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A real-time hyper-accuracy integrative approach to peak identification using lifting-based wavelet and Gaussian model for field mobile mass spectrometer
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Cuiping Li , Jiuqiang Han , Qibin Huang , Ning Mu , Baoqiang Li , Bingqing Cao
Field mobile mass spectrometer is pivotal apparatus for real-time qualitative and quantitative analyses of chemical substances in situ environment pollution detection. To solve spectrum peak signal interfered by complicated noise, and to recognize irregular peak shape as well as quick monitoring, a real-time denoising and hyper-accuracy peak identification integrative approach for field mobile mass spectrometer using lifting-based wavelet transform (LWT) and Gaussian model has been developed. First, LWT was applied to eliminate the noise and to search for mass peak parameters in raw spectral peak data. Then, fitting the irregular mass peaks with Gaussian multi-peaks, a regular spectrum signal was obtained for further processing. Both of synthetic and apparatus experiment results show that LWT is a fast and effective denoising and peak identification method and retained the original peak features. The denoising effect (SNR/RMSE) by LWT was superior to Savitzky–Golay method used widely by experimental mass spectrometer, and the processing time was shortened obviously. Moreover integrated with Gaussian fitting algorithm, the peak parameters (the peak area A, centroid c, and half peak's width w) had been optimized. As the result, qualitative and quantitative accuracies of FMMS increased consequently. In addition, the approach achieved data compression.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Cuiping Li , Jiuqiang Han , Qibin Huang , Ning Mu , Baoqiang Li , Bingqing Cao
Field mobile mass spectrometer is pivotal apparatus for real-time qualitative and quantitative analyses of chemical substances in situ environment pollution detection. To solve spectrum peak signal interfered by complicated noise, and to recognize irregular peak shape as well as quick monitoring, a real-time denoising and hyper-accuracy peak identification integrative approach for field mobile mass spectrometer using lifting-based wavelet transform (LWT) and Gaussian model has been developed. First, LWT was applied to eliminate the noise and to search for mass peak parameters in raw spectral peak data. Then, fitting the irregular mass peaks with Gaussian multi-peaks, a regular spectrum signal was obtained for further processing. Both of synthetic and apparatus experiment results show that LWT is a fast and effective denoising and peak identification method and retained the original peak features. The denoising effect (SNR/RMSE) by LWT was superior to Savitzky–Golay method used widely by experimental mass spectrometer, and the processing time was shortened obviously. Moreover integrated with Gaussian fitting algorithm, the peak parameters (the peak area A, centroid c, and half peak's width w) had been optimized. As the result, qualitative and quantitative accuracies of FMMS increased consequently. In addition, the approach achieved data compression.
Coefficient of variation, signal-to-noise ratio, and effects of normalization in validation of biomarkers from NMR-based metabonomics studies
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Bo Wang , Aaron M. Goodpaster , Michael A. Kennedy
A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15–30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5–10%). The inverse relationship between CV and SNR roughly obeyed a log10 dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log10(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Bo Wang , Aaron M. Goodpaster , Michael A. Kennedy
A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15–30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5–10%). The inverse relationship between CV and SNR roughly obeyed a log10 dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log10(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.
