Texture modification of easily chewable pork meat batter for masticatory dysfunction people: effects and interactions of bromelain, κ‐carrageenan, and plant protein hydrolysates
Bibliographic record
Abstract
Summary Meat intake in masticatory dysfunction people is commonly reduced because the relatively tough texture of meat can impair mastication. Pork meat batters were prepared by different levels of bromelain (0%, 0.5% w/w), κ‐carrageenan (0.5%, 1.0% w/w), and rice berry and mung bean ratios (0:0, 1:0, 0:1, 0.5:0.5). The experiment was designed to study the effect of infusion treatments and their interaction on the properties of pork meat batter. Lower cooking losses (2.33%–3.45%) was observed in the samples with 1.0% (w/w) κ‐carrageenan. Hardness, cohesiveness, chewiness, and gumminess of samples with bromelain were lower, and higher in samples containing 1.0% (w/w) κ‐carrageenan. Bromelain also resulted in decreasing storage modulus ( G ′) and loss modulus ( G ″) values compared to nonbromelain‐treated samples. Microstructure revealed that connective tissue and muscle fibres were disrupted in bromelain‐treated meats while incorporation of plant proteins led to better binding of meat particles. These results revealed that bromelain has a potential to produce a softer texture batter, while 1.0% (w/w) κ‐carrageenan and plant proteins have potential to improve textural properties and shape a product. This implies that the effects and interactions of infusion treatments offer the possibility to improve texture‐optimised product for masticatory dysfunction people, such as elderly.
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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".