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.

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用于使用上转换横向流生物传感器准确分析甲基苯丙胺的卷积神经网络

甲基苯丙胺是一种强效兴奋剂，滥用会威胁人体健康和社会稳定。快速准确地定量甲基苯丙胺对于有效抑制甲基苯丙胺的滥用和流行至关重要。在本文中，我们提出了一种便携式荧光读取器，它具有上转换纳米粒子标记的横向流动免疫测定 (LFIA)，用于快速准确地定量甲基苯丙胺。基于LFIA中甲基苯丙胺抗原与抗体的特异性结合，设计荧光读数仪分别捕获和记录测试线和对照线的荧光强度T和C，并计算T/C比以确定甲基苯丙胺的浓度。甲基苯丙胺的线性范围为 0.1–100 ng/mL。由于传感器往往容易受到噪声干扰，仅使用 T/C 比来区分甲基苯丙胺的弱阳性和阴性样本会导致结果不准确。为了解决这个问题，我们应用卷积神经网络 (CNN) 来学习不同甲基苯丙胺浓度（0、0.01、0.05、0.1 和 0.5 ng/mL）的图像特征，以准确检测弱阳性和阴性样本。结果表明，所提方法能够有效检测甲基苯丙胺的弱阳性和弱阴性样本，准确率高达92%。CNN 提供了一种在上转换纳米粒子标记的 LFIA 中准确分析弱阳性和阴性样本的新方案。我们应用卷积神经网络 (CNN) 学习不同甲基苯丙胺浓度（0、0.01、0.05、0.1 和 0.5 ng/mL）的图像特征，以准确检测弱阳性和阴性样本。结果表明，所提方法能够有效检测甲基苯丙胺的弱阳性和弱阴性样本，准确率高达92%。CNN 提供了一种在上转换纳米粒子标记的 LFIA 中准确分析弱阳性和阴性样本的新方案。我们应用卷积神经网络 (CNN) 学习不同甲基苯丙胺浓度（0、0.01、0.05、0.1 和 0.5 ng/mL）的图像特征，以准确检测弱阳性和阴性样本。结果表明，所提方法能够有效检测甲基苯丙胺的弱阳性和弱阴性样本，准确率高达92%。CNN 提供了一种在上转换纳米粒子标记的 LFIA 中准确分析弱阳性和阴性样本的新方案。