An Integrated Statistical and Machine Learning Framework for Thermo-Mechanical Optimization of Amaranth–Lupin Composite Doughs
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The optimization of composite flour formulations requires analytical approaches capable of integrating multiple interdependent rheological attributes. This study aimed to evaluate the thermo-mechanical behavior of doughs formulated through the progressive substitution of amaranth flour with lupin flour (0–40%). Rheological parameters were determined using Mixolab, considering consistency, protein weakening, gelatinization, thermal stability, and retrogradation. The analysis was conducted using an integrated approach combining univariate statistics, multivariate analysis, and machine learning. The results revealed significant differences among treatments, with large effect sizes in initial consistency and retrogradation, indicating a strong influence of formulation on the structural behavior of the dough. Multivariate analysis identified distinct patterns and explained a high proportion of the total system variability, while machine learning confirmed discrimination between formulations and the stability of the performance ranking. Overall, the integrated approach enhances decision-making reliability and provides a robust strategy for optimizing composite doughs in complex food systems.
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