Cocchi Marina, Hidalgo-Hidalgo-de-Cisneros J L, Naranjo-Rodríguez I, Palacios-Santander J M, Seeber Renato, Ulrici Alessandro
Dipartimento di Chimica, Università di Modena e Reggio Emilia, Via Campi 183, Modena, Italy.
Talanta. 2003 Mar 10;59(4):735-49. doi: 10.1016/S0039-9140(02)00615-X.
Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible.
最近有报道称多元校准在电化学领域成功应用，采用了多种方法，如多元线性回归（MLR）、连续回归、偏最小二乘回归（PLS）和人工神经网络（ANN）。尽管这些方法表现良好，但如今人们认为它们可以受益于旨在消除基线效应、降低噪声和压缩数据的数据变换。在这种情况下，小波变换似乎是一个非常有前途的工具。在此，我们提出一种基于快速小波变换的方法，用于校准前的特征选择。作为基准，使用了一个由差分脉冲阳极溶出伏安法测量的铅和铊混合物数据集，该数据集给出了严重重叠的响应。比较了三种回归技术：MLR、PLS和ANN。获得了良好的预测和有效模型。通过检查重建信号，可以识别和解释伏安图中的重要区域。
