Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry

Zhang, Yueming; Zhang, Yuying; Zhang, Qian; Lv, Yi; Sun, Tao; Han, Lijun; Bai, Changcai; Ya, YU

doi:10.1016/j.chroma.2019.04.065

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…There are different methods to detect peaks, for example, multiscale peak detection, chemometric strategy, and wavelet transformation [17][18][19][20][21] Among these methods, a continuous wavelet transform (CWT) method has good detection ability for weak and overlapping peaks; hence, it was employed to detect Raman peaks in this study. An important step of the CWT-based peak detection method is to convolve the original signal with the mother wavelet function at different scales and shifts to generate wavelet space.…”

Section: Feature Parameter Extractionmentioning

confidence: 99%

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares

et al. 2022

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Due to the advantages of low price and convenience for end-users to conduct field-based, in-situ analysis, handheld Raman spectrometers are widely used in the identification of mixture components. However, the spectra collected by handheld Raman spectrometer usually have serious peak overlapping and spectral distortion, resulting in difficulties in component identification in the mixture. A novel method for mixture components identification based on the handheld Raman spectrometer was proposed in this study. The wavelet transform and Voight curve fitting method were used to extract the feature parameters from each Raman spectral peak, including Raman shift, maximum intensity, and full width at half maxima (FWHM), and the similarities between the mixture and each substance in the database were calculated by fuzzy membership function based on extracted feature parameters. Then, the possible substances in the mixture were preliminarily screened out as candidates according to the similarity. Finally, the Raman spectra of these candidates were used to fit the spectra of the mixture, and the fitting coefficients obtained by sparse nonnegative least squares algorithm were employed to further determine the suspected substance in the mixture. The Raman spectra of 190 liquid mixture samples and 158 powder mixture samples were collected by a handheld Raman spectrometer and these spectra were used to validate the identification performance of the proposed method. The proposed method could achieve good identification accuracy for different mixture samples. It shows that the proposed method is an effective way for the component identification in mixture by using a handheld Raman spectrometer.

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Section: Feature Parameter Extractionmentioning

confidence: 99%

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares

et al. 2022

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“…In recent years, our research group has developed a series of data analysis tools − for GC–MS data sets to retrieve mass spectra of components under TIC peaks, but none of them was designed for addressing various types of time shift problems . In this work, we developed a novel automatic data analysis strategy for GC–MS data analysis (AntDAS-GCMS) to perform a comprehensive workflow for untargeted metabolomics including TIC peak detection, TIC peak resolution, time shift correction, component registration, statistical and chemometric analysis, and compound identification.…”

Section: Introductionmentioning

confidence: 99%

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC–MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem

Ma,

Wang,

Wang

et al. 2024

Anal. Chem.

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Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography−mass spectrometry (GC−MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC−MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC−MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC−MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

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“…In consequence, it is imperative to form a comprehensive and authoritative data set in the data processing process. Data mining methods reported in recent years have emerged endlessly, mainly including the following categories: mass defect filter, extracted ion chromatogram, diagnostic product ion (DPI), neutral loss filtering (NLF), and isotope pattern filtering [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Screening and Identification of Phillyrin Metabolites in Rats Based on UHPLC-Q-Exactive Mass Spectrometry Combined with Multi-Channel Data Mining

Lou

et al. 2020

International Journal of Analytical Chemistry

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Phillyrin, a well-known bisepoxylignan, has been shown to have many critical pharmacological activities. In this study, a novel strategy for the extensive acquisition and use of data was established based on UHPLC-Q-Exactive mass spectrometry to analyze and identify the in vivo metabolites of phillyrin and to elucidate the in vivo metabolic pathways of phillyrin. Among them, the generation of data sets was mainly due to multichannel data mining methods, such as high extracted ion chromatogram (HEIC), diagnostic product ion (DPI), and neutral loss filtering (NLF). A total of 60 metabolites (including the prototype compound) were identified in positive and negative ion modes based on intuitive and useful data such as the standard’s cleavage rule, accurate molecular mass, and chromatographic retention time. The results showed that a series of biological reactions of phillyrin in vivo mainly included methylation, hydroxylation, hydrogenation, sulfonation, glucuronidation, demethylation, and dehydrogenation and their composite reactions. In summary, this study not only comprehensively explained the in vivo metabolism of phillyrin, but also proposed an effective strategy to quickly analyze and identify the metabolites of natural pharmaceutical ingredients in nature.

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Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry

Cited by 12 publications

References 41 publications

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC–MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem

Comprehensive Screening and Identification of Phillyrin Metabolites in Rats Based on UHPLC-Q-Exactive Mass Spectrometry Combined with Multi-Channel Data Mining

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