Machine learning: Assisted multivariate detection and visual image matching to build broad-specificity immunosensor
Machine learning: Assisted multivariate detection and visual image matching to build broad-specificity immunosensor
复制标题
机器学习：辅助多变量检测和视觉图像匹配，构建广泛特异性的免疫传感器
DOI：
10.1016/j.snb.2021.129872
复制
发表时间：
2021-04-10
影响因子：
8.4
通讯作者：
Liu, Yingju
中科院分区：
文献类型：
--
作者：
Qileng, Aori;Zhu, Hongshuai;Liu, Yingju
提出了一种基于图像匹配的机器学习方法，构建了一种检测多种赭曲霉毒素的广特异度免疫传感器。在免疫反应过程中，抗坏血酸2-磷酸(AAP)被固定化的碱性磷酸酶催化生成抗坏血酸(AA)，从而引发以下反应。首先，银离子(Ag+)可以被AA还原，在Au纳米联吡啶(Au Nbps)上形成银涂层，改变Au nBps的直径和溶液的颜色。第二，AA可以作为牺牲剂来增强硫化镉的光电化学电流。Ce~(4+)可被还原为Ce3+，并有很强的荧光。这样，建立了包括比色、光电化学和荧光的三维信号，然后将其转换为用于图像匹配的颜色信号。结果表明，该方法可以预测多个赭曲霉毒素，表明机器学习在结合免疫传感器的多信号检测和多目标检测方面具有很大的潜力。
The machine learning based on image matching is presented to construct a broad-specificity immunosensor for the detection of multiple ochratoxins. During the immunoreaction, ascorbic acid 2-phosphate (AAP) is catalyzed by the immobilized alkaline phosphatase to form ascorbic acid (AA), which can initiate the following reactions. First, silver ions (Ag+) can be reduced by AA to form silver coating on Au nanobipyramids (Au NBPs), changing the diameter of Au NBPs and the color of the solution. Second, AA can act as a sacrificial reagent to enhance the photoelectrochemical (PEC) current of CdS. Third, Ce4+ can be reduced to form Ce3+ and an intense fluorescence was discovered. Thus, a three-dimensional signal including colorimetry, photoelectrochemistry and fluorescence is built and then transformed to color signals for image matching. Results show that this method can predict multiple ochratoxins, suggesting that machine learning has great potential in combining multi-signal detection and multi-target detection by immunosensors.