Tyrosinase is a key enzyme in animal melanogenic pathway that is the rate-limiting step for the production of melanin. Several synthetic and naturally occurring tyrosinase inhibitors have been studied for skin whitening, the development of natural agents is becoming more important due to the disadvantages of synthetics such as high cytotoxicity, insufficient penetration power, and low activity. The purpose of this study was to evaluate the polyphenols, antioxidant and tyrosinase inhibition activity of mealworm (Tenebrio molitor) extract and optimization of extraction condition using statically-based optimization. The results showed that extraction temperature of 92.0°C, extraction time of 36.7 min, and ethanol concentration of 79.5% provided the maximum levels of compounds of 5.69 mg GAE/g DW, tyrosinase inhibition of 78.8%, and radical scavenging activity of 100.8 μg/ml in the validation experiment.

본 연구는 표고균사체 발효 적하수오 열수 최적 추출조건 탐색을 목적으로 수행하였으며, 표고균사로 배양된 하수오의 독립변수로는 독립변수(Xi)로서 추출온도(X1), 추출시간(X2) 및 시료에 대한 용매비(X3)에 대한 실험범위를 설정하여 각각을 5단계로 부호화하였고 중심합성계획에 따라 16구로 설정하여 추출하였다. 또한 이들 독립변수에 영향을 받을 종속변수(Yn)로는 고형분 함량(Y1) 및 당도(Y2), TPC (total polyphenol contents, Y3), TFC (total flavonoid contents, Y4), ABTS cation radical scavenging activity (Y5), DPPH radical scavenging activity (Y6)으로 하였다. 고용분 함량 최대값과 최소값은 각각 31.99%와 16.84%로 나타났다. 이를 회귀분석한 결과 고형분 함량에 대한 모델식의 R2은 0.867로 나타났다. 당도는 7번(8 hrs, 85℃, 67 mg/mL)과 15번(6 hrs, 70℃, 100 mg/mL)시험구에서 3.0 brix로 다른 시험구에 비하여 높은 함량을 보였다. 항산화물질인 총 폴리페놀과 플라보노이드 함량은 8번 시험구(8 hrs, 85℃, 40 mg/mL)에서 총 폴리페놀 함량이 30.40 mg GAE/g, 총 플라보노이드 함량이 146.50 mg QE/g으로 가장 높게 나타났으며, 16가지 시험구의 항산화 활성도 총 폴리페놀과 총 플라보노이드 함량의 결과와 같이 8번 시험구(8 hrs, 85℃, 40 mg/mL)에서 가장 높은 활성을 보였다. 이상의 결과를 참조하여 표고균사체 발효 적하수오에 대한 열수 추출조건을 최적화할 목적으로 추출온도를 95℃로 고정하고 용매비와 추출시간에 대한 종속변수들의 contour map을 superimposing하여 추출물의 특성 중 생리활성물질 함량과 생리활성을 만족시켜주는 최적조건은 91.22℃에서 39.71 mg/mL 용매비와 7.72 추출시간으로 각각 분석되었다. 본 연구는 적하수오에 표고균사체를 배양한 후 생리활성이 우수한 원료를 추출 생산하고자 최적 추출조건을 확립하기 위하여 반응표면분석법을 활용하였다. 향후 생리활성부분에 대한 추가적인 연구가 이루어진다면 더욱 완성도가 높은 표고균사체 발효 적하수오 식품개발에 가능할 것으로 사료된다.

The aim of this study was to evaluate the optimization extrusion variables on quality of textured vegetable protein by using response surface methodology. In this study, 50% soy protein isolate, 40% wheat gluten, and 10% corn starch were blended and 15% of the mixture was substituted with green tea. The moisture content (45, 50, and 55%), barrel temperature (130, 140, and 150oC), and screw speed (100, 150, and 200 rpm) were varied. A Box- Behnken design was used in this experiment. Second order polynomial regression equations were developed to relate the response to extrusion variables as well as to obtain a response surface plot. The independent variables had significant effects on the quality of the products and moisture content was the most significant. The lower moisture content led to the higher integrity index, lower nitrogen solubility index, lower water absorption capacity, higher texture, and higher cutting strength. The optimum conditions were identified as moisture content 47.78%, barrel temperature 150.00oC, and screw speed 196.05 rpm. Incorporation of green tea into protein materials could effectively improve the nutritional value of the product. Understanding these optimized extrusion variables on the product quality was useful for producing textured vegetable protein in the future.

