A preliminary study on the characterization of taste and odor attributes in commercial lager and ale beers using...

This study aimed to investigate the discrimination of taste attributes and volatile odor patterns among eight commercial canned beers representing lager and ale styles using electronic tongue (E-tongue) and electronic nose (E-nose) systems.

To achieve this, five taste-related E-sensors were evaluated across the eight beer samples, and a total of 34 volatile compounds, comprising 3 acids, 4 aldehydes, 7 alcohols, 9 esters, 4 hydrocarbons, 5 heterocyclics, and 2 ketones, were tentatively identified using an E-nose internal library. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to map the flavor profiles. Furthermore, partial least squares discriminant analysis (PLS-DA) was conducted to identify key flavor drivers, and a debiased sparse partial correlation (DPSC) network analysis was utilized to model the interactions between taste attributes and volatile compounds.

The PCA and HCA of the E-sensing datasets successfully differentiated the beer samples into distinct groups according to their taste attributes and volatile compound profiles. The PLS-DA identified six critical flavor contributors based on a variable importance in projection threshold (VIP-score) of ≥1.0, which included sweetness from the E-tongue alongside five volatile groups-acids, hydrocarbons, alcohols, heterocyclics, and aldehydes. The DPSC network analysis further elucidated these intricate interactions, establishing a stable co-occurrence framework dominated by seven key flavor clusters (ketones, sweetness, alcohols, acids, esters, heterocyclics, and hydrocarbons).

These findings demonstrate that integrated electronic sensing platforms, combined with advanced chemometric network models, provide an effective, objective, and rapid instrumental screening approach for fingerprinting and characterizing flavor patterns in commercial lager and ale beers.