Ultrasound-assisted production of corn starch: Process design and optimization

Abstract

Most starch industries utilize maceration as the extraction method to recover starch from corn matrices; however, the production is time-consuming and often inefficient. Ultrasound-assisted extraction (UAE) appears to propose an enhancement of the extraction process. Hence, in this study, UAE has been developed to reach an optimum recovery in a short extraction time for starch obtaining from corn grain. Several extraction variables were studied, including ultrasound power (x 1, 30-90%), cycle (x 2, 0.3-0.9 s−1), and solvent to sample ratio (x 3, 10:1-30:1 g ml−1). A Box-Behnken design, coupled with Response Surface Methodology, was performed to optimize the three studied variables. The acquired mathematical model was used to estimate a predictive equation for the extraction yield. The model was validated based on an acceptable coefficient of determination (R 2, 0.88), a low mean absolute error, and the value of lack-of-fit suggested that the model was adequate in explaining the observed data at the 95.0% confidence level. The predicted optimum yield was achieved by applying UAE conditions as follows: 87% ultrasound power, 1.0 s−1 cavitation cycle, and 30:1 solvent to sample ratio. Additionally, a two-cycle extraction process of 5 min for each cycle was chosen over a single extraction cycle in 25 min. The resulting corn starch produced by UAE could maintain the low viscosity even in high temperatures. Hence, apart from accelerating the extraction, UAE was also useful to modify the starch characterization. Moreover, the proposed method demonstrated a green extraction procedure, as there was no presence of any additional organic solvent.