Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip

Abstract

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.