The purpose of this study was to optimize dough properties using response surface methodology (RSM) and to demonstrate the performances of dough prepared under optimized conditions. Dough mixed with yeast, margarine, salt, sugar and wheat flour was prepared by fermentation process. Hardness, cohesiveness and springiness of dough were selected as critical quality attributes. The critical formulations (yeast and water) and process (fermentation time) variables were selected as critical input variables based on preliminary experiment. Box-Behnken design (BBD) was used as RSM. As a result, the quardratic, the squared and the linear model respectively provided the most appropriate fit (R2>90) and had no significant lack of fit (p>0.05) on critical quality attributes (hardness, cohesiveness and springiness). The accurate prediction of dough characteristics was possible from the selected models. It was confirmed by validation that a good correlation was obtained between the actual and predicted values. In conclusion, the methodologies using RSM in this study might be applicable to the optimization of fermented foods containing various wheat flour and yeast.

The purpose of this study was to optimize dough properties using response surface methodology (RSM) and to demonstrate the performances of dough prepared under optimized conditions. Dough mixed with yeast, margarine, salt, sugar and wheat flour was prepared by fermentation process. Hardness, cohesiveness and springiness of dough were selected as critical quality attributes. The critical formulations (yeast and water) and process (fermentation time) variables were selected as critical input variables based on preliminary experiment. Box-Behnken design (BBD) was used as RSM. As a result, the quardratic, the squared and the linear model respectively provided the most appropriate fit (R2>90) and had no significant lack of fit (p>0.05) on critical quality attributes (hardness, cohesiveness and springiness). The accurate prediction of dough characteristics was possible from the selected models. It was confirmed by validation that a good correlation was obtained between the actual and predicted values. In conclusion, the methodologies using RSM in this study might be applicable to the optimization of fermented foods containing various wheat flour and yeast.

Response surface methodology (RSM) is one of the statistical methods for optimizing dependent variable in response to independent variables, and has been used in various field of food engineering. The model coded by RSM has a canonical formulation of 2nd order polynomial with the normalized ranges of independent variables. To accurately accomplish the optimization using RSM an adequate experimental design, i.e., response surface design, is necessary. Response surface design is determined by type of design and number of independent variables. In this study, we are to develop a response surface design applicable for optimizing hulled barley (Hordeum vulgare) production under various conditions of temperature and humidity in forage growing system. As a result, 3 experimental designs were conceived for future RSM; central composite design (CCD), inscribed central composite design (ICCD) and equiradial design. Each design requires experimental trials of 13, 13, and 8, respectively. We will further select one of the designs for actual experiments for finding the optimal temperature and humidity necessary for maximizing fresh forage production in the system.

This study was carried out to extract ginsenosides in by-products from honeyed red ginseng. Response surface methodology (RSM) was used to optimize the extraction conditions. Based on D-optimal design, independent variables were ethanol (extraction solvent) concentration (30-90%, v/v), extraction temperature (25-70oC), and extraction time (5-11 h). Extraction yield (Y1) and total ginsenosides (Y2) in the extract were analyzed as dependent variables. Results found that extraction yield increased with increasing extraction temperature and time, whereas it was decreased with increasing ethanol concentration. Similar trends were found for the content of ginsenosides in the extracts, except for ethanol concentration, which was increased with increasing ethanol concentration. Regression equations derived from RSM were suggested to coincide well with the results from the experiments. The optimal extraction conditions for extraction yield and total ginsenosides were an extraction temperature of 56.94oC, ethanol concentration of 57.90%, and extraction time of 11 h. Under these conditions, extraction yield and total ginsenoside contents were predicted to be 84.52% and 9.54 mg/g, respectively.

