Consumer-based optimization of a third-generation product made from peanut and rice flour.

Indirectly puffed snacks were produced by an extrusion process with partially defatted (12% fat) peanut flour (30%, 40%, 50%) at different levels of screw speed (200, 300, 400 rpm) and feed rate (4, 5, 6 kg/h). Extrudates were dried to obtain half-products (11% to 12% MC) followed by puffing with deep-fat frying. The puffed snack prototypes were subjected to consumer acceptance test. Consumers rated higher than 6.0 (= like slightly) for all products produced within the experimental factor ranges on the attributes of crispness and texture, whereas consumer scores for appearance, color, flavor, and overall liking were lower than 6.0 for the product containing 50% peanut flour regardless of screw speed and feed rate. The product extruded with 50% peanut flour at screw speed of 400 rpm and feed rate of 6 kg/h received the lowest score of 5.5 on overall liking in a 9-point hedonic score. Predicted regression models indicated that feed rate had the largest effect on consumer attributes followed by peanut flour and screw speed. From the superimposed contour plot of individual contour plot of consumer attributes, the optimum region was identified as the area beginning at the 42.0% to 43.0% peanut flour and 4.0 kg/h feed rates, rising to a maximum at 45% peanut flour and 4.6 kg/h feed rates and decreasing to the 33.0% to 34.0% peanut flour and 6.0 kg/h feed rates. Verification confirmed the ability of predictive regression models to identify peanut-based snacks, which would be scored higher than 6.0 by consumer evaluation.