The Statistical Analysis About Taste Of Youth In Some Beverage

Norio Tominaga, Keisuke Sugimoto, Koji Takamura, Hiroshi Matsuura
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Abstract

Principal Component Analysis, or PCA, is a methods of reducing the dimensionality of a data set consisting of many variables such that most of the information in the data is preserved. We measured chemically sweetness, sourness, salt and umami contained in several commercially available tomato juices and identified the principal components of a four-dimensional data set consisting of them. In result, we found that the first component of the data set has strongly positive correlation with the total estimation of sensory test by young adults around twenty years of age.
某饮料中年轻人口味的统计分析
主成分分析(Principal Component Analysis,简称PCA)是一种对由许多变量组成的数据集进行降维的方法,从而保留了数据中的大部分信息。我们测量了几种市售番茄汁中含有的化学甜度、酸度、盐和鲜味,并确定了由它们组成的四维数据集的主要成分。结果发现,数据集的第一个分量与20岁左右的年轻人感官测试的总估计值呈强正相关。
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