Indolizine-based fluorescent compounds array for noninvasive monitoring of glucose in bio-fluids using on-device machine learning - Kim, Hyungi; Lee, Sungmin; Lee, Kyung Won; Kim, Eun Su; Kim, Hyung Mo; Im, Hyungsoon; Yoon, Hyun C.; Ko, Jeong Gil; Kim, Eunha (Dyes and Pigments, 2023)
Monitoring blood sugar level is highly important for managing diabetes and preventing complications. Herein, we describe a paper-based portable assay system for highly accurate monitoring of glucose levels in tears. The system combines enzyme reactions, the perturbation of fluorescence properties, automated image analysis, and pattern recognition powered by a machine-learning algorithms in a single assay without extra manual steps. A small size (20 × 20 mm) assay system was prepared simply by printing black wax crosswise on cellulose paper, melting the wax with heat, and depositing four different fluorescent compounds and glucose oxidase at the test zones. Applying a small drop of sample in the center zone of the system evenly distributes the samples to the test zone via the paper capillary process and eventually induces a generation of unique fluorescence pattern that is specific to glucose. Two models are used to analyze image data collected from the fluorescent compounds array to make accurate estimations on glucose concentration: a random forest model that discretely classifies the concentration with respect to the training data granularity and an SVM regression model that estimates glucose concentration at interpolated finer granularity. These models are combined with feature engineering techniques for efficient sensor data processing on resource limited mobile platforms. As a result, using the random forest approach, glucose concentration in artificial tears can be accurately classified with 96.7% accuracy (0.2 mM intervals) (MSE of 0.060 mM) and the SVM regression model results in an MSE of 0.026 mM.
Keywords: Assay system; Blood glucose monitoring; Fluorescent compound array; Fluorescent compounds; Glucose concentration; Indolizines; Machine-learning; Non-invasive blood glucose monitoring; Paper-based diagnostic; Test zone; Fluorescent compounds array; Indolizine; Machine learning; Non-invasive blood glucose monitoring; Paper-based diagnostics