An innovative konjac glucomannan/<scp>κ‐carrageenan</scp> mixed tensile gel
Bibliographic record
Abstract
Abstract BACKGROUND Konjac glucomannan (KGM) showed a synergistic interaction with κ‐carrageenan (CAR), which led to the formation of a promising compound hydrocolloid gel in the food field (such as jelly). Nevertheless, the mixed gels formed by adding KGM to CAR still displayed defects in gel strength and syneresis, and would hardly meet the quality requirements of some gel foods. However, deacetylated KGM and maltodextrin (MD) have always been used in gel foods and affect their viscosity and rheological properties. RESULTS In our paper, different amounts of MD were first used to alter the textural properties, and the results showed that both tensile strength and elongation exhibited first an increasing and then a decreasing trend with the increasing MD proportion and achieved a maximum at a final maltodextrin proportion of 4 g kg−1 in the KGM/CAR/MD system. Based on the above results, we further explored the effects of deacetylation degree of KGM on the gel properties of mixed gel system. The results revealed that, compared to the native KGM, the partial deacetylated KGM was capable of significantly improving the tensile strength and elongation of KGM/CAR mixed gel. CONCLUSION Our study found that the appropriate addition of MD (0.4%) and DKGM were able to alter the tensile properties of KGM/CAR mixed gel, with potential to meet the needs of consumers and further design innovative tensile gel products in the soft gel industry. © 2021 Society of Chemical 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 | 0.000 |
| 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".