Application of Response Surface Methodology based D-optimal Design for Modeling and Optimisation of Osmotic dehydration of Zucchini

Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

“…In this paper, feed rate, spindle speed and drill type are treated as the explanatory variables, and thrust force, torque and delamination factor are the responses. In regression analysis, there are three basic least-square methods (LSMs), i.e., ordinary LSM, weighted LSM and generalized LSM available for training, testing and predicting unknown responses for given sets of explanatory variables with high accuracy [33][34][35]. Among them, ordinary or weighted LSM is mostly adopted to model simple linear regression equations in conjunction with dummy variable coding and dataset alteration [34].…”

Performance enhancement of up-flow anaerobic sludge blanket reactor for psychrophilic temperature during the dry season: Kality wastewater treatment plant

“… Where; Y is the experimental response variable, is the intercept, , and are the regression coefficients for intercept, linear effect, double interaction and quadratic effects respectively. , are the independent variables (experimental variables) and Ԑ is random error [ 24 , 25 ]…”

Effects of ultrasound time, xanthan gum, and sucrose levels on the osmosis dehydration and appearance characteristics of grapefruit slices: Process optimization using response surface methodology

“…RSM is a set of statistical designs that determine the relationships among several independent variables and one or more dependent variables [12] , [13] . In a study, Rahman et al, [14] proposed that the optimum condition for lactose concentration using the RSM test setup for osmosis dehydration of zucchini as 49.99%. In this work, the authors achieved 5.88 g/g idm moisture loss, 0.872 g/g idm sucrose absorption, and 0.704 g/g idm moisture content.…”