Optimization of brown rice protein bars formulated with Arabic and Persian gums: Insights from physicochemical, FTIR, and chemometric analyses
Sedigheh Movaghar, Amir Pourfarzad, Alireza Mehregan Nikoo
Abstract
Protein bars often experience hardening, staleness, and quality decline during storage due to high protein and low moisture content. This study aimed to improve the quality and stability of whey protein-based bars fortified with brown rice flour by incorporating Arabic and Persian gums as natural hydrocolloids. A Face-Centered Central Composite Design was used to examine the effects of gum levels (0–1 %) on physicochemical, microbial, structural, and sensory traits. Gum addition significantly increased moisture and carbohydrates while decreasing protein, fat, ash, energy, pH, peroxide value, and water activity. Texture analysis showed reduced hardness and adhesiveness, with improved cohesiveness and chewiness. Color became lighter and browning index decreased. Microbial counts, molds, and yeasts declined, and sensory scores for texture, flavor, color, and acceptability improved. FTIR analysis indicated changes in protein secondary structures, including reduced α-helix and increased β-sheet and random coil content. Principal Component Analysis (PCA) showed clustering based on formulation, while Partial Least Squares Regression (PLSR) modeled correlations between structure and quality. The optimal formulation (0.81 % Persian gum and 0.97 % Arabic gum) enhanced overall quality. These findings support using natural gums to improve the functional, microbial, and sensory qualities of protein bars and mitigate storage-related quality loss. • A protein bar was formulated using Persian and Arabic gums as functional hydrocolloids. • Physicochemical, microbial, sensory, and structural properties were modeled using RSM. • FTIR analysis revealed significant effects of gums on protein secondary structures. • The optimal formulation (0.81 % Persian and 0.97 % Arabic gum) improved bar quality and stability. • High agreement between predicted and experimental values confirmed model adequacy.