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All Journal Food ScienTech Journal
Babatunde, Adewumi
Federal University of Agriculture, Abeokuta (FUNAAB)
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Immunology & microbiology

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Predictive Modeling of Functional and Physical Properties of Extrusion Cooked Ready-To-Eat Corn Meal Aderele, Adeyemi Adio; Babatunde, Adewumi; Olayanju, Adeniyi; Waheed, Adekojo
Food ScienTech Journal Vol 7, No 1 (2025)
Publisher : University of Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33512/fsj.v7i1.28988

Abstract

Cornmeal is the product of corn-based food processed or pre-cooked through extrusion to produce a ready-to-eat breakfast. The quality of the meal depends on the extrusion cooking factors and process variables. The study focused on the effects of screw speed (SS: 100-120 rpm), barrel temperature (BT: 170-190℃), feed rate (FR: 40-60 rpm), and moisture content (MC: 20-25%) on: water absorption index (WAI), water solubility index (WSI), bulk density (BD) and expansion ratio (ER) to predict functional and physical properties of extruded corn meal. Brabender single-screw laboratory-scale extruder was used to process corn flour into ready-to-eat corn meal. JMP Pro 16 was used for experimental design, which was conducted in triplicate using: Central composite response surface methodology. Design Expert 13 and MATLAB 2020b were used respectively for data analysis and visualization. The main, interaction and quadratic effects of SS, BT, FR, and MC were evaluated on WAI, WSI, BD and ER. The significance level was established at p≤0.05. A second-degree polynomial equation was fitted for each response variable as a function of extrusion cooking process factors. Adequate precision/R-Square values for WAI, WSI, BD, and ER respectively were 25.92/0.97, 11.69/0.99, 10.00/0.94, and 22.51/0.99, which measured each model’s degree of fitness. These values proved that each model have good predictability and was fitted for prediction purposes.
Image Processing and Computer Vision System For Color Evaluation of The Extrusion-Cooked Corn Meal Aderele, Adeyemi Adio; Babatunde, Adewumi; Olayanju, Adeniyi; Waheed, Adekojo
Food ScienTech Journal Vol 8, No 1 (2026): In Press
Publisher : University of Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33512/fsj.v8i1.38384

Abstract

Product sensory specialists find themselves adrift, lacking clear guidance on how to instruct sensory panelists in the art of evaluating product color and conducting color sensory tests designed to mitigate any color biases that might skew perceptions. To address this gap, Image Processing (IP) and Computer Vision Systems (CVS) were proposed to deliver objective assessment of color quality through the versatile RGB color space. In this study, a 3-level, 4-factor central composite response surface methodology (RSM) design was employed to study how various extrusion cooking factors influence RGB color profile of extruded cornmeal. The process examined four key factors with their levels: screw speed (SS: 100-120rpm), barrel temperature (BT: 170-190℃), feed rate (FR: 40-60rpm), and moisture content (MC: 20-25%). For each trial run, extrudate samples were randomly selected and digitally captured using HD camera app of an Android phone, meticulously set to ISO 200, a resolution of 2160×2160, and a zoom of 4.0X for optimal clarity. IP and CVS tools in MATLAB 2023a application software were used for RGB color space data extraction. Regression equations were developed for each response as a function of the process factors. Results showed increasing SS increases R, G, and B intensities. However, increasing BT decreases B and R intensities, whereas decreasing FR did not affect R intensity. Key statistical metrics such as R-squared and Adequate precision, provided insight into the models’ performance: the R, G, and B R-squares/Adequate Precision were (0.82, 0.73, and 0.72) / (5.51, 3.70, and 3.17) and were found satisfactory.
Co-Authors Aderele, Adeyemi Adio Olayanju, Adeniyi Waheed, Adekojo