The Effect of Selected Starches on Hydration, Textural and Sensory Characteristics of Restructured Beef Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Wet‐extracted ( WEPS ) and air‐classified pea starches were included at 3 g/100 g in a restructured beef with 0, 20 or 40 g/100 g water addition. Hydration and textural properties, and consumer acceptance were evaluated, and performance of pea starch‐added products was compared with a product made with modified cornstarch ( MCS ) and a starch‐free control. The meat protein/starch matrix in the restructured products was sufficient to entrap water during cooking, although the systems were less stable with 40 g/100 g water addition leading to greater moisture losses. Increased moisture content resulted in products with decreased hardness and chewiness. Compared with control, all starches improved hydration properties of the products containing additional water; however, MCS performance was superior to pea starch, especially for purge control. Firmness acceptability of MCS and WEPS samples was lower than that of the starch‐free control; however, starch type did not affect color, flavor or overall acceptability. Practical Applications Results of this study indicate a potential for using pea starches to improve the processing characteristics of restructured meat products. Of the three starches tested, modified cornstarch was found to be superior for controlling purge and improving hydration properties of water‐added products; however, the pea starches still performed better than the control for moisture retention in corned beef. Pea starches could be a viable and a lower‐cost formulation alternative to traditional cornstarches and therefore create new opportunities for extending their utilization in food systems.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 it