본 논문에서는 반응표면법과 다목적최적화 기법을 이용한 유한요소모델개선기법의 절차를 제안하고 이를 저층의 철근콘 크리트건물의 모델개선에 적용하였다. 대상건물은 전단벽 신설 및 댐퍼부착을 위한 부재의 강재보강을 통해 내진보강이 이 루어진 건물로서 보강전후에 소형 가진기를 이용한 진동실험을 실시하여 동특성을 구하였다. 대상건물의 개선에 사용된 변 수는 기존콘크리트, 신규타설된 콘크리트, 조적의 탄성계수, 신축줄눈부의 스프링계수, 강재보강된 부재의 유효강성비이다. 보강전후 건물의 초기모델을 구축한 후 중심합성법에 따라 개선변수의 값을 변화시키면서 얻은 해석결과를 통해 고유진동 수의 오차와 모드형상의 오차를 나타내는 2개의 반응함수를 구하고, 이를 다목적최적화의 목적함수로 사용하였다

본 연구는 표면 반응 분석법을 이용하여 곤드레 데치기 최적 공정 조건을 확립하고 각각의 공정으로부터 생산된 곤드레 추출물로부터 총 페놀 화합물, 플라보노이드의 함량, DPPH 라디칼 소거능 을 분석하였다. 최적 공정 확립을 위한 데치기 공정 범위로 온도는 70℃ - 99℃, 시간은 70 - 170 sec. 로 하였다. 데치기 공정 후 총 페놀 함량의 범위는 13.00 - 35.48 mg/g 이고 최대 예측 총 페놀함량은 35.48 mg/g, 총 페놀 함량의 경향을 보면 데치기 온도에 따라 급격히 감소하다가 증가하는 것으로 보 여 지므로 온도에 의해 더 많은 영향을 받는 것으로 나타난다. 플라보노이드의 함량의 범위는 2.31 - 8.38 mg/g 이고 최대 예측 플라보노이드 함량은 8.38 mg/g 이었다. 플라보노이드 함량의 경향을 보면 데치기 시간에 따라 서서히 증가하다가 급격히 감소하는 것으로 보여 지므로 시간에 의해 더 많은 영향 을 받는 것으로 나타났다. DPPH 라디칼 소거능의 범위는 42.10 - 67.14%이고 최대 예측 DPPH 라디 칼 소거능은 67.14%로 측정 되었다. DPPH의 경향을 보면 데치기 온도에 따라 증가하다가 급격히 감소 하는 것으로 보여 지므로 온도에 의해 더 많은 영향을 받는 것으로 나타난다. 따라서 데치기 공정 최적 조건은 온도 85℃와 시간 150 sec. 로 결정되었다.

RSM (response surface method) is a statistical method that optimizes a response variable (dependent variable) according to multiple explanatory variables (independent variable) [1]. RSM visualizes responses of the target depending on experimental conditions, using a regression equation containing an intercept, and coefficients of first-order, second-order, and interactive terms (equation 1). Response surface experimental design is a method for designing RSM experiments [2] which aims to identify the optimal number of trials (number of data points) and number of conditions (range of experimental variables) according to the order of the regression model. Generally, the number of trials in an experiment is composed of central points, factorial points, and axial (or star) points, which varies depending on the number of variables. In this study, we used three widely used response surface experimental designs, i.e., simplex, central composite, and equiradial designs to propose experimental set-up applicable for a future study regarding the effects of storage conditions (e.g., temperature and humidity) on glucosinolate content.

The purpose of this study was to determine the optimal mixing ratio of olive oil, parmesan cheese, and nuts for preparation of pesto with spinach. Based on a surface response methodology (RSM), the independent variables were olive oil (100~300 g), parmesan cheese (25~75 g) and nuts (12~36 g). The dependent variables were physico-chemical properties as pH, color values (L, a, and b values), viscosity and sensory evaluation. pH decreased with increasing parmesan cheese levels. The L value decreased and the a and b values increased with increasing olive oil levels. Viscosity was negatively correlated with olive oil levels but positively correlated with the amounts of parmesan cheese and nuts. The addition of oil exerted a positive effect on gloss, and oily taste. In the sensory evaluation, the values of appearance, color, flavor, taste and overall acceptance were in the range of 4.4~7.2, 3.7~7.4, 4.4~7.1, 3.9~7.3 and 3.5~7.6, respectively. The sensory evaluation results showed significant values in appearance (p<0.01), flavor (p<0.01), taste (p<0.01) and overall acceptance (p<0.05). The optimal amounts of spinach pesto with olive oil, parmesan cheese, and nuts were determined by numerical optimization of a canonical model and graphical optimization. The optimal amounts were 175.29 g of olive oil, 49.51 g parmesan cheese, and 27.37 g of nuts per 100 g of spinach.