Abstract
•Blue cheeses were discriminated by volatile fingerprints measured one week apart.•Self-organizing maps and entropy-based feature selection predicted ripening times.•A non-linear machine learning approach outperformed a linear chemometric approach.•Methyl ketone production began prior to sporulation of Penicillium roqueforti.•Methyl ketone development may be driven by conidia rather than mycelium.
Blue cheese flavour development derives from complex biochemical reactions that depend on numerous factors including milk source, culture/strain selection, processing, and ripening conditions. Understanding volatile compound development during blue cheese ripening will help reduce production costs and facilitate quality improvements. Volatile compounds contribute to the characteristic flavours of the cheeses but ripening time predictions based on chemical data have proven difficult. The present study employed untargeted fingerprinting combined with linear and non-linear chemometric approaches to identify key volatiles for the modelling of Shenley Station blue cheese ripening times. Self-organizing maps and entropy-based feature selection along with partial least squares regression and variable identification coefficients were used to parse the linear and non-linear development behaviours of volatiles. The blue cheese ripening times were accurately modelled by twenty-three discriminant volatiles. The present study demonstrated that volatile fingerprints can be used to effectively model blue cheese ripening times using a non-linear chemometric approach.