Integrating independent component analysis with artificial neural network to analyze overlapping fluorescence spectra of organic pollutants.

Independent component analysis (ICA) combined with Elman recurrent neural network (ERNN) regression as a hybrid approach named ICA-ERNN was proposed for the simultaneous spectrofluorimetric determination of organic pollutants. Fluorescence spectra of these compounds under study are strongly overlapped, which does not permit direct determination without prior separation by conventional spectrofluorimetry. ICA is a blind source separation (BSS) method aiming at extracting independent source variables and their corresponding concentration profiles from the observed fluorescence spectra of chemical mixtures without using any prior knowledge about the components.

Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines.

This paper suggests a novel method named DF-LS-SVM, which is based on least squares support vector machines (LS-SVM) regression combined with data fusion (DF) to enhance the ability to extract characteristic information and improve the quality of the regression. Simultaneous multicomponent determination of Fe(III), Co(II) and Cu(II) was conducted for the first time by using the proposed method. Data fusion is a technique that integrates information from disparate sources to produce a single model or decision.

Prediction of nitrophenol-type compounds using chemometrics and spectrophotometry.

Two chemometric methods, WPT-ERNN and least square support vector machines (LS-SVM), were developed to perform the simultaneous spectrophotometric determination of nitrophenol-type compounds with overlapping spectra. The WPT-ERNN method is based on Elman recurrent neural network (ERNN) regression combined with wavelet packet transform (WPT) preprocessing and relies on the concept of combining the idea of WPT denoising with ERNN calibration for enhancing the noise removal ability and the quality of regression without prior separation. The LS-SVM technique is capable of learning a high-dimensional feature with fewer training data and reducing the computational complexity by requiring the solution of only a set of linear equations instead of a quadratic programming problem.

Multivariate calibration of spectrophotometric 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(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.

Combining orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping spectra of three kinds of metal ions.

A novel method named OSC-WPT-PLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. OSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C.

[Simultaneous resolution of overlapped spectra of three kinds of organic compounds using a wavelet packet transform-based generalized regression neural network].

A wavelet packet transform-based generalized regression neural network (WPTGRNN) was developed to perform simultaneous spectrophotometric determination of p-nitroaniline, alpha-naphthylamine and benzidine. This method combines wavelet packet transform (WPT) with generalized regression neural network (GRNN) for improving the quality of noise removal and enhancing the ability of prediction. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise.

[Studies on simultaneous quantitative determination of overlapping spectra using chemometric techniques].

Based on comparative study of eight chemometric denoising methods, a wavelet packet transform Elman recurrent neural network (WPERNN) method was developed to study simultaneous quantitative determination of overlapping spectra. The quality of noise removal and ability of regression were improved by combining wavelet packet transform with Elman recurrent neural network. Through optimization, the wavelet function, the wavelet packet decomposition levels as well as the structure and parameters of Elman recurrent neural network were selected.

Simultaneous multicomponent analysis of overlapping spectrophotometric signals using a wavelet-based latent variable regression.

A wavelet-based latent variable regression (WLVR) method was developed to perform simultaneous quantitative analysis of overlapping spectrophotometric signals. The quality of the noise removal was improved by combining wavelet thresholding with principal component analysis (PCA). A method for selecting the optimum threshold was also developed.

Quantitative determination of the multicomponents with overlapping ultraviolet spectra using wavelet-packed transform and partial least squares.

This paper presented a novel method named wavelet packet transform-based partial least squares method (WPTPLS) for simultaneous spectrophotometric determination of alpha-naphthylamine, p-nitroaniline, and benzidine. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise. The quality of the noise removal can be improved by using best-basis algorithm and thresholding operation.

[Simultaneous determination of Sm( III) and Y(III) by spectrophotometry with a wavelet packet transform latent variable regression].

A wavelet packet transform latent variable regression (WPLVR) method was developed to perform simultaneous quantitative analysis of Sm(III) and Y(III). The quality of the noise removal was improved by combining wavelet packet transform with latent variable regression (VLR). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet domain, the quality of the noise removal can be improved.

Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network.

A novel method named a wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for the simultaneous UV-visible spectrometric determination of Cu(II), Cd(II) and Zn(II). This method combined wavelet packet denoising with an Elman recurrent neural network. A wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity.

[Simultaneous quantitative analysis of three kinds of phenols by Elman recurrent neural network].

Elman recurrent neural network ( ERNN) was applied to study the simultaneous quantitative analysis of seriously overlapped spectra of a p-nitrophenol, o-nitrophenol and 2,4-dinitrophenol system. The multivariate linear regression (MLR) method was also applied in this study for comparison. Two programs (PERNN and PMLR) were designed to perform the calculations.

Simultaneous spectrophotometric determination of four metals by two kinds of partial least squares methods.

Simultaneous determination of Ni(II), Cd(II), Cu(II) and Zn(II) was studied by two methods, kernel partial least squares (KPLS) and wavelet packet transform partial least squares (WPTPLS), with xylenol orange and cetyltrimethyl ammonium bromide as reagents in the medium pH = 9.22 borax-hydrochloric acid buffer solution. Two programs, PKPLS and PWPTPLS, were designed to perform the calculations.

[Simultaneous spectrophotometric determination of multicomponent mixtures by a soft thresholding wavelet-based radial basis function neural network].

A Soft Thresholding Wavelet-based Radial Basis Function Neural network (STWRBFN) method was developed to perform simultaneous quantitative analysis of multicomponent mixtures. The quality of noise removal and regression was improved by combining wavelet soft thresholding with radial basis function neural network. Through optimization, the wavelet function, wavelet decomposition level (L), thresholding method and spread parameter sigma of RBFN were selected.

Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.

This paper presents a novel method, named wavelet packet transform based multilayer feedforward neural network with Levenberg-Marquardt and back propagation algorithm (WPTLMBP), developed for simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet packet domain the quality of noise removal can be improved. The artificial neural network was applied for non-linear multivariate calibration.