Data Mining of Ultraviolet Spectral Data Using a Partial Least Squares with Data Fusion

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(II). Data fusion is a technique that seamlessly integrates information from disparate source 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 are 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. Experimental results showed the DF-PLS method to be successful for simultaneous multicomponent determination even where there was severe overlap of spectra and to be better than PLS.