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

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.

Figure 1.

Fluorescent compound array for portable classification of chemicals assisted by machine learning algorithms. a) Chemical structure of the Kaleidolizine (left) and schematic figure of the atomic coefficients of the HOMO and LUMO of indolizine core skeleton (right). The sizes and colors of the circles indicate the π-electron density and phase difference of the orbitals, respectively. b) Photograph of fluorescent compound array made with 75 different KIz compound on wax-printed cellulose paper under irradiation with ambient light (up) and 365 nm UV light (down). Inserted image represent comparable size of single array with the coin (korean hundred won). c) Photograph of fluorescent compound array for proof of concept study (made with KIz 42, and KIz 43) under 365 nm handheld UV before (left up) and after (left right) the exposure with volatile organic compound (3-Bromopropionic acid). Schematic representation for the emission wavelength changes induced by ICT process in KIz after VOC exposure (Right). d) 3D illustration of the cradle for portable application of the system. e) photograph for actual application (right). f) Schematic representation of automated analysis of fluorescent pattern changes assisted by machine learning algorithm.

Figure 2.

The photophysical property of Kaleidolizine library. a) Photograph of KIz 40, KIz 39 KIz 38 and KIz 06 under UV irradiation (365 nm) in solution and solid state. b) Scatter plot of KIz derivatives, color coded according to the R₃ substituent (blue for acetyl, yellow for hydrogen and red for trifluoromethyl). c) Scatter plot of KIz derivatives, grouped according to R₁ substituent in solution (left) and in solid (right) state. Color of the dot represent the R₁ substituent (red, orange, yellow, green and blue for dimethylamino, methoxy, hydrogen, acetyl and trifluoromethyl, respectively). Size of the dot represent Hammet constant of R₂ substituent.

Figure 3.

Solid state photophysical property changes of KIz derivatives exposed with VOC. Photophysical property of KIz 15 (left column), KIz 40 (middle column), and KIz 65 (right column), which were spin-coated on glass, was measured before (gray spectrum) and after (colored spectrum) exposure with acetic acid (upper row), ethyl acetate (middle row), and ethylamine (downer row).

Figure 4.

Fluorescence pattern changes of KIz sensor array exposed with VOC. a) The representative bar chart analysis showing the hue difference (y axis) of individual KIz compounds on array (x axis) exposed with 9 different acids (5 replicates). Only the data from KIz compound exhibit the hue difference above the threshold (more than 20 hue difference) more than 3 times were marked in darker color than others. b) Representative photograph of the KIz sensor array under irradiation with 365 nm hand held UV lamp. White box indicate the array spot exhibit hue difference above the threshold more than 3 times. Please see supporting information for the complete data set (from Fig. S9 to Fig. S43).

Figure 5.

Machine learning algorithm assisted VOC classification a) PCA analysis of data set acquired with pattern changes of fluorescent array, exposed with 5 different VOC (acetic acid (yellow), acetone (red), ethylenediamine (violet), phenol (green) and toluene (blue)). For each VOC, data from 100 replicates were analyzed. b) Data description and summary of classification accuracy for each VOC type. c-e) Confusion matrices of 5-folds cross-validation Random forest models with RGB Euclidean distance (c), Hue difference (d) and CIEDE2000 dataset (e).