From Leaf to Blend: CNN-Enhanced Multi-source Feature Fusion Enables Threshold-Driven Style Control in Digital Tobacco Formulation

Background: This study establishes a computational framework for predictive style modeling in tobacco formulation design, resolving the critical disconnect between empirical approaches and blended system complexity. A convolutional neural network (CNN) framework was developed to integrate conventional chemical indicators with thermogravimetric analysis derived features from 434 geographically authenticated tobacco leaf samples. By implementing regionally constrained Monte Carlo sampling of composition ratios, 304,800 formulation datasets simulating real-world blending constraints were generated to enable robust model training. Results: The leaf-centric CNN demonstrated remarkable region-style classification accuracy (99.54% via five-fold cross-validation), outperforming conventional machine learning models and revealing thermal-chemical complementarity in regional style characterization. However, direct application to blended formulations revealed a critical limitation: only 50.91% of blended formulations maintained stylistic consistency with their primary source leaves, underscoring the inadequacy of single-material models for blended systems. To overcome this, a hybrid learning model through multi-source data fusion of leaf and formulation representations waas engineered, achieving dual breakthroughs: 90.09% regional style identification accuracy and 87.90% leaf-to-blend style consistency. Mechanistic analysis identified a nonlinear threshold effect, showing that primary source leaves maintained 99.91% stylistic dominance when their composition exceeded 90%, decreasing to 67.90% at 30% composition. Significant formulation style deviation risks emerged when contribution gaps between principal and secondary source leaves composition narrowed below 10%. Conclusions: Building on these insights, a probabilistic style modulation strategy was proposed and validated through case applications. This data-driven framework advances tobacco engineering from empirical practices to predictive digital transformation, providing a template for agricultural product manufacturing systems with similar formulation challenges.