Pseudomonas aeruginosa detection based on droplets incubation using an integrated microfluidic chip, laser spectroscopy, and machine learning

Abstract

Pseudomonas aeruginosa is an opportunist pathogen responsible for causing several infections in the human body, especially in patients with weak immune systems. The proposed approach reports a novel pathogens detection system based on cultivating microdroplets and acquiring the scattered light signals from the incubated droplets using a microfluidic device. Initially, the microdroplets were generated and incubated to cultivate bacteria inside the microdroplets. The second part of the microfluidic chip is the detection module, embedded with three optical fibers to connect laser light and photosensors. The incubated droplets were reinjected in the detection module and passed through the laser light. The surrounding photosensors were arranged symmetrically at 45° to the flowing channel for acquiring the scattered light signal. The noise was removed from the acquired data, and time-domain waveform features were evaluated. The acquired features were trained using machine learning classifiers to classify P. aeruginosa. The k-nearest neighbors (KNN) showed superior classification performance with 95.6 % accuracy among other classifiers, including logistic regression (LR), support vector machines (SVM), and naïve Bayes (NB). The proposed research was performed to validate the method for pathogens detection with a concentration of 105 CFU/mL. The total duration of 6 h is required to test the sample, including five hours for droplets incubation and one hour for sample preparation and detection using light scattering module. The results indicate that acquiring the light scattering patterns from incubated droplets can detect P. aeruginosa using machine learning classification. The proposed system is anticipated to be helpful as a rapid device for diagnosing pathogenic infections.