Improving food quality with AI-driven sensomics

Professor Chiara Cordero discusses the latest application of her team’s AI smelling machine as methods are developed to measure quality in hazelnuts, a consumer favourite in sweets and chocolates.

Ensuring food products are of the highest quality is mandatory for international FMCG businesses. In a competitive market, reputations are easy to lose and difficult to regain. As a result, there has been strong demand for developing innovative methods to help establish the quality and authenticity of ingredients. For example, confectionery companies need to be able to certify the attributes of raw and roasted hazelnuts, a major constituent in a wide range of products. Fortunately, the tools to perform this type of analysis continue to be refined and enhanced.

The use of two-dimensional gas chromatography (GC×GC) paired with mass spectrometry (MS) is a well-established method for food analysis but the variety of volatile components in a solid matrix, such as hazelnuts, can create complex challenges. That’s one of the reasons why our team started to develop an artificial intelligence (AI)-enhanced smelling machine with the aim of being able to provide more objective support to the food industry through factory instrumental methodologies as subtle and discerning as the human nose.