Properties of cheese and ground beef in the presence of staghorn sumac
Bibliographic record
Abstract
Abstract Cost‐effective and under‐exploited plant species as functional food ingredients represent alternatives to chemicals with safety concerns and potent toxicity. Staghorn sumac (Rhus typhina) is considered a good source of natural polyphenols, which can be introduced into food products to carry out some specific technological functions (e.g., antioxidation and preservation). However, sumac fruit as a spice is under‐explored for its diversified uses in various food products as a novel functional ingredient. This study explored the technological feasibility and sensory acceptability of two representative sumac‐fortified food systems, namely Cheddar cheese and ground beef. Extracts of sumac (0.5%−5%) dose‐dependently increased the total phenolic and anthocyanin contents, while reducing thiobarbituric acid reactive substance (up to 46%), total plate counts of bacteria (up to 4.5 log colony‐forming units [CFU]/g) and pH of the cheese. In fresh ground beef, sumac extract at concentrations of 1%−5% was effective in reducing the total aerobic counts of Escherichia coli inoculated fresh ground beef by 0.16−1.14 log CFU/g, while improving meat color and oxidative stability during a storage period of 7 days at 4°C. Phytochemical profiling by high‐performance liquid chromatography showed the presence of gallic acid, caffeic acid, ellagic acid, quercetin and anthocyanins in the sumac extracts. These compounds were possibly responsible for the antimicrobial and antioxidant activities in the evaluated food systems. Sensory acceptability of Cheddar cheese or cooked beef meatballs with sumac extract addition (0.5%−3%) were similar to their respective controls. This study provides a feasible approach to satisfy health‐conscious consumers who favor a natural multifunctional ingredient to preserve food products.
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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) | 0.001 | 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".