Thermal measurement techniques, such as Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA), measure aspects of a material such as the breadth and absolute value of its melting point. They are relatively simple, fast and cheap methods that have a long history of use for specification testing of pharmaceutical active ingredients and other pure chemicals.
Advances in machine learning and classification algorithms now enable thermal measurement techniques to be applied to food authenticity testing. This review (open access) investigates recent advances in thermal analysis for food authentication, focusing on its principles and applications across food matrices, including edible oils, dairy products, honey, and meat. Thermal fingerprints generated by DSC and TGA provide indicators of compositional and structural changes, enabling evaluation of authenticity, thermal stability, oxidation, crystallization behaviour, and shelf life. Integration with chemometric and machine learning approaches enhances adulteration detection, even at low levels. The authors conclude that standardized protocols, thermal databases, and regulatory acceptance remain essential for widespread adoption.
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