstract
A sequential process, CO2 osmotic treatment combined with subcritical fluid defatting was developed to prepare low-fat walnut kernel preserving shape integrity. The influence of operating parameters involving osmotic pressure and subcritical butane defatting repeats on the defatting effect has been investigated. CO2 osmotic at 25 MPa resulted in defatting rate as 37.12% and minimum perfect kernel rate (81.48%). Increase of defatting repeats from one to three times led to exploding quantities of defatting rate (from 9.98% to 40.61%) but no significant varies of perfect kernel rate (p >0.5). With the increase of defatting rate, the amount of oil bodies in walnut cells decreased, and defatting rate presented negative correlated with hardness and chewiness of products. Hexanal, nonanal and heptaldehyde were the predominant volatile compounds. After defatting, they may be removed as accompanying components and showed a slight downward trend. However, results of electronic nose revealed that there was no significant difference among flavor of walnut with different defatting rate (p >0.5). Slight defatting caused significant variations in the taste of walnuts. As the defatting rate exceeded 18.65%, products expressed similar taste. Overall, coupling CO2 osmotic treatment with subcritical fluid defatting proved to be great potential for application in shape-preserving defatting.
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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".