The Taguchi method is a statistical design developed to improve the quality of manufactured products by emphasizing the importance of producing a product as close to the mid-value of the specified limits. In the Taguchi method, factors are arranged in an orthogonal array (OA). Using OAs allows for each combination of levels to appear an equal number of times. The effects of the other factors can therefore be balanced and give a relative value representing the effects of a level compared against the other levels of a given factor. In OA experiments, the number of test runs is minimized while keeping a pair-wise balance, an added benefit as the number of factor/level combinations increases in the study design. The Taguchi method adds a new avenue for experimentation by providing an adaptable, quick and accurate way of determining optimization. This optimization methodology has more recently been expanded to biotechnology, engineering, and chemistry, among other fields. As described previously, a wide range of factors can influence the results of metabolomics studies. Therefore, proper study design and selecting appropriate factors and their corresponding levels are critical for accurate and reproducible work. Despite this, the application of DOE methods to metabolomics-related studies has been limited compared to other scientific fields. To our knowledge, the use of a Taguchi design in untargeted metabolomics has not previously been reported and presents an effective strategy for matrixspecific sample preparation optimization.