Preparation of rice paper enriched with laver (Pyropia sp.) and tapioca starch with process optimization using response surface methodology
Bibliographic record
Abstract
The objective of the present study is to enhance the nutritional value of rice paper by enriching it with laver (Pyropia sp.) and tapioca starch to meet the global demand for processed laver products. The conditions of the prepared laver and tapioca starch-enriched rice paper (LTRP) were optimized using a central composite design (CCD) of response surface methodology (RSM). For the preparation of LTRP, the optimal ingredients were 23.10 g laver powder, 60.08 g tapioca starch, and 12.10 g rice powder. Sensory evaluation of the LTRP based on the CCD indicates that laver powder positively influences taste, flavor, and appearance. Furthermore, the physicochemical analysis revealed that the LTRP has a higher protein content (11.87 ± 0.22%) and a higher amount of essential amino acids (3513.21 mg/100 g) than commercial rice paper (CRP). The antioxidant and total phenolic contents of the LTRP, compared to that of the CRP, significantly increased (p < 0.001). The results suggest that the nutritional value and the sensory characteristics of the LTRP improved as a result of the enrichment with laver powder and tapioca starch. The prospect outlined in this study is likely to usher in a new era in the rapidly growing laver industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".