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The administration of 7\u2010keto\u2010DHEA was studied as a proof\u2010of\u2010concept of the effectiveness of the combination of knowledge\u2010based and machine\u2010learning approaches to differentiate the changes due to the athletic activities from those due to the recourse to doping substances and methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Urine samples were collected from five healthy volunteers before and after an oral administration by identifying three time intervals. Raw data were acquired by injecting less than 1\u2009\u03bcL of derivatized samples into a model 8890 gas chromatograph coupled to a model 7250 accurate\u2010mass quadrupole time\u2010of\u2010flight analyzer (both from Agilent Technologies), by using a low\u2010energy electron ionization source; the samples were then preprocessed to align peak retention times with the same accurate mass. The resulting data table was subjected to multivariate analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariate analysis showed a high similarity between the samples belonging to the same collection interval and a clear separation between the different excretion intervals. The discrimination between blank and long excretion groups may suggest the presence of long excretion markers, which are particularly significant in anti\u2010doping analysis. Furthermore, matching the most significant features with some of the metabolites reported in the literature data demonstrated the rationality of the proposed metabolomics\u2010based approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The application of metabolomics tools as an investigation strategy could reduce the time and resources required to identify and characterize intake markers maximizing the information that can be extracted from the data and extending the research field by avoiding a priori bias. Therefore, metabolic fingerprinting of prohibited substance intakes could be an appropriate analytical approach to reduce the risk of false\u2010positive\/negative results, aiding in the interpretation of \u201cabnormal\u201d profiles and discrimination of pseudo\u2010endogenous steroid intake in the anti\u2010doping field.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/rcm.9217","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T01:09:15Z","timestamp":1636074555000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Metabolomics workflow as a driven tool for rapid detection of metabolites in doping analysis. 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