The second approach involved statistical analysis with MetaboAnalyst , which allows to easily filter for relevant features. To apply this approach, the pilot study samples were divided into three groups: 24-h urine following washout, 24-h urine following the ingestion of a high polyphenol smoothie, and 24-h urine following a day of a high polyphenol diet. Missing values in the data set were imputed with 1/5 of the minimum positive values. Volcano plots were made showing fold change (FC) versus T-test (p) between these groups, with significance if FC > 2 and p < 0.2 (Figure ). This allowed the detection of 446 significant features (Table ). Moreover, as for some features no chromatographic peaks were detected in the washout urine samples, unique features from the other two sample groups were screened for, which yielded 226 features (Table ). It is important to highlight though that from the 672 features, not all of them are expected to be polyphenolic features. These statistical analysis techniques were also applied to the annotated features (Table ), which would improve confidence of Level 3d matched features as significant features should be polyphenol related. For example, the annotated feature apigenin-7-Oglucuronide (Level 2) showed a high fold change of 25 from the washout to the 24-h urine following the consumption of a polyphenol smoothie, and of 44 from the washout to the urine after following a high-polyphenol diet (Table , Figure ). Moreover, the extracted ion chromatogram for this feature is shown in Figure , and the MS 2 spectra with the library match in Figure .