Effect of Okra Cell Wall and Polysaccharide on Physical Properties and Stability of Ice Cream
Bibliographic record
Abstract
Stabilizers are used in ice cream to increase mix viscosity, promote smooth texture, and improve frozen stability. In this study, the effects of varying concentrations (0.00%, 0.15%, 0.30%, and 0.45%) of okra cell wall (OKW) and its corresponding water-soluble polysaccharide (OKP) on the physical characteristics of ice cream were determined. Ice cream mix viscosity was measured as well as overrun, meltdown, and consumer acceptability. Ice recrystallization was determined after ice cream was subjected to temperature cycling in the range of -10 to -20 °C for 10 cycles. Mix viscosity increased significantly as the concentrations of OKW and OKP increased. The addition of either OKW or OKP at 0.15% to 0.45% significantly improved the melting resistance of ice cream. OKW and OKP at 0.15% did not affect sensory perception score for flavor, texture, and overall liking of the ice cream. OKW and OKP (0.15%) reduced ice crystal growth to 107% and 87%, respectively, as compared to 132% for the control (0.00%). Thus, our results suggested the potential use of OKW and OKP at 0.15% as a stabilizer to control ice cream quality and retard ice recrystallization. OKP, however, at 0.15% exhibited greater effect on viscosity increase and on ice recrystallization inhibition than OKW.
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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".