Comparison of Different Data Analysis Tools to Study the Effect of Storage Conditions on Wine Sensory Attributes and Trace Metal Composition
作者： Helene Hopfer , Susan E. Ebeler , Hildegarde Heymann
DOI: 10.1007/978-3-319-10226-9_8
关键词: Mathematics 、 Trace metal 、 Composition (combinatorics) 、 Principal component analysis 、 Partial least squares regression 、 Statistics 、 Wine 、 Sensory system 、 Food science 、 Exploratory data analysis 、 Component analysis
摘要: Storing Cabernet Sauvignon wine at three different temperatures and in four packaging configurations led to significant changes of the elemental sensory profiles. Two exploratory data analysis techniques—principal component (PCA) canonical variate CVA)—were used analyze both sets separately study outcomes. Additionally, partial least squares regression (PLSR) was correlate two each other.
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