Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates. 1. Introduction Sugar cane spirit, or cacha？a, is a typical Brazilian distilled beverage [1, 2]. This spirit is the second most consumed alcoholic beverage in the country and the third most consumed in the world. There are almost 30,000 cacha？a producers in Brazil and over 5,000 cacha？a brands available on the market [3]. In Cape Verde, the sugar cane spirit, grogue, is a drink produced mainly by archaic practices, without supervision and without efficient control of the production process. To ensure the quality of grogue, it is necessary to correct deviations over the course of the whole production process. For Cape Verde, because there is still no local legislation, the results are evaluated according to Brazilian legislation. Mathematical and statistical methods of analysis can be used for diverse scientific purposes, such as selecting the measurements and procedures best suited to chemical experiments or obtaining a more accurate analysis of the resulting information. According to the needs of any particular study, chemometrics can be used for analytical signal processing, experimental planning and optimization, pattern recognition, data classification, multivariate calibration, and/or monitoring and modeling of various processes, among other applications [4–7]. One of the first steps of chemometric analysis is to plot data in a multidimensional space, grouping the data with similar characteristics to demonstrate that there is some natural relationships between these data points. Thus, groups with distinct characteristics will be differentiated. Exploratory multivariate analysis is performed in a matrix, and the data are organized in a spreadsheet where “ ” samples with “ ” variables results in a
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