Model-enabled food design: The effect of protein addition on the physicochemical properties of protein-fortified soups using mixtures of milk and pea protein

The demand for protein-fortified products has increased due to several factors, with a major one being the growing population of older adults worldwide. The aim of this study was to optimize the formulation of protein-fortified tomato soup by adding milk protein concentrate (MPC) and pea protein isolate (PPI) individually or in combination. The combination was used to balance the nutritional limitations of plant proteins (such as the low levels of leucine) while enhancing the environmental sustainability of milk proteins. The key objective was to develop a tomato soup with at least 20% of the caloric content coming from protein, with similar physicochemical properties to a control soup, reduced CO₂ emissions, and high leucine content to meet the nutritional needs of older adults. The effects of various protein combinations on the physicochemical properties of the tomato soups were examined using a machine learning approach. The findings revealed that while it is challenging to simultaneously maximize leucine content and minimize carbon emissions, a formulation with double leucine content and a relatively low CO₂ footprint was achievable. The models demonstrated high accuracy for most physicochemical properties but encountered greater complexity for certain attributes, emphasizing the importance of considering both individual and combined protein effects. In conclusion, the study highlights the potential to use machine learning to blend proteins in a strategic manner with the aim to develop nutritious and sustainable food products, addressing the nutritional needs of older adults, while providing more environmentally friendly options in protein fortification.