Experimental Analysis of the Egg White Cooking Process through a Trifactorial Design

Abstract

Thermal process optimization in the food industry is essential for reducing variability and ensuring operational standardization. This study, conducted by Industrial Engineering students, aimed to evaluate the influence of three critical variables, egg type, fatty medium, and cooking surface material, on the response time during the egg white frying process. A methodology based on Design of Experiments was employed, utilizing a 2x2x2x2 full factorial model with two replicates, totaling 16 experimental runs performed under strict principles of randomization, replication, and control of external variables. Data were processed using ANOVA with a 95% confidence level, allowing for the determination of the statistical significance of main effects and their interactions. The results demonstrated that the pan type is the only main factor with a significant influence (p < 0.05) on cooking kinetics, which is attributed to differences in thermal diffusivity and inertia of the evaluated materials (Teflon vs. stainless steel). This study allowed the students to integrate advanced statistical tools into a practical scenario, strengthening competencies in systems analysis and process optimization through specialized software (Excel and SPSS v.31). It is concluded that tool control and the strategic selection of input combinations are decisive for optimizing cycle time, providing future engineers with a robust quantitative basis for data-driven decision-making and demonstrating that systematic experimentation is essential for efficiency improvement in both academic and industrial contexts.