Machine Learning-Assisted Biomass-Derived Carbon Dots as Fluorescent Sensor Array for Discrimination of Warfarin and Its Metabolites.
Journal:
Langmuir : the ACS journal of surfaces and colloids
PMID:
39797801
Abstract
Warfarin (WAR), an effective oral anticoagulant, is of utmost importance in treating many diseases. Despite its significance, rapid and precise discrimination of WAR remains a formidable challenge, especially facing its structural analogs of metabolites. Here, three kinds of herb-derived N-doped carbon dots (NCDs) were greenly synthesized via a fast and simple microwave-assisted method. Three NCDs showcased respectable blue fluorescent (FL) properties and sensing capabilities for the discrimination of WAR and its metabolites. To improve accuracy in identifying WAR and its metabolites, a sensor array composed of three unique herb-derived NCDs was meticulously designed. Combined with the machine learning model, the sensor array displayed a strong immunity to interference in the discrimination of the WAR, even in unknown samples. Meanwhile, the FL sensing mechanism is deeply expounded. The methodology proffers broad prospects for biomass-derived nanomaterials and provides an effective and feasible project for pharmaceutical analysis by capitalizing on machine learning.