Authentication of craft and industrial beers by excitation-emission matrix fluorescence spectroscopy and chemometrics

Abstract

In this article, some ingenious strategies based on chemometrics were proposed to achieve the authentication of industry and craft beers in the Chinese market. Excitation-emission matrix fluorescence spectroscopy technique was used to obtain high-dimensional data of beer samples with 3 dilution levels (the content of beer in water = 3%, 50% and 100%). Alternating trilinear decomposition (ATLD) and alternating quadrilinear decomposition (AQLD) algorithms were used to characterize beer samples. Three chemical pattern recognition methods including partial least squares-discrimination analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA) and random forest (RF) were used to handle two classification tasks with different aims, including the classification of industry beer and craft beer (Case 1) and the classification of industry beer and craft beers of different brands (Case 2). For case 1, the correct classification rates of the cross-validation, training set, test set and prediction set are all 100% obtained by the classification methods based on four-way data arrays. For case 2, the results obtained by PLS-DA based on four-way data are excellent, the correct classification rates of cross-validation, training set, test set and prediction set are 90.3%, 100.0%, 100% and 88.9%, respectively. This study showed that four-way data coupled with PLS-DA could be a good choice for all beer classification tasks. The proposed method is expected to safeguard the interests of consumers and manufacturers, and it can be extended to the authentication of other beverages.