A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva
A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva
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DOI：
10.1016/j.aca.2022.340447
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发表时间：
2022-10-12
影响因子：
6.2
通讯作者：
Ebrahimi, Aida
中科院分区：
文献类型：
--
作者：
Kammarchedu, Vinay;Butler, Derrick;Ebrahimi, Aida
生物分子的多重检测在从疾病诊断到食品安全和环境监测的各个领域都具有重要价值。然而，使用单一装置/材料在混合物中实现准确和多重分析物检测是具有挑战性的。在本文中，我们展示了一种机器学习（ML）驱动的多模态分析设备，该设备基于由激光诱导石墨烯（LIG）上的电沉积多硫化钼（eMoSx）制成的单一传感材料，用于对汗液和唾液中的酪氨酸（TYR）和尿酸（UA）进行多重检测。MoSx的电沉积显示出增加的电化学活性表面积（ECSA）和异质电子转移速率常数，k 0。从电化学数据中提取特征，以训练ML模型来预测样品（单掺和混合样品）中的分析物浓度。探索不同的ML架构以优化感测性能。优化的基于ML的多峰分析系统提供的检测限（LOD）比传统的基于ML的多峰分析系统高两个数量级。
Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoSx) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoSx shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than