Authors : Sowmya B. L.

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/37det45w

Scribd : https://tinyurl.com/499as65b

DOI : https://doi.org/10.38124/ijisrt/25nov1044

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Abstract : Flour rheological properties such as water absorption, dough development time, stability, and extensibility are fundamental to assessing dough behavior and bread-making quality. Conventional rheological instruments like the farinograph and extensograph are accurate but costly and time-consuming. Artificial intelligence (AI) and machine learning (ML) provide modern, data-driven solutions for rapid and accurate prediction of rheological properties. This paper presents an AI-based framework integrating artificial neural networks (ANN), convolutional neural networks (CNN), and explainable AI (XAI) methods to model rheological attributes using physicochemical, near-infrared (NIR), and imaging data. The ANN achieved R^2 values exceeding 0.78 for farinograph parameters, while CNN models processing 3D imaging data achieved R^2 near 0.90 for extensibility and toughness. SHAP analysis identified protein content and specific NIR wavelengths as dominant features. The study demonstrates that advanced AI algorithms can replace traditional rheological testing with reliable, cost-effective, and explainable predictive systems.

Keywords : Machine Learning, Neural Networks, Flour Rheology, Explainable AI, NIR Spectroscopy, Deep Learning, Predictive Modeling, Agricultural AI.

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