Multivariate calibration of spectrophotometric data using a partial least squares with data fusion

Abstract

A novel method named DF-PLS based on partial least squares (PLS) regression combined with data fusion (DF) was applied to enhance the ability of extracting characteristic information and the quality of regression for the simultaneous spectrophotometric determination of Cu(II), Ni(II) and Cr(III). Data fusion is a technique that seamlessly integrates information from disparate sources to produce a single model or decision. Wavelet representations of signals provide a local time-frequency description and are multiscale in nature, thus in the wavelet domain, the quality of noise removal is implemented by a scale-dependent threshold method. Information from different wavelet scales is just like different sources of information. Integrating the information from different wavelet scales to obtain a PLS model belongs to the technique of data fusion. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, by optimization, wavelet functions, decomposition level and thresholding methods and the number of PLS factors for DF-PLS were selected as Daubechies 4, 7, HYBRID thresholding and 3, respectively. The relative standard errors of prediction (RSEP) for all compounds with DF-PLS and PLS were 3.13% and 10.3%, respectively. Experimental results showed the DF-PLS method to be successful for simultaneous multicomponent determination even when severe overlap of spectra was present and proved it to be better than PLS. The DF-PLS method is a hybrid technique that combines the best attributes of DF and PLS, which makes it a promising and attractive method.