Whey is a byproduct of dairy production that possesses high nutritional value and versatile applications across various industries. In the framework of circular economy, the reutilization of whey is essential to transform this high-volume byproduct into a valuable resource. In this narrative review, the authors focus on trends in the utilization of whey for manufacturing functional and nutritional beverages. This publication summarizes the current information on whey composition based on various influencing factors and explores the technological processes currently used in whey-based beverage production and analysis, as well as nutritional profiles and properties of whey-based drinks with functional modifiers. Information about whey processing methods, compositional profiles, and whey-based beverages’ health benefits along with the author-proposed typology of functional drinks are summarized in tables throughout the paper.

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R²CV and R²pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R²CV and R²pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.