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Data analysis aims to decipher a useful meaning behind the signals acquired by analysis systems of volatile organic compounds (VOCs). Signal processing determinedly depends on the technique employed to analyze the samples. As presented in previous chapters, a big difference is noted between the chromatograms provided by the data-rich analytical techniques and the multivariate sensor signals and is reflected in how they are analyzed. Machine learning and artificial intelligence are applied for training a classification model and learning a specific VOCs pattern associated with a certain health condition, which is then searched for in any new sample analyzed. Statistical tests are employed to find significant differences between samples and between specific compounds in the samples, which could eventually represent possible disease biomarkers. Statistical methodologies are also applied to assess the performance of the diagnostic tests developed based on VOC samples analysis. All these aspects are presented and discussed in this chapter, in which the most relevant approaches employed in data analysis applied in the field of volatile biomarkers for human health are presented, without pretending, however, to achieve full exhaustiveness.

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