Multivariate Data Analysis

Abstract

Data preprocessing is a very important step in multivariate analysis, which can be used separately (before a method is applied), or as a self-adjusting procedure that forms part of the chemometric approach. Ideally, data preprocessing can be used to remove known interference(s) from data to improve selectivity and enhance more important information to improve robustness techniques such as principal components (see Section 6.4.2), which are scale dependent. If one variable has a much higher variance than the others, it is necessary to scale the original variable before calculating principal components. Common preprocessing scaling techniques include mean centering, autoscaling, column standardization, and autoscaled profi les. Other data preprocessing techniques commonly used in chemometrics include minimum/ maximum transformation, variance normalization, and baseline corrections (1st and 2nd derivative, subtraction).