Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.
Sensors (Basel). 2020 Aug 25;20(17):4798. doi: 10.3390/s20174798.
Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.
在此，我们研究了主成分分析（PCA）和典范变量分析（CVA）的使用，结合 F 因子聚类度量，用于针对给定电子舌（ET）应用预先定制选择最佳传感器阵列。前者允许我们直观地比较不同传感器的性能，而后者允许我们数值评估保留或删除不同传感器对 ET 区分能力的影响。所提出的方法基于对每种研究化合物的纯储备溶液的测量，以及通过 PCA/CVA 进行的后续分析，其中通过逐步迭代删除在保留为阵列一部分时降低聚类的传感器。为了说明和评估这种方法的潜力，选择了对乙酰氨基酚、抗坏血酸和尿酸混合物的定量作为研究案例。最初，考虑了一个由八个不同电极组成的阵列，从中推导出一个由四个传感器组成的最佳阵列，以构建定量 ANN 模型。最后，将优化后的 ET 的性能与之前针对相同混合物分析报告的结果进行了基准测试，显示出了改进的性能。
