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Sens Actuators B Chem

Title:		Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing
Author(s):		Kim H; Choi SK; Ahn J; Yu H; Min K; Hong C; Shin IS; Lee S; Lee H; Im H; Ko J; Kim E;
Address:		"Department of Molecular Science and Technology, Ajou University, Suwon, 16499, Korea. Department of Computer Engineering, Ajou University, Suwon, 16499, Korea. Center for Systems Biology, Massachusetts General Hospital, Boston, MA, 02114, USA. Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, 94720, USA. Department of Applied Chemistry and Biological Engineering, Ajou University, Suwon, 16499, Korea. Department of Chemistry, Soongsil University, Seoul, 07027, Korea. Center for Neuro-Medicine, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, Korea. Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA. School of Integrated Technology, Yonsei University, Incheon, 21983, Korean"
Journal Title:		Sens Actuators B Chem
Year:		2021
Volume:		20201201
Issue:
Page Number:		-
DOI:		10.1016/j.snb.2020.129248
ISSN/ISBN:		0925-4005 (Print) 0925-4005 (Linking)
Abstract:		"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"
Keywords:		Fluorescent compound array Indolizine Pattern recognition machine learning multiplexing;
Notes:		"PubMed-not-MEDLINEKim, Hyungi Choi, Sang-Kee Ahn, Jungmo Yu, Hojeong Min, Kyoungha Hong, Changgi Shin, Ik-Soo Lee, Sanghee Lee, Hakho Im, Hyungsoon Ko, JeongGil Kim, Eunha eng R00 CA201248/CA/NCI NIH HHS/ Switzerland 2021/01/16 Sens Actuators B Chem. 2021 Feb 15; 329:129248. doi: 10.1016/j.snb.2020.129248. Epub 2020 Dec 1"