Metabolomics generates complex data that need dedicated workflows to extract the meaningful information. For biological interpretation, experts are mainly focusing on metabolites rather than on the redundant different analytical species. Moreover, the high degree of correlation in datasets is a constraint for the use of statistical methods. In this context, we developed a new tool to detect analytical correlation into datasets. The algorithm principle is to group features from the same analyte and to propose one single representative per group. The user can define grouping criteria with various options including correlation coefficient, retention time, mass defect information. The representative feature can be determined following four methods according to the analytical technology. The present tool was compared to one of the most commonly used free package proposing a grouping method: ‘CAMERA’, using its Galaxy version ‘CAMERA.annotate’ available in workflow4Metabolomics (W4M; http://workflow4metabolomics.org). To illustrate its functionalities, a published dataset available on W4M (Thevenot et al., 2015) was used as an example. Within the 3,120 ions of the dataset, the tool allowed creating 2,651 groups, meaning that 15% of ions are proposed to be filtered because of analytical redundancies. The proposed tool subdivided more than 20 groups of more than 10 ions into smaller ones corresponding to individual annotated metabolites, thus demonstrating the efficiency and relevance of the present approach. As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows.