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돼지 뒷다리고기의 대체로써 머릿고기의 첨가 수준이 비유화형 훈연 가열 소시지의 냉장저장 중 이화학적 품질특성에 미치는 영향
강선문,김윤석,설국환,성필남,조수현,김진형,Kang, Sun Moon,Kim, Yunseok,Seol, Kuk-Hwan,Seong, Pil-Nam,Cho, Soohyun,Kim, Jin-Hyoung 한국식생활문화학회 2021 韓國食生活文化學會誌 Vol.36 No.1
This study investigated the effect of the addition of various levels of pig head meat (HM) as a substitute for rear leg meat (RLM) on the physico-chemical quality characteristics of non-emulsified, smoked, and cooked sausage during refrigerated storage. Sausages were prepared in four variations according to the proportion (0%, 10%, 20%, or 30%) of HM added and maintained at 4℃. Quality measurements were taken for 28 days. The sausages added with the addition of 20% and 30% HM had significantly (p<0.05) higher moisture and lower protein content compared to those without the addition of HM. The pH value during the storage period was higher (p<0.05) in the sausages to which the HM had been added than in those without HM. The sausages with 30% HM showed the lowest (p<0.05) L⁎ and b⁎ values and the highest (p<0.05) a⁎ value during the storage period. The 2-thiobarbituric acid reactive substances (TBARS) content, hardness, cohesiveness, springiness, gumminess, and chewiness of the sausages showed no significant variations with the addition of various levels of HM. These data suggest that RLM could be substituted with 30% HM because it does not negatively affect the quality of the non-emulsified sausage. However, a further study on sausages made with 100% HM instead of RLM may be needed to improve its utilization.
최인호(InHo Choi),김윤석(YunSeok Kim) 한국통신학회 2007 韓國通信學會論文誌 Vol.32 No.12D
본 연구는 패킷망에서 보다 고품질의 실시간 서비스를 위한 트래픽 전송속도 변화에 관한 예측 정보를 제공할 수 있는 모델을 제안하는 것으로써 예측 알고리즘 방법으로는 비선형 예측에 우수한 성능을 보이는 신경망(Neural Network)을 기본으로 하며 실시간 학습과 예측이 가능한 3중신경망을 사용하여 변화하는의 트래픽(패킷) 전송속도를 학습하고 이를 토대로 앞으로의 유입될 트래픽 전송속도를 실시간으로 예측하기 위한 모델이다. This paper is a study on the prediction of Packets transmission time using thr tripple neural network model is used. Because there is not the traffic model of multi-media to make clear, the traffic model is assumed to be non-linear time variable funtion. For real-time prediction of it, the tripple neural network model which is composed with parallel triple neural networks is used. From the result, it's capability is shown that the tripple neural network model can be used in the real-time prediction of Packets transmission time.
아질산나트륨 및 비타민 C 대체로 첨가한 수벌번데기 분말이 유화형 소시지의 이화학적 품질 특성에 미치는 영향
강선문,맹아란,성필남,김진형,조수현,김윤석,최용수,Kang, Sun Moon,Maeng, Ah Ran,Seong, Pil-Nam,Kim, Jin-Hyoung,Cho, Soohyun,Kim, Yunseok,Choi, Yong-Soo 한국식품영양학회 2018 韓國食品營養學會誌 Vol.31 No.6
This study estimated the effect of drone pupa meal (DPM) added as replacement of sodium nitrite (SN) and vitamin C (VC) on physico-chemical quality characteristics of emulsion-type sausages. Samples were prepared either with 150 ppm SN+200 ppm VC (control); 75 ppm SN+100 ppm VC+6.015% DPM (T1); or 12.03% DPM (T2) and then stored at $4^{\circ}C$ for 30 days. The pH value decreased (p<0.05) with increase in the levels of DPM. Moisture and protein content decreased (p<0.05) but fat and ash content increased (p<0.05) with higher levels of DPM. T1 and T2 had higher (p<0.05) saturated fatty acids content and lower (p<0.05) unsaturated and polyunsaturated fatty acids content compared to the control. Lower (p<0.05) $L^*$ and $a^*$ values and higher (p<0.05) $b^*$ and $h^{\circ}$ values were exhibited in the T1 and T2 than in the control; and $C^*$ value was the lowest (p<0.05) in T2. The TBARS content was the highest (p<0.05) in T2, especially, 2 times higher (p<0.05) than in the control. T1 and T2 had harder (p<0.05) texture compared to the control. These findings suggest that the DPM has no replacement effects against SN and VC in emulsion-type sausage, but it has negative effects on color, lipid oxidation stability, and texture.