Advancements in food authentication using soft independent modelling of class analogy (SIMCA): a review

Food authentication verifies the match between product characteristics and claims and it is crucial in a globalized and complex food sector. Currently, class-modelling approaches, such as soft independent modelling of class analogy (SIMCA), are powerful tools for assessing food authenticity. The aim of this review is to discuss the application of SIMCA for food authentication and to describe the conceptual differences between discriminant and class-modelling approaches. The discussion of research articles is organized around three elements: (i) the research objectives, (ii) the analytical methodologies, and (iii) the food products investigated. Moreover, the challenges and future perspectives considering the development of innovative food products are discussed. Adulteration is the most investigated food authentication issue, followed by verification of geographical origin. Food authenticity appeared to be predominantly evaluated using non-destructive spectroscopy. Overall, the articles collectively cover a broad spectrum of food categories, representing those most prone to adulteration. However, there is a notable lack of food authentication studies on innovative food products, underscoring the urgency for further research in this field. Graphical Abstract