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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".