Integrated Multivariate Analysis and Desirability-Based Optimization of Milk–Whey Mixtures: Effects on Physicochemical Properties, Amino Acid Profile, and Nutritional Quality

Foods, 2026

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DOI 10.3390/foods15101759
Data confidence 85 high
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Abstract

The valorization of dairy by-products, particularly whey, represents a key challenge and opportunity in sustainable food systems. This study aimed to evaluate the physicochemical and amino acid composition of milk and whey-derived products and to identify optimal whey-milk mixtures using integrated multivariate and desirability-based approaches. Ten model systems (M1-M10) were prepared with increasing whey content (7.5-75%), and their composition was analyzed using infrared spectroscopy and high-performance liquid chromatography. Multivariate analysis, including PCA and correlation heatmaps, revealed that protein, casein, TS, SNF, and amino acid fractions (ΣEAA and ΣBCAA) were the primary drivers of compositional variability, whereas lactose and acidity-related parameters contributed to secondary differentiation. Desirability function analysis was applied by integrating nutritional quality, functional balance, and sustainability score into a composite index. The results demonstrated that intermediate formulations achieved a more balanced profile compared with extreme compositions. Among all mixtures, the formulation containing 30% whey (M5) showed the highest overall desirability within the evaluated parameters, reflecting a favorable balance between compositional quality and whey utilization. These findings highlight the potential of integrated analytical approaches for the development of nutritionally optimized and resource-efficient dairy systems.