Application of linear and nonlinear pattern recognition techniques for discrimination of fluoroquinolones using modified AgNPs-based colorimetric sensor array

Abstract

Surface plasmon resonance-based colorimetric sensor arrays made up of metallic nanoparticles (NPs) are powerful tools that are mostly used for pattern recognition and quantification purposes, especially in the case of pharmaceutical and biologically active substances. Ease of fabrication, simplicity, cost-effectiveness, and huge amounts of collectible data besides the various fabrication techniques, expand their applications in analytical chemistry, especially when combined with chemometrics data analysis tools. In this study, a colorimetric sensor array is fabricated using unmodified citrate-capped together with the modified AgNPs for the discrimination of three fluoroquinolone drugs ofloxacin, ciprofloxacin, and moxifloxacin in real serum samples. The aggregation-induced color changes of the AgNPs were monitored during the time and the collected kinetic-spectrophotometric huge data in the ranges 300–800 nm was compressed by Discrete Wavelet Transform (DWT) to remove the redundant variables and analyzed with different unsupervised and supervised pattern recognition techniques like Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA) and XY-Fused neural networks (XYF). The best classification performance was achieved using the genetic algorithm-optimized XY-Fused networks with 92% accuracy and an error of cross-validation equal to zero. In addition, all of the spiked real serum samples were classified correctly using the generated model according to the assigned class labels.