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Abstract：
When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account.
摘要：
在分析实验化学数据时,通常有必要将研究设计的结构纳入化学计量/统计模型，以有效解决感兴趣的研究问题。方差分析-同时成分分析（ASCA）是在多变量数据的定量分析中包含此类信息的最突出的方法之一，特别是当变量数量很大时。本教程综述旨在以一种简单的方式解释ASCA是如何工作的，如何操作以及如何正确解释ASCA结果，平易近人的数学和视觉描述。给出了两个例子:第一个,模拟化学反应,用于说明ASCA步骤和第二个步骤，来自真实的化学生态学数据集，结果的解释。还提供了与ASCA密切相关的方法的概述，指出它们的差异和范围，以提供广泛的图片，以构建考虑实验设计的多变量模型。
