Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing

Based on indolizine fluorescent core skeleton, a library of 75 different fluorescent derivatives were synthesized. Machine-learning algorithm allowed differentiation of 35 different volatile organic compounds via analysis of fluorescent pattern changes of fluorescent compound array, using a smartphone-based handheld detection system. Classification accuracy was 97 %. [Display omitted] •The library of 75 different fluorescent KIz derivatives were designed and synthesized.•KIz core skeleton have the full-color-tunable solid state fluorescence property.•Fluorescent pattern changes of KIz array were specific to target chemicals adsorbed on that paper.•Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97%. Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds. Using an indolizine structure as a fluorescent core skeleton, named Kaleidolizine (KIz), a library of 75 different fluorescent KIz derivatives were designed and synthesized. These KIz derivatives are simultaneously excited by a single ultraviolet (UV) light source and emit diverse fluorescence colors and intensities. For multiplexed POC sensing system, fluorescent compounds array on cellulose paper was prepared and the pattern of fluorescence changes of KIz on array were specific to target chemicals adsorbed on that paper. Furthermore, we developed a machine-learning algorithm for automated, rapid analysis of color and intensity changes of individual sensing arrays. We showed that the paper sensor arrays could differentiate 35 different volatile organic compounds using a smartphone-based handheld detection system. Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97 % in the VOC detection. The highly multiplexed paper sensor could have favorable applications for monitoring a broad-range of environmental toxins, heavy metals, explosives, pathogens.