Homogenate Extraction of Polysaccharides from Pine Nut Meal: Optimization and Comparison with Other Extraction Methods by Characterizing Their Extracts
Bibliographic record
Abstract
Homogenate extraction technology was applied to extract polysaccharides from pine nut (Pinus koraiensis) meal, a food waste from its oil processing. The first objective was to study the effects of liquid-solid ratio, homogenate extraction time, and temperature on the yield of pine nut meal polysaccharides (PNMP) using single-factor and Box–Behnken design. The optimal processing conditions were liquid-solid ratio of 43.23 mL/g at 35°C for 2 min 50 sec that produced PNMP yield of 11.7%. Extraction factors interacted with each other significantly ( p<0.01 ). The second objective was to compare the homogenate extract with those produced using other methods including ultrasonic, microwave, and hot water extraction. Polysaccharides were separated using HPLC based on molecular weight (MW) and characterized with the polysaccharide and protein contents of each fraction. The homogenate PNMP had the most of small size (<100 kDa) polysaccharides, and its glycoprotein fractions generally contained higher level of protein, as well as a high ABTS+scavenging activity observed by HPLC-UV-ABTS+assay. FTIR analysis was used to characterize the functional groups. Homogenate extraction is superior in producing PNMP compared with other common methods, and its PNMP extract has potential antioxidant benefit.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".