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

• Main Authors: Nahemiah Danbaba, Iro Nkama, Mamudu Halidu Badau
• Format: Article
• Language: English
• Published: ISEKI_Food Association (IFA) 2019-04-01
• Series: International Journal of Food Studies
• Subjects: broken rice; soybean; extrusion; response surface methodology; optimization
• Online Access: https://www.iseki-food-ejournal.com/ojs/index.php/e-journal/article/view/499

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.