Optimization of Single Screw Extrusion Processing Variables and Soy and Rice Flour Blend Formulations based on Physical Properties of Extrudates

Abstract

Background: Extruded cereals and snacks are mainly carbohydrates. Most adults require more protein above RDA by restricting carbohydrates for many health benefits. A wide variety of soy protein-rich extrudates can be produced by blending with rice flour. However, optimum extrusion processing variables (i.e., screw speed, die temperature, and product formulations) are required for maximum retention of nutrients with desired product characteristics. Objective: The objective of this study was to optimize the extrusion operating conditions and soy flour and rice flour blend formulations for developing protein-rich cereal-like extrudates. Methods: Twenty formulations using CCRD with the combinations of soy flour content (43-77%), die temperature (123-157℃), and screw speed (250-350 rpm) were extruded using a single screw extruder. The physical properties (expansion ratio, density, porosity, hardness, crispness, color, and water solubility index) of extrudates were determined. The regression models and 3-dimensional response surface models were developed for each property of extrudates using RSM. Numerical and graphical optimizations were conducted based on the desirability to determine an optimum condition. Results: The regression models were able to predict the physical properties of extrudates with an accuracy of 75-90%, depending on their properties. The 3-dimensional response surface models indicated that the soy flour content, die temperature, and screw speed affected the physical properties of extrudates significantly (p<0.05). Soy flour content (50-55%), die temperature (140-145℃), and screw speed (290-320 rpm) were very effective for the desired quality of soy flour and rice flour blend extrudates. result: The regression models were able to predict the physical properties of extrudates with an accuracy of 75-90 Conclusion: The optimum condition is expected to be very useful for soy protein and rice flour blend extruded cereal-like product development commercially within the experimental range. conclusion: The optimum condition is expected to be very useful for soy protein and rice flour blend extruded cereal-like product development commercially within the experimental range.

Optimization of Coffee-Gelatin Shot Formulations Based on Physicochemical, Rheological and Sensory Properties Using Response Surface Methodology

“…The R 2 value of a model falls between 0 and 1. The R 2 value closer to 1 is considered a better fit of a model [Mitra et al, 2023]. The adequate precisions for all the response variables of coffee-gelatin shots were larger than 4 (Table 3).…”

“…There were 13 experimental points (n=2 k + 2k + m, where n= total experimental points, independent variables, k=2 and replicated central points, m=5). The total 13 experimental points were divided into 4 factorial points, 4 axial points and 5 replicated center points (Mitra et al, 2023) as shown in Table 1.…”

“…The RSM technique was also used optimize the coffee content and alcohol content of coffee-gelatin shots on response variables of coffee-gelatin shots to achieve a desired formulation of coffee gelatin-shots. The analysis of the data was performed assuming a second order polynomial equation 1 (Mitra et al, 2023) for the response variables of overall acceptability, color, texture and L-value and a third polynomial cubic equation 2 (Mitra et al, 2020) for the response variables of flavor, pH and viscosity of coffee-gelatin shots as a function of the coffee content (X1) and alcohol content (X2): X 1 = (C-2)/1, X 2 =(A-10)/5, C = grams of coffee content, A = mL of alcohol content.…”

“…The numerical optimization of the coffee-gelatin shots was conducted using a trial-and-error method based on a desirability function. The optimum region was obtained by a graphical visualization (superimposing of all contour plots together/overlay plots) using the design-expert software (Mitra et al, 2023).…”

“…The effect of the independent variables (coffee content, X1 and alcohol content, X2) on the response variable of sensory properties (overall acceptability, color, flavor and texture), L-value, pH and viscosity of coffee-gelatin shots can be grasped by the 3-demensional response surface models. In this process, a design expert software was used to measure a relationship between independent variables of coffee content and alcohol content and the response variables of sensory properties, L-value, pH and viscosity of the coffee-gelatin shots and visualize this relationship as 3-D graphs created by varying two independent variables (coffee content and alcohol content) for each response (Mitra et al, 2020).The 3-dimensional graph (response surface graphs) elucidated the direction of variation made in response variables (overall acceptability, color, flavor and texture, L-value, pH and viscosity) with the change of coffee content and alcohol content of the coffee-gelatin shots (Mitra et al, 2023). The response surface graphs (effect of coffee content and alcohol content on response variables of coffee-gelatin shots) are shown in Figure 2 Coffee content had a linear effect on the sensory properties (overall acceptability, color, flavor and texture, Figures 2-5) and viscosity (Figure 8) of the coffee-gelatin shots and an inverse linear effect on the L-value and pH (Figures 6-7) of the coffee-gelatin shots.…”