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Chemometrics is a scientific tool that uses data dimensionality reduction to extract maximum information from complex data sets containing a large number of samples and variables. This tool is of particular importance considering that technological advancements in analytical chemistry have enabled the generation of larger, more complex data sets. In order to be examined, these data sets often require advanced mathematical and statistical approaches, such as chemometrics. The objectives of this chapter are to discuss important parameters to consider in study design, chemical analysis, and data pretreatment prior to chemometric analysis in foodomics, and to examine the main statistical tools used in chemometrics including unsupervised and supervised analysis methods. This chapter provides detailed information on the benefits and challenges of each of these methods, their principles, and best practices for utilizing them in food analysis. Key food-related applications of chemometrics are also presented.

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