Extracting relevant and useful information from measurements is an issue of paramount importance and it can be considered as complementary to the process of data acquisition. This is a crucial point especially in the field of chemical measurements, where data sets can consist of hundreds or even thousands of variables so their interpretation can require long time. Chemometrics try to tackle this issue by applying mathematical and statistical tools to data coming from chemical, biological or medical analyses. Among possible methods, Principal Components Analysis (PCA) has found wide application in the I&M field thanks to its ability to identify patterns in acquired measurements and classify data in different groups. Possible applications span from chemicals detection [1] to concentration estimation of compounds in a given system [2]. Actually, many studies demonstrated the possibility to use PCA to process different kinds of data [3], in some cases coupling PCA to other tools such as artificial neural networks to improve the processing performance [4].

中文翻译：

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用于数据解释的化学计量学：主成分分析 (PCA) 在多元光谱测量中的应用


从测量中提取相关且有用的信息是一个至关重要的问题，它可以被视为对数据采集过程的补充。这是一个关键点，尤其是在化学测量领域，因为数据集可能包含数百甚至数千个变量，因此它们的解释可能需要很长时间。化学计量学试图通过将数学和统计工具应用于化学、生物或医学分析的数据来解决这个问题。在可能的方法中，主成分分析 (PCA) 因其能够识别所获取的测量中的模式并对不同组中的数据进行分类的能力而在 I&M 领域得到了广泛的应用。可能的应用范围从化学品检测 [1] 到给定系统中化合物的浓度估计 [2]。实际上，许多研究证明了使用 PCA 处理不同类型数据的可能性 [3]，在某些情况下将 PCA 与人工神经网络等其他工具耦合以提高处理性能 [4]。