The JAB Code, a 2D barcode standardized in ISO/IEC 23634:2022, offers improved reliability and data capacity over traditional barcodes, but its color recognition poses challenges. These issues stem from the suboptimal utilization of the RGB color space in printing and the non-bijective RGB-to-CMYK conversion, prompting the need to select colors that ensure distinct segregation in the transformed color space for enhanced detection robustness. We propose an approach for calibrating the colors of the JAB Code, involving the creation of a test pattern, quantization of the color space, and the calibration of colors using a calibration target. This method aims to ensure optimal color representation within the barcode and can be integrated into JAB Code generation tools or web apps, simplifying the process for users and ultimately improving color accuracy and fidelity within the barcode. We conduct an experiment with different printers, utilizing a smartphone for image capture. The evaluation includes printing JAB Code test patterns, creating and calibrating standard and calibrated JAB Codes, and capturing images under various lighting conditions. We use the JAB Code detection algorithm to analyze color distances in the RGB space, revealing improvements in color distribution and lower error rates with printer color calibration, which can lead to faster reading processes and smaller JAB Code sizes with reduced area requirements. This work offers important insights that should be considered during the next revision of the ISO standard.