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.

• Do-Gyun Kim(Department of Biosystems Machinery Engineering, Chungnam National University)
• Wang-Hee Lee(Department of Biosystems Machinery Engineering, Chungnam National University)