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A hybrid sensing system, which combines simultaneous cyclic voltammetric (CV) and UV–vis absorbance measurements using a commercial carbon screen-printed electrode and a set of optical fibres in disposable cuvettes, is proposed. The hybrid system approach was applied to 27 samples of recognized beer brands, improving the classification power as compared to only voltammetric or only spectrophotometric measurements. The developed partial least squares discriminant analysis (PLS-DA) model was able to discriminate between five types of beer (lager, marzen, black/stout, alcohol-free and white/ale). The model was also successfully applied to 28 beer samples of white-label brands sold in local supermarkets, demonstrating their similarity to recognized brand beers. [Display omitted] • Hybrid sensing system combines simultaneous cyclic voltammetry and UV–vis absorbance. • A carbon screen-printed electrode and a set of optical fibres were used. • The hybrid sensing system was successfully applied for beer discrimination by PLS-DA. • Combination of complimentary techniques provides better results than one technique. [ABSTRACT FROM AUTHOR]

Pasta is a key element of the Mediterranean Diet and it has been declared by Unesco intangible cultural heritage of humanity. Despite seems a simple food, only made of semolina and water, pasta is produced following a multi-step process that strongly affect the final product. Drying stage is the one that has the greater influence on its organoleptic/nutritional characteristics. This study aimed to analyse the flavour of pasta to test whether the different drying treatments (High Temperature-Short time or Low Temperature-Long time) have a direct impact on its composition and consequently whether they could influence the end-product quality. The headspace solid-phase microextraction was optimized using an experimental design and 52 samples were analysed by HS-SPME/GC-MS and classified by PLS-DA. The resulting classification model (validated by repeated double cross-validation and permutation tests) allowed correctly predicting more than 80% of samples, confirming that drying may have a significant impact on pasta flavour. Image 1 • HS-SPME/GC-MS method to determine pasta flavour was developed. • 52 samples produced under HT/VHT-St and LT-Lt drying process were analysed. • Double-cross-validated PLS-DA model correctly classified 80% of samples. • VIP indices were used to identify relevant volatiles in pasta discrimination. • Flavour fingerprint is a tool to valorise pasta produced at low temperature. [ABSTRACT FROM AUTHOR]