Key wavelengths selection from near infrared spectra using Monte Carlo sampling–recursive partial least squares
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Mingjin Zhang , Shizhi Zhang , Jibran Iqbal
Variable selection is a critical step in data analysis for near infrared spectroscopy. Recently, many studies have been reported on variable selection and researchers have proposed a large number of methods to identify variables (wavelengths) that contribute useful information. In the present study, a key wavelengths selection method named Monte Carlo sampling–recursive partial least squares (MCS-RPLS) is proposed. The method mainly includes three steps: (1) Monte Carlo sampling; (2) feature selection for each subset; and (3) determination of the optimum feature set for the dataset. The method has been used for feature selection and multivariate calibration on four near infrared spectroscopic datasets: corn moisture, corn protein, HSA and γ-globulin of biological samples. And the 10-fold cross validation results are compared with those obtained by full spectra-PLS, Moving Window Partial Least Squares (MWPLS), Monte Carlo-based Uninformative Variable Elimination (MC-UVE) and CARS. The results showed that the data dimensionalities and the RMSECV values of the selected variables are greatly reduced, thus the MCS-RPLS is available for feature selection from NIR data.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Mingjin Zhang , Shizhi Zhang , Jibran Iqbal
Variable selection is a critical step in data analysis for near infrared spectroscopy. Recently, many studies have been reported on variable selection and researchers have proposed a large number of methods to identify variables (wavelengths) that contribute useful information. In the present study, a key wavelengths selection method named Monte Carlo sampling–recursive partial least squares (MCS-RPLS) is proposed. The method mainly includes three steps: (1) Monte Carlo sampling; (2) feature selection for each subset; and (3) determination of the optimum feature set for the dataset. The method has been used for feature selection and multivariate calibration on four near infrared spectroscopic datasets: corn moisture, corn protein, HSA and γ-globulin of biological samples. And the 10-fold cross validation results are compared with those obtained by full spectra-PLS, Moving Window Partial Least Squares (MWPLS), Monte Carlo-based Uninformative Variable Elimination (MC-UVE) and CARS. The results showed that the data dimensionalities and the RMSECV values of the selected variables are greatly reduced, thus the MCS-RPLS is available for feature selection from NIR data.
A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): José L. Godoy , Jorge R. Vega , Jacinto L. Marchetti
A new statistical monitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis in multivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS-decomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): José L. Godoy , Jorge R. Vega , Jacinto L. Marchetti
A new statistical monitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis in multivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS-decomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems.
Predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds using bee algorithm and adaptive neuro-fuzzy inference system
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): K. Zarei , M. Atabati , S. Moghaddary
A new method was developed for prediction of the heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. A set of 1497 zero- to three-dimensional descriptors was generated for each molecule in the data set. A major problem of modeling is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm (BA) was used to select the best descriptors. Bee algorithm is a new population-based optimization algorithm, which is derived from the observation of real bees and proposed to feature selection. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Squared correlations of coefficients were obtained as 0.9980, 0.9996 and 0.9988 for training, test and validation sets, respectively. In comparison with genetic algorithm (GA)-ANFIS and multiple linear regression (MLR)-ANFIS, the results showed that Bee-ANFIS can be used as a powerful model for prediction of heats of combustion of these compounds.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): K. Zarei , M. Atabati , S. Moghaddary
A new method was developed for prediction of the heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. A set of 1497 zero- to three-dimensional descriptors was generated for each molecule in the data set. A major problem of modeling is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm (BA) was used to select the best descriptors. Bee algorithm is a new population-based optimization algorithm, which is derived from the observation of real bees and proposed to feature selection. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Squared correlations of coefficients were obtained as 0.9980, 0.9996 and 0.9988 for training, test and validation sets, respectively. In comparison with genetic algorithm (GA)-ANFIS and multiple linear regression (MLR)-ANFIS, the results showed that Bee-ANFIS can be used as a powerful model for prediction of heats of combustion of these compounds.
Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): E.P. Tao , W.H. Shen , T.L. Liu , X.Q. Chen
This paper presents a PCA (principal component analysis)-based diagnostic approach, combining the principal component scores with the principal component loadings, to determine the fault location of sensors in a pilot-scale SBR (sequencing batch reactor activated sludge process) wastewater treatment process. The PCA diagnostic model is firstly built with the historical normal data, and the determination of fault location of sensors in wastewater treatment process is further achieved through the combination of the scores with the loadings of principal components. The study results reveal that PCA model can be used to detect faults; the loadings of principal components can well represent the contributions of variables to the principal components; and the scores of principal components give a clear indication of the faulty samples. The feasibility and effectiveness of the application of the combination of score plots with loading plots for sensor fault diagnosis in the wastewater treatment process are well demonstrated in the study.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): E.P. Tao , W.H. Shen , T.L. Liu , X.Q. Chen
This paper presents a PCA (principal component analysis)-based diagnostic approach, combining the principal component scores with the principal component loadings, to determine the fault location of sensors in a pilot-scale SBR (sequencing batch reactor activated sludge process) wastewater treatment process. The PCA diagnostic model is firstly built with the historical normal data, and the determination of fault location of sensors in wastewater treatment process is further achieved through the combination of the scores with the loadings of principal components. The study results reveal that PCA model can be used to detect faults; the loadings of principal components can well represent the contributions of variables to the principal components; and the scores of principal components give a clear indication of the faulty samples. The feasibility and effectiveness of the application of the combination of score plots with loading plots for sensor fault diagnosis in the wastewater treatment process are well demonstrated in the study.
Uninformative variable elimination assisted by Gram–Schmidt Orthogonalization/successive projection algorithm for descriptor selection in QSAR
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Nematollah Omidikia , Mohsen Kompany-Zareh
Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and Cvalue's were calculated for each descriptor. In this case the Cvalue's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q2 =0.769 and R 2 =0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q2 =0.81, R 2 =0.84 and Q2 F3 =0.8.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Nematollah Omidikia , Mohsen Kompany-Zareh
Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and Cvalue's were calculated for each descriptor. In this case the Cvalue's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q2 =0.769 and R 2 =0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q2 =0.81, R 2 =0.84 and Q2 F3 =0.8.
Graphical abstract
An improved self-adaptive differential evolution algorithm and its application
25 September 2013,
09:32:08
Publication date: 15 October
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Wu Deng , Xinhua Yang , Li Zou , Meng Wang , Yaqing Liu , Yuanyuan Li
Because of the deficiencies in the global searching ability and convergence speed of the differential evolution (DE) algorithm in solving high-dimensional problems, this paper proposes an improved self-adaptive differential evolution algorithm with multiple strategies (ISDEMS) algorithm using a different search strategy and a parallel evolution mechanism. In the ISDEMS algorithm, the population is dynamically divided into multiple populations according to the fitness value of the individuals. Multiple strategies are used to improve the diversity of the individuals, to avoid premature convergence and to ensure efficiency in exchanging information among sub-populations. In addition, a self-adaptive adjustment method is introduced to automatically adjust the scaling and crossover factors during the running time. It is helpful to improve the robustness of the ISDEMS algorithm. To prove the validity of the ISDEMS algorithm for solving complex problems, thirteen benchmark problems and one real-life problem are selected to validate the performance of the ISDEMS algorithm. The experiment results show that the ISDEMS algorithm is better in terms of search precision and convergence performance than the DE, ACDE and SACDE algorithms from the literature.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 128
Author(s): Wu Deng , Xinhua Yang , Li Zou , Meng Wang , Yaqing Liu , Yuanyuan Li
Because of the deficiencies in the global searching ability and convergence speed of the differential evolution (DE) algorithm in solving high-dimensional problems, this paper proposes an improved self-adaptive differential evolution algorithm with multiple strategies (ISDEMS) algorithm using a different search strategy and a parallel evolution mechanism. In the ISDEMS algorithm, the population is dynamically divided into multiple populations according to the fitness value of the individuals. Multiple strategies are used to improve the diversity of the individuals, to avoid premature convergence and to ensure efficiency in exchanging information among sub-populations. In addition, a self-adaptive adjustment method is introduced to automatically adjust the scaling and crossover factors during the running time. It is helpful to improve the robustness of the ISDEMS algorithm. To prove the validity of the ISDEMS algorithm for solving complex problems, thirteen benchmark problems and one real-life problem are selected to validate the performance of the ISDEMS algorithm. The experiment results show that the ISDEMS algorithm is better in terms of search precision and convergence performance than the DE, ACDE and SACDE algorithms from the literature.
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