Background

The development of statistical software has enabled food scientists to perform a wide variety of mathematical/statistical analyses and solve problems. Therefore, not only sophisticated analytical methods but also the application of multivariate statistical methods have increased considerably. Herein, principal component analysis (PCA) and hierarchical cluster analysis (HCA) are the most widely used tools to explore similarities and hidden patterns among samples where relationship on data and grouping are until unclear. Usually, larger chemical data sets, bioactive compounds and functional properties are the target of these methodologies.

Scope and approach

In this article, we criticize these methods when correlation analysis should be calculated and results analyzed.

Key findings and conclusions

The use of PCA and HCA in food chemistry studies has increased because the results are easy to interpret and discuss. However, their indiscriminate use to assess the association between bioactive compounds and in vitro functional properties is criticized as they provide a qualitative view of the data. When appropriate, one should bear in mind that the correlation between the content of chemical compounds and bioactivity could be duly discussed using correlation coefficients.

中文翻译：

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使用主成分分析（PCA）和层次聚类分析（HCA）进行食品中生物活性化合物与功能特性之间的多元关联：批判性观点

背景

统计软件的开发使食品科学家能够执行各种数学/统计分析并解决问题。因此，不仅复杂的分析方法而且多元统计方法的应用也大大增加。在此，主成分分析（PCA）和层次聚类分析（HCA）是使用最广泛的工具来探索样本之间的相似性和隐藏模式，而这些样本之间的数据和分组关系尚不清楚。通常，较大的化学数据集，生物活性化合物和功能特性是这些方法的目标。

范围和方法

在本文中，当应该计算相关分析并分析结果时，我们会批评这些方法。

主要发现和结论

由于结果易于解释和讨论，因此在食品化学研究中PCA和HCA的使用有所增加。然而，由于它们提供了数据的定性观点，因此人们批评它们不加区别地用于评估生物活性化合物与体外功能特性之间的关联。在适当的时候，应该记住，可以使用相关系数适当地讨论化合物含量和生物活性之间的相关性。