Processing Characteristics, Composition, Shelf-life, and Sensory Attributes of Beef Bacon Manufactured From Seven Value-Added Cuts of Beef
Bibliographic record
Abstract
The purpose of this study was to evaluate the influence of different beef cuts on their potential for adding value by assessing processing characteristics, composition, shelf-life, and sensory attributes of these cuts as beef bacon. Six briskets (Institutional Meat Purchase Specification [IMPS]#120), 6 clod hearts (IMPS#114E; divided horizontally into 2 halves: silver-skin side and non–silver-skin side), 6 flanks (IMPS#193), 6 outside flats (IMPS#171B), and 7 short plates (IMPS#121A; cut into a deboned short-rib half and navel half) were sourced commercially from separate Canadian quality grade AA beef carcasses. Data for processing yields, composition, and image analysis were analyzed as a generalized linear mixed model with fixed effect of cut and random effect of replication nested within block (processing group). Sensory data collected using a trained sensory panel were analyzed in the same manner, with an additional fixed effect of storage day and additional random effects of session and panelist. Rested pump uptake, which was targeted at 20%, was not different (P = 0.21) among cuts; however, smokehouse cook yield differed (P < 0.01) among cuts, with heavier cuts (brisket, plate cuts, and outside flat) generally having greater yields compared with lighter cuts (clod cuts and flank). As expected, composition of bacon slices was affected (P < 0.01) by cut, with leaner cuts (clod cuts, flank, and outside flat) having greater moisture, lower lipid levels, and greater protein compared with fatter cuts (brisket and plate cuts). Sensory analysis revealed significant differences in muscle fiber toughness and connective tissue among cuts. The differences that were quantified in this study should allow manufacturers to tailor their cut selection to the processing specifications that may be most profitable and well-suited for the meat industry and its customer base. Overall, this research should help define beef bacon and further indicate that a variety of beef cuts can be used to manufacture beef bacon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".