AI-assisted screening of soybean varieties: Linking protein composition to tofu quality.

Precise control of soy-protein gelation is essential for consistent tofu manufacture, yet current cultivar-selection methods remain slow, empirical, and prone to batch-to-batch variability. This study introduces the first AI-assisted platform specifically designed to translate SDS-PAGE protein fingerprints from a diverse panel of 170 soybean lines into rapid, objective tofu quality predictions. Leveraging computer vision for the digitization of protein subunits, coupled with an optimized support vector machine (SVM) classifier, our workflow bridges a critical gap between biochemical analysis and functional food quality. The model classified seeds into three tofu-quality classes-firm, high-yield, and high-moisture-through hierarchical clustering of multiple quality metrics (firmness, yield, moisture content, protein content, and textural attributes). These labels reflected the predominant characteristics of each class, and the approach achieved 82 % accuracy, outperforming k-nearest neighbor, decision-tree, random-forest, and gradient-boosting algorithms. A feature-weight analysis revealed that 11S A