Immune algorithms in analytical chemistry

Shao, Xueguang; Yu, Zhenhai; Sun, Li

doi:10.1016/s0165-9936(03)00205-x

Cited by 29 publications

(20 citation statements)

References 14 publications

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“…Thus, the M matrix in this simulated GC/MS signal above is 1.000 0.030 0.018 0.035 1.000 2.531 . 0.071 0.400 1.000 After the rough solution is obtained, an IA [8][9][10]26] is used to calculate the accurate chromatographic profiles, since the IA method is more accurate, especially when there is noise in the experimental signals. The mass spectra of benzene, toluene and xylene after PMBK are shown in Figure 1(c) and the corresponding chromatographic profiles are shown in Figure 2(c).…”

Section: Post-modification Strategy Based On Knowledge (Pmbk)mentioning

confidence: 99%

“…These overlapping peaks may affect the qualitative analysis by mass spectral information and worsen the quantitative measurement by chromatographic peak area, and sometimes even make the analysis completely impossible. Despite experimental and technical efforts to improve the separation efficiency, many chemometrical tools have been developed to obtain the spectral information and the corresponding concentration profiles directly from the overlapping chromatographic peaks, such as principal compo nent analysis (PCA) [1,2] , chemical factor analysis (CF-A) [3][4][5] , multivariate curve resolution techniques [6,7] , immune algorithm(IA) [8][9][10] , wavelet transform (WT) [11][12][13][14] , etc.In recent years, independent component analysis (ICA) [15][16][17][18] was greatly developed as a potential statistical technique for blind source separation (BSS). ICA was firstly applied to solving the cocktail party problem.…”

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confidence: 99%

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A post-modification approach to independent component analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

2007

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Independent component analysis (ICA) has demonstrated its power to extract mass spectra from overlapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is proposed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.independent component analysis (ICA), post modification, immune algorithm (IA), GC/MS Gas chromatography mass spectroscopy (GC/MS) is now a powerful tool to qualitatively and quantitatively analyze the composition of mixtures, especially the practical complex samples. The overlapping chromatographic peaks, which are generated by the incomplete separation in column, are often observed in practical analysis, since the compounds in such systems are complicated and often similar in properties. These overlapping peaks may affect the qualitative analysis by mass spectral information and worsen the quantitative measurement by chromatographic peak area, and sometimes even make the analysis completely impossible. Despite experimental and technical efforts to improve the separation efficiency, many chemometrical tools have been developed to obtain the spectral information and the corresponding concentration profiles directly from the overlapping chromatographic peaks, such as principal compo nent analysis (PCA) [1,2] , chemical factor analysis (CF-A) [3][4][5] , multivariate curve resolution techniques [6,7] , immune algorithm(IA) [8][9][10] , wavelet transform (WT) [11][12][13][14] , etc.In recent years, independent component analysis (ICA) [15][16][17][18] was greatly developed as a potential statistical technique for blind source separation (BSS). ICA was firstly applied to solving the cocktail party problem. It concerns that m receptors, i.e., microphones, are used to record the mixed speech signals from n sources, i.e., the speakers, in a cocktail party. Through making full use of the high-order statistical characteristics of the source, i.e., the fourth-order central moment, ICA can effectively estimate the independent components from their linear combinations without any additional information about the source signals. This method is now widely applied in the signal processing fields, such as biomedical signals [19] , image processing [20] and financial

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Section: Post-modification Strategy Based On Knowledge (Pmbk)mentioning

confidence: 99%

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confidence: 99%

A post-modification approach to independent component analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

2007

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“…Usually, it is not easy to decide these two parameters when noise exists (see Appendix A for explanation). The immune algorithm (IA) [9,10] can extract the compounds from noise. But, the standard chromatogram peaks for compounds are needed from experiments in advance.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

Cui

Zhou

Poon

et al. 2014

Applied Computational Intelligence and Soft Computing

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This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM) model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1) the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2) the GRCM-PSO method is able to handle the real HPLC-DAD data set.

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“…And two important parameters, which are difficult to obtain (See discussion 2 and 3 in the experiments), are involved: 1) a threshold for determining the number of the compounds; 2) the noise level of the HPLC-DAD data set for estimating the initial spectra. The Immune Algorithm (IA) [9] can extract compounds from noise, but standard profiles for compounds are needed. The method of Independent Component Analysis (ICA) [10] can separate the HPLC-DAD data set without knowing the number of the compounds, but with many useless independent components in the results which need to be identified.…”

Section: Introductionmentioning

confidence: 99%

Separation model of Generalized Reference Curve Measurement for HPLC-DAD and it solution by multi-target Bare Bones Particle Swarm Optimization

Cui

Poon

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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In order to separate the chromatogram peaks and spectra from the High Performance Liquid Chromatography with Diode Array Detector (HPLC-DAD) data set, a separation model of Generalized Reference Curve Measurement and its solution by multitarget Bare Bones Particle Swarm Optimization (GRCMmBBPSO) is proposed in this paper. Firstly, parameters are constructed which will generate Reference Curves (RCs) for chromatogram peaks. Secondly, the GRCM model is proposed to transform all these parameters to scalar values, which indicate the possibility for the HPLC-DAD data set containing chromatogram peaks similar to the RCs constructed by corresponding parameters. Then, the algorithm of mBBPSO is introduced to calculate the optimal parameters by minimizing the scalar values. Finally, the spectra are estimated. Through simulations and experiments, following conclusions are drawn: (1) the GRCMmBBPSO method can extract chromatogram peaks from the simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist; (2) the GRCMmBBPSO method can be applied to real HPLC-DAD data set.

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Immune algorithms in analytical chemistry

Cited by 29 publications

References 14 publications

A post-modification approach to independent component analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

A post-modification approach to independent component analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

Separation model of Generalized Reference Curve Measurement for HPLC-DAD and it solution by multi-target Bare Bones Particle Swarm Optimization

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