2019
DOI: 10.1039/c9ra05032a
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A proposed protocol based on integrative metabonomics analysis for the rapid detection and mechanistic understanding of sulfur fumigation of Chinese herbal medicines

Abstract: In the current work, Lonicera japonica Flos (FLJ) was selected as a model Chinese herbal medicine (CHM) and a protocol was proposed for the rapid detection of sulfur-fumigated (SF) CHMs.

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Cited by 2 publications
(1 citation statement)
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“…Moreover, there are currently neither reports on the formation of new sulfur-fumigation markers during the sulfur fumigation process of TR nor reports on the regulation of the chemical conversion of different levels of sulfur fumigation. At present, non-targeted metabolomics and targeted metabolomics based on LC-QTOF-MS or LC-QTRAP-MS have become more mature in the analysis of the chemical composition of Chinese herbal medicines and the mining of quality control indicators ( Ma et al., 2014 ; Li et al., 2016 ; Dai et al., 2018 ; Wei et al., 2018 ; Zhang et al., 2018 ; Shengyun et al., 2019 ; Jiang et al., 2020 ). Specifically, broadly targeted metabolomics is a detection technology that integrates the “extensiveness” of non-targeted metabolomics with the “accuracy” of targeted metabolomics ( Luo et al., 2018 ; Zhu et al., 2018 ).…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, there are currently neither reports on the formation of new sulfur-fumigation markers during the sulfur fumigation process of TR nor reports on the regulation of the chemical conversion of different levels of sulfur fumigation. At present, non-targeted metabolomics and targeted metabolomics based on LC-QTOF-MS or LC-QTRAP-MS have become more mature in the analysis of the chemical composition of Chinese herbal medicines and the mining of quality control indicators ( Ma et al., 2014 ; Li et al., 2016 ; Dai et al., 2018 ; Wei et al., 2018 ; Zhang et al., 2018 ; Shengyun et al., 2019 ; Jiang et al., 2020 ). Specifically, broadly targeted metabolomics is a detection technology that integrates the “extensiveness” of non-targeted metabolomics with the “accuracy” of targeted metabolomics ( Luo et al., 2018 ; Zhu et al., 2018 ).…”
Section: Introductionmentioning
confidence: 99%