Cooking loss, texture properties, and color of comminuted beef prepared with breadfruit (Artocarpus altilis) flour
Bibliographic record
Abstract
Cooking loss, texture properties, and color of comminuted beef when prepared with breadfruit (Artocarpus altilis) flour or other flour sources was evaluated using 2 separate studies. Flour sources tested in these studies (against a negative control with no added flour) were breadfruit flour, soy flour, corn flour, wheat flour, and tapioca flour. Study 1: Finely minced, comminuted beef batters (extra lean beef targeted to 97% lean and 3% fat, salt, and ice/water) prepared with inclusion levels of 0, 1, 2, 3, 4, and 5% flour were evaluated for cooking loss and texture. Cooking loss was reduced (P < 0.05) in comminuted beef prepared with breadfruit flour compared with those not prepared with flour and cooking loss decreased as breadfruit flour inclusion level increased (Linear P < 0.01). Hardness was not different (P = 0.49) in comminuted beef prepared with breadfruit flour compared with soy flour, and was much less (P < 0.01) compared with the 3 other flour sources at each inclusion level. Study 2: Comminuted beef (lean beef targeted to 90% lean and 10% fat, salt, and ice/water) with inclusion levels of 0, 2.5, and 5% flour were formed into patties and were evaluated for color over a simulated retail display period. Redness values (a*) of comminuted beef prepared with breadfruit flour were the greatest (P < 0.05) during the 7-d simulated retail display compared with all other treatments, including control samples with no flour. Overall, the results indicated that breadfruit flour could be effectively used as an ingredient in comminuted beef to produce similar texture as observed with soy flour, while actually improving redness values beyond that of other flour sources.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".