Using Box–Behnken Design Coupled with Response Surface Methodology for Optimizing Rapeseed Oil Expression Parameters under Heating and Freezing Conditions

Abstract

The effect of heating and freezing pretreatments on rapeseed oil yield and the volume of oil energy under uniaxial compression loading was investigated. Four separate experiments were carried out to achieve the study objective. The first and second experiments were performed to determine the compression parameters (deformation, mass of oil, oil yield, oil expression efficiency, energy, volume of oil and volume of oil energy). The third and fourth experiments identified the optimal factors (heating temperatures: 40, 60 and 80 °C, freezing temperatures: −2, −22 and −36 °C, heating times: 15, 30 and 45 min and speeds: 5, 10 and 15 mm/min) using the Box–Behnken design via the response surface methodology where the oil yield and volume of oil energy were the main responses. The optimal operating factors for obtaining a volume of oil energy of 0.0443 kJ/mL were a heating temperature of 40 °C, heating time of 45 min and speed of 15 mm/min. The volume of oil energy of 0.169 kJ/mL was reached at the optimal conditions of a freezing temperature of −36 °C, freezing time of 37.5 min and speed of 15 mm/min. The regression model established was adequate for predicting the volume of oil energy only under heating conditions.

Valorization of Coffee Silverskin through Subcritical Water Extraction: An Optimization Based on T-CQA Using Response Surface Methodology

“…BBD is an independent rotatable quadratic design with no embedded factorial or fractional factorial points [36,37]. It has been commonly used for the optimization of plant material extraction processes [39][40][41][42]. In this study, the effects of the three independent variables (i.e., extraction temperature of 120-240 • C, time of 10-60 min, and the solid-liquid ratio of 1:10-1:40 g/mL) on the response (Y) were investigated.…”

“…As presented in Table 3, the values of R 2 (pred) for the TPC, AA, and T-CQA are in agreement with R 2 (adj). For a value higher than 75%, the regression model can predict the designated response very well [36,[38][39][40]42]. A multiple regression analysis was implemented to determine the coefficients and equations, which can be used to estimate the responses (i.e., TPC, AA, and T-CQA).…”

“…The lack of fit is the parameter used for confirming the validity of the models. If the lack of fit has a p-value > 0.05, the model can precisely fit the experimental data [36,[38][39][40][41][42]. As shown in Table 4, all the developed quadratic polynomial models are consistent and precise for predicting the designated responses (i.e., TPC, AA, and T-CQA).…”

Supercritical Carbon Dioxide Extraction of Vegetable Oils: Retrospective and Prospects

“…A quantitative understanding of various statistical tools such as response surface methodology (RSM) coupled with Box‐Behnken design (BBD), Central Composite Design (CCD), etc., for statistical interpretation and analysis of systems is of considerable importance. [ 115 ] This will help in the design of equipment and in process development involving unit operations or unit processes. The scope for the identification of key variables, with only a few experiments, at an early stage of experimentation is enhanced.…”

Adjustable Gel Texture of Recovered Crude Agar Induced by Pressurized Hot Water Treatment of Gelidium sesquipedale Industry Waste Stream: An RSM Analysis

“…These constraints could be related to challenges such as the complete combination of several factors to evaluate multiple responses. The Box-Behnken design can provide pertinent experimental models that are influenced by the chosen range of process factors with a reduced number of experiments [ 19 ].…”