Response surface modeling and process optimization of aqueous extraction of natural pigments from Beta vulgaris using Box–Behnken design of experiments

• Published in: Dyes and pigments Vol. 111; pp. 64 - 74
• Main Authors: Swamy, Gabriela John, Sangamithra, A., Chandrasekar, V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2014
• Subjects: Aqueous extraction; Beetroot; Box–Behnken design; Natural colorants; Optimization; Response surface methodology; Beetroot; Natural colorants; Box–Behnken design; Response surface methodology; Aqueous extraction; Optimization
• ISSN: 0143-7208; 1873-3743
• DOI: 10.1016/j.dyepig.2014.05.028

The aim of the study was to investigate the influence of independent process variables like extraction temperature, time and mass of beetroot on the aqueous extraction of betalain from beetroot. The optimum conditions for the aqueous extraction of betalamic acid, betaxanthin and betacyanin from beetroot were performed using a three-factor and three-level Box–Behnken design under response surface methodology. The pigments were extracted from beetroot at temperature (40–70 °C), time (30–90 min) and mass of beetroot (0.5–1.5 g) using water as solvent. The data obtained from the experiments were analyzed by Pareto analysis of variance. Further, the data was fitted to a second-order polynomial equation using multiple regression analysis. The optimal conditions based on both individual and combinations of all responses (extraction temperature – 60 °C, time – 84 min and mass – 1.5 g) were found out. At this optimum condition, the total betalamic acid, betaxanthin and betacyanin content were found to be 9.9 mg/100 g, 16.3 mg/100 g and 30.9 mg/100 g with desirability value of 0.999. The experimental values closely agreed with the corresponding predicted values. •Betalain was extracted from red beetroot.•Extraction temperature, time and mass of beetroot were the independent variables.•Box–Behnken design of experiments was used to derive optimum process parameters.•Second order polynomial regression models were developed.•Optimal conditions were deduced by Derringer's desired function methodology.