BackgroundThe detection and quantification of urinary metabolites play an important role in disease diagnosis. In most cases, urinary analyses are done with liquid urine samples, which must be quickly transported to the laboratory to avoid metabolites degradation that is associated with temperature fluctuations. Consequently, dried sampling devices have emerged to minimize analyte degradation. However, most commercial dried sampling devices are expensive, aggregate low volumes, and need better analytical sensitivity. Therefore, a new dry urine sampling device that is inexpensive, suitable for domestic sampling operation, and efficient for quantifying metabolites without requiring high–resolution instruments is proposed in the present study. ResultsThe newly designed dry urine sampling device was produced by 3D printing that efficiently determines 63 urinary organic acids using liquid chromatography coupled with mass spectrometry (LC–MS/MS). The system's efficiency was demonstrated with analytical figures of merit, such as precision, accuracy, and stability of analytes after the sampling and storing of ordinary urine samples. The limits of quantification ranged from 0.01 to 0.42 ng mL–1. Precision and accuracy tests showed relative standard deviations of less than 15 %. The urine stability in the sampling device was high within seven days without any significant degradation of the metabolites. The method was applied to the analysis of 10 human urine samples and compared to a conventional method without the use of the sampling device. The results showed no statistically significant differences, demonstrating the method's efficiency. SignificanceThe proposed 3–D printing device was developed with fast, low–cost manufacturing features and can be manufactured with different volumetric capacities, adaptable to the needs of each user. Furthermore, it is innovative because this is the first sampling device that is effective for the simultaneous storage and preservation of several important urinary metabolites. Thus, it is anticipated that its application would contribute significantly to the identification of metabolic disorders.