Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Barcelona, Spain.
Food Chem. 2013 Dec 1;141(3):2533-40. doi: 10.1016/j.foodchem.2013.05.091. Epub 2013 May 25.
In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).
本工作提出了一种基于离子选择性电极（ISE）阵列的电子舌（ET）系统，用于区分不同商业啤酒类型。该阵列由 21 个 ISE 组成，结合了阳离子和阴离子传感器以及具有通用响应的其他传感器。为此，使用 ET 对啤酒样品进行分析，无需任何预处理，而只需用磁力搅拌器轻轻搅拌样品，以减少样品泡沫的产生，因为泡沫可能会干扰测量。然后，使用两种不同的模式识别方法（主成分分析（PCA）和线性判别分析（LDA））对获得的响应进行评估，PCA 允许识别一些初始模式，而 LDA 则用于实现样品品种的正确识别（准确率为 81.9%）。在 LDA 的情况下，使用基于马氏距离准则的逐步纳入方法选择最具判别力的变量。在这方面，结果表明，使用监督模式识别方法（如 LDA）是解决复杂识别情况的一种很好的选择。此外，为了展示 ET 的定量应用，使用人工神经网络模型从阵列数据预测啤酒的酒精含量（测试子集的均方根误差为 0.131 abv）。
