Quality Characteristics on Adding Blood Levels to Blood Sausage
Bibliographic record
Abstract
본 연구는 영양학적으로 우수한 혈액을 활용한 식육제품의 품질 특성에 미치는 영향을 규명하여 혈액 소시지를 개발하고자 실시하였다. 수분함량, 명도, 적색도, 황색도, 지질 산패도, 응집성, 검성 및 씹음성은 혈액 첨가량이 증가함에 따라 감소하는 경향을 보였으나, 단백질 함량, 지방 함량, 회분 함량, 휘발성 염기태질소 함량은 혈액 첨가량에 따라서 유의적인 차이를 보이지 않았다. 경도 및 탄력성은 혈액을 20% 첨가한 처리구가 다른 처리구들에 비하여 높은 수치를 나타내었다. 또한 혈액을 20% 첨가한 처리구가 전체적인 기호도에서 가장 높은 점수를 받았으며, 색, 풍미, 연도 및 다즙성에서도 혈액을 20%첨가한 처리구가 우수한 평가를 받은 것으로 보여진다. 따라서, 활용도가 낮은 돈육 부산물 중 혈액을 활용하여 혈액 소시지를 제조할 시 혈액의 첨가량이 20%로 하는 것이 혈액 소시지의 품질 및 관능적으로 우수한 혈액 소시지를 제조할 수 있을 것으로 보여진다. This study evaluated the effects of adding blood levels to phycochemical properties, textural properties, and sensory characteristics of blood sausage. 4 treatment groups of blood sausage were produced, T1 (pork ham : pork blood = 60:15), T2 (55:20), T3 (50:25), and T4 (45:30). T1 had the highest moisture content, most cohesiveness, and gumminess, CIE L-value, CIE a-value, and CIE b-value of raw and cooked blood sausages. Protein content, fat content, ash content, and VBN values were not significantly different among the treatments. T4 was treated with the most added pork blood, and had the highest pH of raw and cooked blood sausages, cooking loss, and TBA values. T2's sausage was the hardest, but had more springiness, cohesiveness, gumminess, and chewiness than T4. The best scores were from T4 and had the most overall acceptability. The results of this study show that blood sausages containing 20% pork blood had higher improved quality characteristics in blood sausages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".