Use of response surface methodology (RSM) for composite blends of low grade broken rice fractions and full-fat soybean flour by a twin-screw extrusion cooking process

Abstract

In this study, seventeen (17) composite blends of broken rice fractions and full-fat soybean, formulated using response surface methodology and central composite design within a range of barrel temperatures (100-140 °C), initial feed moisture content (15-25%) and soybean composition (8-24%), were extruded with a twin-screw extruder and the expansion and color indices were optimized. The results indicated a significant (p<0.05) effect of extrusion conditions on the responses. Fitted predictive models had coefficients of 88.9%, 95.7%, 97.3%, 95.4% and 95.2%, respectively, for expansion index, bulk density, lightness, redness and yellowness. The p-value and lack-of-fit tests of the models could well explain the observed variability and therefore could be used to establish production setting for the twin-screw extruder. The optimum extrusion conditions were found to be 130 °C (barrel temperature), 20% (feed moisture level) and 23% feed soybean composition and optimum responses in terms of bulk density, expansion index, lightness, redness and yellowness chroma indices were 0.21 g cm-3, 128.9%, 17.1, 3.13 and 24.5 respectively. This indicates that optimum conditions can be established in twin-screw extrusion cooking of broken rice fractions and full-fat soybean composite blends that can result in product of low bulk and maximum expansion with a satisfactory light yellow product color that can be used to produce products that valorize broken rice and reduce qualitative postharvest loss.

Prediction of the fiber diameter of melt electrospinning writing by kriging model

“…As an effective statistical technique for optimizing complex processes, it has been widely used for experiments design. 37,38 The RSM is a method, which based on the mathematical models (linear, square polynomial functions, and others), to fit the experimental results generated from the designed experiment. Meanwhile, statistical techniques were used to validate the model.…”

“…It is widely used in the optimizing of engineering processes for its relatively small number of experiment requirements. 38,39 The number of experiments required for the development of CCF is 2 k + 2 k + n c , in which 2 k and n c represent the number of axial and center points, respectively. A total of 31 experiments were employed in this work including seven replicates at the central point.…”

“…RSM is an effective statistical technique to investigate the interaction effects of several independent variables on one or more responses. As an effective statistical technique for optimizing complex processes, it has been widely used for experiments design 37,38 . The RSM is a method, which based on the mathematical models (linear, square polynomial functions, and others), to fit the experimental results generated from the designed experiment.…”

“…CCF is practicable and high efficiency. It is widely used in the optimizing of engineering processes for its relatively small number of experiment requirements 38,39 . The number of experiments required for the development of CCF is 2 k + 2 k + n c , in which 2 k and n c represent the number of axial and center points, respectively.…”