Technological Properties of Beef Emulsions Prepared with a Novel Processed Potato Ingredient (O’Brien’s Best)
Bibliographic record
Abstract
ObjectivesThe processed potato ingredient tested in this study was a commercially available ingredient (O’Brien’s Best; Botaniline Foods, LLC) that consists of skinned, sliced potatoes that were cooked to an exact time/temperature to enhance physiochemical properties. The objective of the study was to assess the technological properties of beef emulsion modeling systems prepared with the novel processed potato ingredient (O’Brien’s Best).Materials and MethodsThe meat used in this study was lean ground beef from one master batch of beef that was targeted to 90% lean and 10% fat. The meat batter formulations contained 20% water, 6.15% spice/seasoning, 0.18% Prague powder, and 0.0035% sodium erythorbate, and varying quantities of sodium tri-polyphosphate, salt (NaCl), and binders (processed potato, tapioca starch, or all-purpose binder). In total, eight treatments were formulated and manufactured on three separate, independent occasions (N = 24 experimental units; n = 3 replications). Three treatments were formulated with the novel processed potato ingredient (formulated without phosphate, 0.635% NaCl, and either 5, 10, or 15% the processed potato ingredient). Three treatments were formulated with commercially sourced tapioca starch (formulated without phosphate, 0.635% NaCl, and either 5, 10, or 15% commercial tapioca starch, which was tested to be 78% starch purity). Two treatments were formulated with a commercial formulation [formulated with 0.30% sodium tri-polyphosphate, 10% all-purpose binder (a multi-ingredient proprietary blend binder from Herman Laue Spice Company Inc.; Uxbridge, Ontario), and 1.905% NaCl, or 1.270% NaCl]. Parameters tested were cooking loss, proximate composition of cooked meat batters, texture profile analysis of cooked meat batters, and instrumental color of uncooked and cooked meat batters. Data were analyzed with the GLIMMIX procedure of SAS v9.4 with a fixed effect of treatment and a random effect of replication. Least square means were separated using the PDIFF option with a Tukey-Kramer adjustment. Differences were considered statistically different at P < 0.05.ResultsCooking loss was not different (P = 0.44) among treatments and ranged from 0.64% to 0.77%, indicating acceptable stability for all emulsion formulations in the study. Proximate composition revealed significant differences (P < 0.05) in moisture, protein, ash, and other components (carbohydrates), while lipid content was unaffected. Texture profile analysis revealed that textural properties were generally unaffected (P > 0.05) by treatment, with the exception of less gumminess (P < 0.05) and less chewiness (P < 0.05) in processed potato formulated emulsions compared with the tapioca starch and commercially formulated emulsions. Instrumental color of uncooked emulsions was affected to a greater degree than instrumental color of cooked emulsions. Yet, when tapioca starch was included at high levels (> 10%) in cooked emulsions lightness (L*) and yellowness (b*) were greater (P < 0.05) compared with emulsions formulated with the processed potato ingredient and with the commercial formulations.ConclusionIn summary, the technological properties (cooking loss, texture profile analysis, and instrumental color) of beef emulsion systems were largely unaffected by the processed potato ingredient (despite removal of phosphates and less NaCl) and performed similar to the commercial formulations.
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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".