Linear Programming Optimization of Nutritional and Technological Properties in Wheat Bread Fortified With Taioba ( Xanthosoma sagittifolium L .) Leaf Flour

Background and Objectives This study applied linear programming to optimize taioba ( Xanthosoma sagittifolium ) leaf flour (TLF) incorporation in wheat bread, balancing nutritional enrichment and technological quality. Findings Breads containing 0%–15% TLF (flour basis) were produced and evaluated for composition, dough expansion, specific volume, and crumb structure by image analysis. Linear regression models describing the effect of TLF level on protein, ash, and key technological responses were embedded into a linear programming framework whose objective function maximized a composite nutritional score based on normalized protein and ash contents, subject to minimum constraints for expansion factor, specific volume, and alveoli number. Protein and ash increased linearly with TLF level, whereas expansion factor, specific volume, and alveoli number decreased due to disruption of the gluten–starch matrix by fibrous, mineral‐rich taioba particles. The optimization identified an optimal TLF level of 11.7%, at which protein and ash increased by about 21% and 220%, respectively, compared with the control bread, while technological parameters remained within acceptance limits. Conclusions The linear programming model identified an optimal TLF level of 11.7%, and experimental validation confirmed the adequacy of this solution in balancing nutritional enrichment and technological quality. Significance and Novelty This work introduces a multi‐criteria optimization framework for products enriched with unconventional leaf flours, supporting rational formulation of nutritionally improved wheat breads.