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

SENSORS AND ACTUATORS B-CHEMICAL

卷:329

Article

Kim, Hyungi¹  Choi, Sang-Kee¹  Ahn, Jungmo²  Yu, Hojeong³  Min, Kyoungha^3,4  Hong, Changgi⁵  Shin, Ik-Soo⁶  Lee, Sanghee⁷  Lee, Hakho^3,8  Im, Hyungsoon^3,8  Ko, JeongGil⁹  Kim, Eunha^1,5 

[1] Ajou Univ, Dept Mol Sci & Technol, Suwon 16499, South Korea

[2] Ajou Univ, Dept Comp Engn, Suwon 16499, South Korea

[3] Massachusetts Gen Hosp, Ctr Syst Biol, Boston, MA 02114 USA

[4] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

[5] Ajou Univ, Dept Appl Chem & Biol Engn, Suwon 16499, South Korea

[6] Soongsil Univ, Dept Chem, Seoul 07027, South Korea

[7] Korea Inst Sci & Technol, Brain Sci Inst, Ctr Neuromed, Seoul 02792, South Korea

[8] Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA

[9] Yonsei Univ, Sch Integrated Technol, Incheon 21983, South Korea

关键词: Indolizine;    Fluorescent compound array;    Pattern recognition;    Machine learning;    Multiplexing;   

DOI  :  10.1016/j.snb.2020.129248

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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