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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Optimization of ingredient compositions and process parameters for the development of hot extruded ready-to-eat (RTE) products using response surface methodology

DOI
https://doi.org/10.14719/pst.9980
Submitted
11 June 2025
Published
08-10-2025

Abstract

Extrusion cooking is a high-temperature, short-time process that combines heat, pressure and shear to gelatinize starch, denature proteins and produce expanded, shelf-stable ready-to-eat (RTE) foods. This study advances beyond prior work by employing a composite blend of sorghum (Sorghum bicolor), pearl millet (Pennisetum glaucum), barnyard millet (Echinochloa esculenta), corn grits (Zea mays) and soy chunks (Glycine max), which has not previously been optimized together using response surface methodology (RSM). Unlike earlier studies restricted to single or binary blends, this work integrates proximate profiling and sensory validation into the optimization framework, thereby addressing a key research gap. While the regression model for moisture content demonstrated strong statistical validity (R² = 0.837), the models for expansion ratio and bulk density showed limitations and were therefore interpreted as indicative trends rather than predictive outcomes. The optimized formulation, comprising 57.5 % millet blend, 45 % corn grits and 25 % soy chunks, achieved desirable physical properties with high expansion ratio (20.12), low bulk density (0.31 g/cm³) and moderate moisture content (11.43 %). Nutritional analysis revealed significantly higher protein (16.2 g/100 g) and dietary fiber (7.1 g/100 g) compared to commercial control products. Sensory evaluation using a 9-point hedonic scale further confirmed superior acceptability in appearance, 
flavor, crispiness and overall quality. Collectively, the findings demonstrate the potential of multi-millet–soy–corn formulations for developing nutritious, consumer-acceptable extruded RTE snacks, while also recognizing statistical limitations and highlighting directions for future refinement.

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