Discriminating craft and industrial beers by untargeted GC-MS metabolomics combined with multivariate statistical analysis

Abstract

Developing robust analytical methodologies for the discrimination of craft and industrial beers presents a significant yet formidable challenge. The identification and differentiation of products necessitate the utilization of reliable analytical methods, which are indispensable for ensuring market stability and safeguarding consumer interests. In this study, representative beer samples with a significant market share were selected for headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis to compare the volatile substance characteristics of craft and industrial beers. The findings suggested that alcohols and esters constituted the primary volatile compounds. Craft beer exhibited a higher abundance of both types and concentrations of volatile compounds compared to industrial beer. Subsequently, unsupervised and supervised chemometric techniques were employed for further analysis of the data matrix. The orthogonal partial least squares discriminant analysis (OPLS-DA) method was established to effectively discriminate between industrial and craft beers, enabling the identification of characteristic volatile markers. The untargeted GC-MS metabolomics coupled with multivariate statistical analysis holds promise for the authentication of craft and industrial beers.