Convolutional Neural Network for Accurate Analysis of Methamphetamine With Upconversion Lateral Flow Biosensor

Methamphetamine is a powerful stimulant drug, the abuse of which threatens human health and social stability. Rapid and accurate quantification of methamphetamine is essential to inhibit the abuse and prevalence of methamphetamine effectively. In this paper, we present a portable fluorescence reader with upconverting nanoparticle-labeled lateral flow immunoassay (LFIA) for rapid and accurate quantification of methamphetamine. Based on specific binding of a methamphetamine antigen to an antibody in the LFIA, the fluorescence reader is designed to capture and record the fluorescence intensities T and C of the test and control lines, respectively, and the T/C ratio is calculated to determine the concentration of methamphetamine. The linear range for methamphetamine is 0.1–100 ng/mL. Because the sensor is often susceptible to noise interference, using only the T/C ratio to distinguish weakly positive and negative samples of methamphetamine renders the results inaccurate. To solve this problem, we applied a convolutional neural network (CNN) to learn image features of different methamphetamine concentrations (0, 0.01, 0.05, 0.1, and 0.5 ng/mL) for accurate detection of weakly positive and negative samples. The results show that the proposed method can effectively detect weakly positive and negative samples of methamphetamine with an accuracy of up to 92%. The CNN provides a novel scheme for accurate analysis of weakly positive and negative samples in upconverting nanoparticle-labeled LFIA.