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papers from the latest issue:
A real-time hyper-accuracy integrative approach to peak identification using lifting-based wavelet and Gaussian model for field mobile mass spectrometer
15 August 2013,
10:18:44
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
15 August 2013,
10:18:44
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
15 August 2013,
10:18:44
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.
15 August 2013,
10:18:44
Publication date: 15 August
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Andrés Mauricio Castillo , Lalita Uribe , Luc Patiny , Julien Wist
An efficient method to extract and store information from NMR spectra is proposed that is suitable for comparison and construction of a search engine. This method based on trees doesn't require any peak picking or any pre-treatment of the data and is found to outperform the currently available methods, both in terms of compactness and velocity. Our approach was tested for 1D proton spectra and 2D HSQC spectra and compared with the method proposed by Pretsch and coworkers [1,2] [Bodis et al. 2007, Bodis et al. 2009]. Additionally, the correspondence between spectral and structural similarity was evaluated for both methods.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Andrés Mauricio Castillo , Lalita Uribe , Luc Patiny , Julien Wist
An efficient method to extract and store information from NMR spectra is proposed that is suitable for comparison and construction of a search engine. This method based on trees doesn't require any peak picking or any pre-treatment of the data and is found to outperform the currently available methods, both in terms of compactness and velocity. Our approach was tested for 1D proton spectra and 2D HSQC spectra and compared with the method proposed by Pretsch and coworkers [1,2] [Bodis et al. 2007, Bodis et al. 2009]. Additionally, the correspondence between spectral and structural similarity was evaluated for both methods.
Applications of a new empirical modelling framework for balancing model interpretation and prediction accuracy through the incorporation of clusters of functionally related variables
15 August 2013,
10:18:44
Publication date: 15 August
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Marco S. Reis
Current classification and regression methodologies are strongly focused on maximizing prediction accuracy. Interpretation is usually relegated to a second stage, after model estimation, where its parameters and related quantities are scrutinized for relevant information regarding the process and phenomena under analysis. Network-Induced Supervised Learning (NI-SL) is a recently proposed framework that balances the goals of prediction accuracy and interpretation [1], by adopting a modelling formalism that matches more closely the dependency structure of variables in current complex systems. This framework computes interpretable features that are incorporated in the final model, which effectively constrain the predictive space to be used. However, this restriction does not compromise prediction ability, which quite often is enhanced. Both classification and regression problems can be handled. Four widely different real world datasets were used to illustrate the main features claimed for the NI-SL framework.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Marco S. Reis
Current classification and regression methodologies are strongly focused on maximizing prediction accuracy. Interpretation is usually relegated to a second stage, after model estimation, where its parameters and related quantities are scrutinized for relevant information regarding the process and phenomena under analysis. Network-Induced Supervised Learning (NI-SL) is a recently proposed framework that balances the goals of prediction accuracy and interpretation [1], by adopting a modelling formalism that matches more closely the dependency structure of variables in current complex systems. This framework computes interpretable features that are incorporated in the final model, which effectively constrain the predictive space to be used. However, this restriction does not compromise prediction ability, which quite often is enhanced. Both classification and regression problems can be handled. Four widely different real world datasets were used to illustrate the main features claimed for the NI-SL framework.
Multivariate Curve Resolution of incomplete data multisets
15 August 2013,
10:18:44
Publication date: 15 August
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Marta Alier , Romà Tauler
In this paper the application of the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS) to incomplete data multisets is explored. The experimental incomplete data multiset studied in this work is taken from a previous multiannual atmospheric monitoring study of the changes of ozone and nitrogen oxide concentrations in an air quality sampling station located in the city of Barcelona, in which some of the individual data sets were missing. Based on the preliminary results obtained in this study, new data multisets, complete and incomplete, with different levels of noise were simulated and analysed by a new variant of the MCR-ALS method which optimises a combined error function including all possible complete data subsets derived from the original incomplete data multiset. Conclusions are drawn about the effects of data completeness on the results obtained for different noise levels and on the viability of trilinear models.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 127
Author(s): Marta Alier , Romà Tauler
In this paper the application of the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS) to incomplete data multisets is explored. The experimental incomplete data multiset studied in this work is taken from a previous multiannual atmospheric monitoring study of the changes of ozone and nitrogen oxide concentrations in an air quality sampling station located in the city of Barcelona, in which some of the individual data sets were missing. Based on the preliminary results obtained in this study, new data multisets, complete and incomplete, with different levels of noise were simulated and analysed by a new variant of the MCR-ALS method which optimises a combined error function including all possible complete data subsets derived from the original incomplete data multiset. Conclusions are drawn about the effects of data completeness on the results obtained for different noise levels and on the viability of trilinear models.
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