2019
DOI: 10.1016/j.chroma.2019.04.065
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Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry

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Cited by 12 publications
(3 citation statements)
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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%
“…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%
“…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%
“…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%