Department of Biochemistry, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic.
CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic.
Food Chem. 2019 Jun 30;284:296-302. doi: 10.1016/j.foodchem.2019.01.113. Epub 2019 Jan 26.
Bio-electronic tongue was linked to artificial intelligence processing unit and used for classification of wines based on carboxylic acids levels, which were indirectly related to malolactic fermentation. The system employed amperometric biosensors with lactate oxidase, sarcosine oxidase, and fumarase/sarcosine oxidase in the three sensing channels. The results were processed using two statistical methods - principal component analysis (PCA) and self-organized maps (SOM) in order to classify 31 wine samples from the South Moravia region in the Czech Republic. Reference assays were carried out using the capillary electrophoresis (CE). The PCA patterns for both CE and biosensor data provided good correspondence in the clusters of samples. The SOM treatment provided a better resolution of the generated patterns of samples compared to PCA, the SOM derived clusters corresponded with the PCA classification only partially. The biosensor/SOM combination offers a novel procedure of wine classification.
生物电子舌与人工智能处理单元相连,用于根据与苹果酸-乳酸发酵间接相关的羧酸水平对葡萄酒进行分类。该系统在三个传感通道中使用了带有乳酸氧化酶、肌氨酸氧化酶和延胡索酸酶/肌氨酸氧化酶的电流生物传感器。使用两种统计方法——主成分分析(PCA)和自组织映射(SOM)对结果进行处理,以对来自捷克南摩拉维亚地区的 31 个葡萄酒样本进行分类。参考分析使用毛细管电泳(CE)进行。CE 和生物传感器数据的 PCA 模式在样品簇中提供了很好的对应关系。与 PCA 相比,SOM 处理提供了更好的样本生成模式分辨率,SOM 衍生的聚类仅部分对应于 PCA 分类。生物传感器/SOM 组合提供了一种新的葡萄酒分类程序。