Effects of high-pressure processing on the physicochemical and adsorption properties, structural characteristics, and dietary fiber content of kelp (Laminaria japonica)
Bibliographic record
Abstract
To investigate the effects of high-pressure processing (HPP) on the physicochemical and adsorption properties and structural characteristics of kelp, kelp slice (KS) and kelp powder (KP) were treated under different pressures (300, 450, and 600 MPa) for 5 and 10 min. Compared to untreated KP, HPP-treated KP yielded a 1.31-fold increase in water holding capacity (600 MPa/5 min), a 0.12-fold increase in swelling capacity (450 MPa/10 min), a 1.33-fold increase in oil holding capacity (600 MPa/10 min), a 10-fold increase in glucose adsorption capacity (450 MPa/10 min), and a 0.22-fold increase in cholesterol adsorption capacity (163.1 mg/g DW at 450 MPa/10 min), and exhibited good Cd (Ⅱ) adsorption capacity when its concentration was 10 mmol/L in the small intestine. The physicochemical properties of HPP-treated KS were not improved due to its low specific surface area. In addition, HPP treatment efficiently reduced the particle size of KP and increased its total and soluble dietary fiber content by 17% and 63% at 600 MPa/10 min, respectively. Scanning electron microscope micrographs demonstrated that the surface of HPP-treated KP was rough and porous, and the specific surface area increased with increasing pressure and processing time. To conclude, the results obtained in the present study suggest that HPP is a promising processing method for improving the functionality and structural characteristics of KP and provide a theoretical basis for the utilization of HPP-treated KP as a fiber-rich ingredient in the functional food industry.
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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 | 0.001 |
| 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".