Distinguishing plant and milk proteins and their interactions in hybrid cheese using confocal Raman microscopy with machine learning

The increasing demand for plant-based alternatives to milk protein has led to the development of hybrid processed cheese analogues (HPCAs) combining plant proteins and casein. However, their complex microstructure and molecular interactions remain poorly understood. This study integrated confocal Raman spectroscopy with advanced machine learning for high-resolution spatial mapping and molecular characterization of HPCAs containing mung bean protein isolate (MPI) or hemp protein isolate (HPI) with casein. This integration helped distinguish between protein sources and elucidate structural changes. The addition of casein changed the HPI structure, promoting structural disorder, disulfide bond rearrangement, and a sharp decrease in the tyrosine doublet ratio from 4.5 in HPI100 to 1.2 in HPI50. Conversely, casein interaction with MPI led to microstructural segregation and changes of β-sheet content (from 53 % in MPI100 to 20 % in MPI30). This integrated method represents a powerful tool for analysing protein structure and interactions in complex food systems. • Raman imaging reveals the physical and chemical distribution of cheese components. • Machine Learning allowed protein type recognition in hybrid cheese. • Plant protein secondary structure is altered by cheese-making processing. • Hemp–casein mixtures showed high structural disorder and disulfide rearrangement. • Mung bean–casein mixtures led to pronounced phase separation.