A fast, simple and costless methodology without sample pre‐treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen‐printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non‐linear methods provide better results than linear ones.

中文翻译：

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使用单碳丝网印刷电极的循环伏安法鉴别啤酒

提出了一种无需样品预处理的快速，简单，无成本的方法来区分啤酒。它基于使用商业碳丝网印刷电极（SPCE）的循环伏安法（CV），并包括对使用不同SPCE单位测量的信号的校正。数据被提交给偏最小二乘判别分析（PLS-DA）和支持向量机判别分析（SVM-DA），这允许对啤酒进行合理的分类。同样，来自啤酒的CV数据可用于通过偏最小二乘（PLS）和人工神经网络（ANN）来预测其酒精度。通常，非线性方法比线性方法提供更好的结果。