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    Explainable AI unlocks temperature-driven oscillatory viscoelastic transitions in sesame protein isolate during integrated heating-cooling cycles
    (Elsevier Sci Ltd, 2025) Yilmaz, Mustafa Tahsin; Alkabaa, Abdulaziz S.; Saricaoglu, Furkan Turker; Milyani, Ahmad H.; Gul, Osman; Parlak, Mahmut Ekrem; Hassanein, Wael S.
    The temperature-dependent viscoelastic behavior of sesame protein isolate (SePI) gels was investigated across integrated heating-cooling cycles (25-95 degrees C) under oscillatory rheometry (10 % strain, 0.1 Hz). Experiments were performed across a range of treatment conditions, including pressure levels of 0, 50, and 100 MPa and ionic concentrations (IC) of 0-200 mM. Empirical results showed that storage modulus (G ') consistently exceeded loss modulus (G ''), particularly during cooling, indicating elastic-dominant gelation. Application of pressure and ionic concentration (IC) treatments enhanced viscoelastic recovery, yet condition-specific nonlinear trends in G ' and G '' responses-particularly across temperature cycles-and associated hysteresis effects remained difficult to isolate from aggregated empirical trends alone. To address these limitations, stacking ensemble mimicry models were developed and explainable AI (XAI) methods, including SHAP values, partial dependence plots (PDPs), and variance-based sensitivity indices (VBSIs), were employed. The XGBMeta-Stacker and LGBMMeta-Stacker models predicted G ' and G '' with high accuracy, achieving R2 values above 0.94 for both training and testing sets. Despite variability and outliers in the temperature sweep dataset, both ensemble models showed strong predictive alignment with actual values, highlighting the robustness of the stacking strategy in complex rheological modeling. XAI analyses uncovered temperature-driven oscillatory viscoelastic transitions-repeated patterns unlikely to be captured when heating and cooling cycles are examined separately, particularly between 25 and 75 degrees C-highlighting the necessity of integrated cycle analysis to reveal such behavior and enabling quantitative ranking of temperature, pressure, and IC influences across the domain. Temperature emerged as the dominant driver of G ' and G '' transitions, while pressure exerted stronger effects on viscous behavior under high-intensity conditions. Integrated interpretation of SHAP, PDP, and VBSI analyses revealed condition-dependent feature dynamics and interaction effects, offering mechanistic insights inaccessible through traditional methods alone.
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    Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models
    (Elsevier, 2025) Yilmaz, Mustafa Tahsin; Badurayq, Salman; Polat, Kemal; Milyani, Ahmad H.; Alkabaa, Abdulaziz S.; Gul, Osman; Saricaoglu, Furkan Turker
    In this study, we conducted a comparative analysis of the explainability of Decision Tree Regressor (DTR) and Gaussian Process Regressor (GPR) models in predicting the shear stress and viscosity of sesame protein isolate (SPI) systems, employing explainable machine learning (EML) techniques to elucidate complex, nonlinear relationships among processing parameters. SPI samples were processed across pressure levels ranging from 0 to 100 MPa and ion concentration (IC) values from 0 to 200 mM. DTR model accurately predicted shear stress (R2 = 0.999), while a GPR model achieved high performance for viscosity prediction (R2 = 0.9925). Formally, the modeling task is framed as learning a predicting mapping function f : Rp -> R, where x is an element of Rp denotes the vector of predictors (pressure, IC, shear rate) and y is an element of R is the target variable (shear stress or viscosity), by minimizing a loss function such as mean squared error. Interpretation of model predictions using SHapley Additive exPlanations (SHAP), permutation importance, and partial dependence analysis revealed that pressure and IC are the most influential factors affecting shear stress and viscosity, with pressure inducing protein conformational changes that impact rheological properties. The shear rate exhibited a lesser direct impact within the systems examined. Partial Dependence Plots (PDPs) from the DTR model revealed strong, nearly linear positive relationships between pressure and shear stress, while the GPR model depicted more nuanced responses, highlighting the models' differing sensitivities. Variance-Based Sensitivity Indices (VBSIs) further quantified these influences, with pressure and IC showing higher sensitivity scores in the DTR model compared to the GPR model. Permutation importance and SHAP interaction analyses corroborated these results, emphasizing the dominant role of pressure and IC, both independently and interactively, in determining shear stress. In contrast, viscosity predictions were influenced by more distributed and subtle interactions among all features. Employing explainable machine learning techniques enables a comprehensive understanding of feature relevance in complex, nonlinear rheological systems, facilitating the elucidation of viscosity development in sesame protein systems through rheological indices. This approach ensures no bias toward formulation composition and applied pressure, offering valuable insights for optimizing formulation and processing conditions in food applications to enhance the functional properties of SPI-based products.