The increasing legalization and availability of cannabis in the United States has increased the need for rapid, reliable, and low-cost roadside tools to assist in identifying potentially impaired drivers. This project evaluated a novel approach for on-site cannabinoid detection by integrating a 3D-printed test cartridge with Fast Blue– based colorimetric chemistry. The study assessed the performance of Fast Blue B (FB-B) and Fast Blue BB (FB-BB) dyes for detecting Δ9-tetrahydrocannabinol (Δ9-THC), cannabinol (CBN), and cannabidiol (CBD) across multiple matrix systems. Fast Blue dyes were incorporated into three carrier materials as dry-down films, agar, and synthetic gelatin. All carrier materials were tested using liquid cannabinoid standards spanning a range of concentrations.
Experimental work included optimization of dye concentration, assessment of drop-size variability, construction of calibration curves, Lab* colorimetric analysis, and fluorescence evaluation. Images were obtained using both a flatbed scanner and a controlled-lighting 3D-printed photo box; analytical signal intensities were quantified via ImageJ to evaluate system performance. Across carrier materials, the synthetic gelatin platform produced the lowest variability among replicate cartridges for both FB-B and FB-BB.
The FB-B/gelatin system demonstrated preliminary linearity (R² = 0.55, r = 0.74) with consistent intensity responses between 10–100 ng, indicating a low detection limit despite a narrow signal range. The FB-BB/gelatin system showed stronger linearity (R² = 0.94, r = 0.97) and a wider working intensity range, also supporting detection from 10–100 ng. Exploratory differentiation of cannabinoids using three-dimensional Lab* color-space modelling revealed two primary clusters, one corresponding to CBD and another containing Δ9-THC and CBN, suggesting early evidence of hue-based selectivity for certain analytes. Fluorescence-based differentiation using FB-BB did not produce meaningful separation among cannabinoids. Overall, this project established foundational data supporting the feasibility of a portable, low-cost, colorimetric tool for detecting cannabinoids using 3D-printed cartridges and readily accessible reagents. While additional validation and field-oriented development are needed, these findings provide a proof-of-concept framework for future roadside or point-of-collection testing technologies.