Rapid discrimination of beer based on quantitative aroma determination using colorimetric sensor array

In this study, 6 beers from Tsingtao Brewery were analyzed by using colorimetric GC-MS and sensor array (CSA). First, forty volatile compounds of six beers, including 16 esters, 10 alcohols, 4 acids and 4 aldehydes, were identified by GC-MS. Beers from the same category were grouped using principal component analysis (PCA) score plot and hierarchical clustering analysis (HCA) dendrogram. Discrimination of the beers was subsequently implemented using a 4 x 4 CSA combined with multivariate analysis. A linear discriminant analysis (LDA) model achieved a 100% recognition rates of the 6 beers. In addition, a partial least square (PLS) model could be used to quantitatively determine ethyl octanoate, phenethyl acetate, isoamyl alcohol and octanoic acid, with correlation coefficients over 0.85 for both the calibration curves of the training and prediction sets. Hence, CSA could be used for rapid and non-destructive determination of beer quality.

Automated summary

In this study, 6 beers from Tsingtao Brewery were analyzed using colorimetric GC-MS and sensor array (CSA). The study identified volatile compounds in the beers and discriminated them using multivariate analysis, demonstrating that CSA could be used for rapid and non-destructive determination of beer quality.