Key Laboratory of Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing, 400044, People's Republic of China.
National Engineering Research Center of Solid-State Brewing, Luzhou Laojiao Group Co. Ltd., Luzhou, 646000, People's Republic of China.
Mikrochim Acta. 2019 Jul 3;186(8):496. doi: 10.1007/s00604-019-3601-8.
Carboxylic acids (CAs) have been reported as potential biomarkers of specific diseases or human body odors. A visual sensor array is described here that is based on indicator displacement assays (IDAs). The arrays were prepared by spotting solutions of the following metal complexes: Murexide-Ni(II), murexide-Cu(II), zincon-Zn(II) and xylenol orange-Cu(II), with the capability of discrimination of 15 carboxylic acids (CAs) and the quantitation of pyruvic acid (PA). Clear differences can be observed through distinctive difference maps obtained within 5 min by subtraction of red, green and blue (RGB) values of digital images after and before exposure to analytes. After an analysis of multidimensional data by pattern recognition algorithms including HCA, PCA and LDA, excellent classification specificity, and accuracy of >96% were obtained for all samples. The IDA array exhibited a linear range from 10 to 1500 μM with a theoretical detection limit of 3.5 μM towards PA. Recoveries of real samples varied from 84.8% to 114.3%. As-fabricated IDA sensor array showed an excellent selectivity among other organic interfering substances and a good batch to batch reproducibility, demonstrating its robustness. All these observations suggested that the IDA sensor array is one of the most promising paths for the discrimination of CAs. Graphical abstract Schematic diagram of indicator displacement assay (a), the procedure for acquisition of difference maps (b), and pattern recognitions for CAs (c). The method uses hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA).
羧酸（CA）已被报道为特定疾病或人体气味的潜在生物标志物。本文描述了一种基于指示剂置换分析（IDA）的可视化传感器阵列。该阵列是通过点样以下金属配合物的溶液制备的：Murexide-Ni（II）、Murexide-Cu（II）、zincon-Zn（II）和二甲酚橙-Cu（II），具有区分 15 种羧酸（CA）和定量测定丙酮酸（PA）的能力。通过在暴露于分析物前后减去数字图像的 RGB 值，可以在 5 分钟内获得明显的差异图，从而观察到明显的差异。通过包括 HCA、PCA 和 LDA 在内的模式识别算法对多维数据进行分析后，获得了所有样品的分类特异性和准确性均>96%。IDA 阵列对 PA 的线性范围为 10 至 1500μM，理论检测限为 3.5μM。实际样品的回收率在 84.8%至 114.3%之间。所制备的 IDA 传感器阵列在其他有机干扰物质之间表现出优异的选择性和良好的批间重现性，显示出其稳健性。所有这些观察结果表明，IDA 传感器阵列是区分 CA 的最有前途的方法之一。
