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Record W2994331438 · doi:10.221751/rmc2016.039

Use of Beef Collagen in Beef Hot Dogs

2017· article· en· W2994331438 on OpenAlex
G. Prabhu, R. Husak, Hugo G. Hulshof

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMeat and Muscle Biology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsnot available
Fundersnot available
KeywordsIngredientFood scienceChemistryLipid oxidationBiochemistry

Abstract

fetched live from OpenAlex

ObjectivesThe use of protein ingredients in meat products is becoming increasingly popular to increase protein levels, as a meat substitute due to its lower cost compared to meat, improve product texture, increase cook yields or to enhance final product flavor. Beef collagen comes from the corium layer of beef hides is an allergen-free, functional protein ingredient which can replace some or all of the traditional binders and other Group 2 protein ingredients like mustard (a declared allergen in Europe and Canada) in meat products. Beef collagen can also be used to replace meat to provide cost savings. Beef collagen is USDA/FSIS approved and listed on Directive 7120.1 for various comminuted meat products where binders are permitted not to exceed 3.5% of product formulation. The objective of this study was to evaluate quality characteristics of beef hot dogs by utilizing beef collagen with or without mustard flour as a Group 2 protein to maximize the added water. Materials and MethodsThree treatments of beef hot dogs were formulated: Control with 0.6% mustard flour (added water = 8.78), TRT 2: 0.6% mustard flour + 0.4% Beef collagen + 1.37% additional water (added water = 8.78), TRT 3: 1.01% Beef collagen + 2.83% additional water (added water = 10). Lean beef with 10% fat, salt, sodium phosphate, sodium nitrite, sodium erythorbate and half the water/ice were chopped in a bowl chopper to a temperature of 12°C. Beef with 50% fat, rest of the dry ingredients and the remaining water were added to the bowl chopper and chopped until the temperature reached 21°C. The emulsion was stuffed into a 22 mm diameter cellulose casings and cooked in a smokehouse to an internal temperature of 71.6°C. Hot dogs were stabilized using USDA Appendix B guidelines, peeled, vacuum packaged and stored in a cooler at 4°C for 120 d. Hot dogs were evaluated for cook yield by difference in weight before and after cooking, texture profile analysis (TPA) using a Texture Analyzer equipped with a 1-cm diameter stainless steel probe, with compression setting to 30% of hot dog of 2.54-cm height. TPA was measured on hot dogs that were warmed on a Model 12 Star Roller Grill set on medium heat for 15 min. Interior color (L, a, and b values) was measured using a handheld Hunterlab color reflectance meter equipped with a D65 Optical Sensor. Purge was measured by difference in weight of product and weight of liquid expelled in the package over 12 wk of refrigerated storage of vacuum packaged hot dogs. The study was replicated three times and statistical analysis was performed using ANOVA (P 0.05) different for TRT 3 compared to control. Hunterlab interior L values were significantly (P 0.05) different for any of the treatments. Purge was significantly (P < 0.05) lower for all test treatments compared to control over 12 wk of refrigerated storage of vacuum packaged hot dogs. ConclusionBeef collagen is an allergen-free functional Group 2 protein ingredient that can be used to replace other protein ingredients or meat to increase cook yields, reduced purge and improve texture in comminuted meat products.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.288
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it