In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.

中文翻译：

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饮料中化合物的分析 - 建立成分分析指南。


近年来，人们对非酒精饮料和酒精饮料的化学成分进行了大量分析。然而，没有任何研究考虑过此类研究中固有的成分缺陷，这可能会导致任意结果。本文比较了两种方法：成分数据分析 (CoDa) 和经典统计分析，以证明 (1) 陈酿如何影响啤酒；(2) 结果如何根据所应用的方法而变化。例如，对 43 种啤酒样品的化学成分进行了分析。我们的工作使我们在对未修改的、对数变换的或封闭的数据进行相关分析和主成分分析时获得了与对数比坐标分析相反的不同结果。后者的结果可以更好地分离陈酿啤酒样品和新鲜啤酒样品，并且更容易解释。这些结果使我们建议使用 CoDa 方法来分析饮料的化学成分。