Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models

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dc.authorid0000-0003-1620-4246
dc.contributor.authorYilmaz, Mustafa Tahsin
dc.contributor.authorBadurayq, Salman
dc.contributor.authorPolat, Kemal
dc.contributor.authorMilyani, Ahmad H.
dc.contributor.authorAlkabaa, Abdulaziz S.
dc.contributor.authorGul, Osman
dc.contributor.authorSaricaoglu, Furkan Turker
dc.date.accessioned2026-02-08T15:15:08Z
dc.date.available2026-02-08T15:15:08Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn 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.
dc.description.sponsorshipKAU Endowment (WAQF) at king Abdulaziz University, Jeddah, Saudi Arabia; Deanship of Scientific Research (DSR)
dc.description.sponsorshipThe project was funded by KAU Endowment (WAQF) at king Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for technical and financial support.
dc.identifier.doi10.1016/j.asej.2025.103565
dc.identifier.issn2090-4479
dc.identifier.issn2090-4495
dc.identifier.issue9
dc.identifier.scopus2-s2.0-105008219225
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asej.2025.103565
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5624
dc.identifier.volume16
dc.identifier.wosWOS:001516908500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAin Shams Engineering Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectExplainable artificial intelligence
dc.subjectSesame protein isolates
dc.subjectSteady shear rheology
dc.subjectTree-based machine learning models
dc.subjectGaussian Process regressor
dc.titleExplainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models
dc.typeArticle

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