Sensory, Physicochemical and Microbiological Characteristics of Venison Jerky Cured with NaCl and KCl
Bibliographic record
Abstract
Traditionally, jerky is produced from sliced whole muscle marinated in a high sodium chloride (NaCl) concentration and dried. Because a high salt diet has been linked to hypertension, salt substitutes are often recommended as a healthier alternative. However, potassium chloride (KCl), a popular salt substitute may impart an undesired bitterness and metallic aftertaste. The objective of this study was to evaluate specific attributes of venison jerky prepared in three different (NaCl/KCl) salt solutions. Through sensory testing, each preparation was evaluated for consumer product acceptance and purchase intent. Additionally, the venison jerky was assayed for physicochemical characteristics and microbial counts. Using a 9-point hedonic scale, sixty-eight consumers evaluated the jerky for acceptability of flavor, texture, taste, saltiness, bitterness and overall liking. Physicochemical characteristics were evaluated for moisture content, pH, color and TBAR. Jerky was assayed for microbial counts via aerobic plate count, Escherichia coli, Staphylococcus aureus and Campylobacter spp. Results show that jerky prepared with 100% KCl received the most desirable score (8.75), compared to jerky prepared with 100% NaCl (6.28), and jerky prepared with 50% NaCl + 50% KCl (6.13). Acceptability and purchase intent questionnaires indicate jerky prepared with 100% KCl ranked the highest at 86.8% and 70.6%, respectively. Jerky prepared with 100% KCl had the lowest moisture content, TBAR, and a* values (P<0.05). No E. coli, S. aureus and Campylobacter spp. were detected over the 28 day period. Our study suggests that jerky prepared with KCl represents a low sodium alternative to traditional jerky.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".