Near-Infrared (NIR) sensors are routinely used for in-process monitoring in the cheese industry, from raw milk analysis to final product grading. For example, in curd processing, real-time NIR monitoring of moisture and fat content enables dynamic adjustments to cutting and cooking parameters, reducing batch inconsistencies. During ripening, hyperspectral NIR imaging tracks proteolysis and lipid oxidation, providing insights into flavour development and shelf-life prediction.
There have been many proof-of-concept studies to extend the technique from quality monitoring and in-process adjustments to real-time checks for authenticity or chemical contaminants. None have yet made it into routine use. This review (open access) discusses the current gaps, the latest developments, and argues that – with the pace of AI development – these gaps could soon be closed, particularly in the PDI/PGO cheese supply chains. Success would require coordinated efforts among research laboratories, regulatory authorities and producers to establish harmonised protocols, shared spectral repositories and validation frameworks.
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