Jakubowska Małgorzata, Sordoń Wanda, Ciepiela Filip
Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland.
Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland.
Food Chem. 2016 Jul 15;203:476-482. doi: 10.1016/j.foodchem.2016.02.112. Epub 2016 Feb 16.
This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness.
这项工作提出了一种基于伏安信号、利用专门的数据预处理程序和无监督多变量分析来区分不同品牌苹果酒及其陈酿程度的完整方法。结果表明，在pH值为2的 Britton-Robinson缓冲液中，在玻碳电极上记录的伏安图对每个品牌都是可重复的。通过应用聚类算法和主成分分析，获得了可见的同质簇。先进的信号处理策略，包括自动基线校正、区间缩放和使用专用母小波的连续小波变换，是正确识别目标的关键步骤。结果表明，伏安法结合优化的单变量和多变量数据处理是区分不同品牌苹果酒并评估其新鲜度的充分工具。
