Explainable AI unlocks temperature-driven oscillatory viscoelastic transitions in sesame protein isolate during integrated heating–cooling cycles
Yazarlar (7)
Mustafa Tahsin Yılmaz Faculty Of Engineering, King Abdulaziz University, Türkiye
Abdullaziz S. Alkabaa Faculty Of Engineering, King Abdulaziz University, Suudi Arabistan
Furkan Türker Sarıcaoğlu Bursa Teknik Üniversitesi, Türkiye
Ahmad H. Mılyanı
Faculty Of Engineering, King Abdulaziz University, Suudi Arabistan
Faculty Of Engineering, King Abdulaziz University, Suudi Arabistan
Prof. Dr. Osman GÜL Kastamonu Üniversitesi, Türkiye
Arş. Gör. Mahmut Ekrem Parlak Bursa Teknik Üniversitesi, Türkiye
Wael S. Hassaneın
Faculty Of Engineering, King Abdulaziz University, Suudi Arabistan
Faculty Of Engineering, King Abdulaziz University, Suudi Arabistan
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Food Hydrocolloids (Q1) | ||
| Dergi ISSN | 0268-005X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 12-2025 |
| Cilt / Sayı / Sayfa | 169 / 1 / – | DOI | 10.1016/j.foodhyd.2025.111656 |
| Makale Linki | https://doi.org/10.1016/j.foodhyd.2025.111656 | ||
| Özet |
| The temperature-dependent viscoelastic behavior of sesame protein isolate (SePI) gels was investigated across integrated heating–cooling cycles (25–95 °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 … |
| Anahtar Kelimeler |
| Explainable artificial intelligence (XAI) | Integrated analysis of heating–cooling cycle | Sesame protein isolate gels | Stacking ensemble modeling | Temperature-dependent viscoelasticity |
Science Direct
Explainable AI unlocks temperature-driven oscillatory viscoelastic transitions in sesame protein isolate during integrated heating–cooling cycles
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 3 |