سامانه همایش های موسسه پژوهشی علوم و صنایع غذایی

صفحه اصلی / سومین همایش ملی پژوهش های کاربردی امنیت غذایی: فرآوری، ایمنی و سلامت

Identification and Ranking of Key Formulation Factors Affecting Sensory Texture in Bakery Products Using Machine Learning-Based Sensitivity Analysis

نویسندگان :

ALI ZAMANPOUR BOROJUNY¹ Zeynab Raftani Amiri² Reza Esmaeilzadeh Kenari³ Jaafar Mohammadzadeh Milani⁴ Mohsen Zandi⁵

1- دانشگاه علوم کشاورزی و منابع طبیعی ساری 2- دانشگاه علوم کشاورزی و منابع طبیعی ساری 3- دانشگاه علوم کشاورزی و منابع طبیعی ساری 4- دانشگاه علوم کشاورزی و منابع طبیعی ساری 5- دانشگاه زنجان

کلمات کلیدی :

Hydration Optimization،Gluten Network،SHAP،XGBoost،Food Science،بهینه‌سازی هیدراتاسیون،شبکه گلوتن،علوم غذایی

چکیده :

This study aimed to identify and rank key formulation factors influencing sensory texture in bakery products using interpretable machine learning. A dataset of 287 formulations (bread, cake, biscuit) from peer-reviewed literature (2018–2024) was analyzed, with sensory scores (1–5) as the target variable and ingredient percentages as inputs. An XGBoost model coupled with SHAP analysis revealed that flour-to-water ratio was the most influential factor (mean |SHAP| = 0.41), followed by fat (0.33) and yeast (0.28), while sugar and salt had negligible effects. High yeast (>2.5%) with low water (<45%) produced dense, sour textures due to gluten disruption and over-acidification, whereas optimal hydration (flour-to-water ratio 1.4–1.6) yielded springy, cohesive crumb. This work provides the first SHAP-based, data-driven framework for evidence-based formulation in bakery science, enabling targeted optimization to enhance quality, reduce waste, and replace trial-and-error practices in the food industry.