2022
DOI: 10.1016/j.microc.2021.106919
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Application of UHPLC-Q-TOF MS based untargeted metabolomics reveals variation and correlation amongst different tissues of Eucommia ulmoides Oliver

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Cited by 25 publications
(6 citation statements)
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“…Correlation analysis is a powerful tool for characterising the metabolic proximities among metabolites having significant differences 29 . Hence, a total of 159 differential metabolites in O. gracilis cultures were analysed by generating a correlation matrix using Pearson's correlation in order to find the mutual regulatory relationship among metabolites in the process of O. gracilis in different culture conditions.…”
Section: Resultsmentioning
confidence: 99%
“…Correlation analysis is a powerful tool for characterising the metabolic proximities among metabolites having significant differences 29 . Hence, a total of 159 differential metabolites in O. gracilis cultures were analysed by generating a correlation matrix using Pearson's correlation in order to find the mutual regulatory relationship among metabolites in the process of O. gracilis in different culture conditions.…”
Section: Resultsmentioning
confidence: 99%
“…Related studies had been conducted in diverse tea populations ( Yu et al., 2020 ). Chemical components in different tissues of E.ulmoides including leaves, seeds and barks had been determined by ultra-high-performance liquid chromatography-tandem time-of-flight mass spectrometer (UHPLC-QTOF/MS) untargeted metabolomics, and finally 2,373 metabolites were identified in total ( Chen et al., 2022 ). Besides, the dynamic metabolic models for leaf growth and development of E.ulmoides were constructed by integrated uses of widely targeted metabolomics and transcriptomics ( Li et al., 2019 ).…”
Section: Discussionmentioning
confidence: 99%
“…Partial least squares discriminant analysis is a supervised model analysis method based on a latent variable regression approach of the variance between predictor and response variables that has been shown to be effective in handling datasets with multicollinearity predictor variables (Chen et al, 2022). The samples are also clearly divided into four groups in the PLS-DA score plot, indicating that the method is accurate and reliable (Figure 4c,d).…”
Section: Partial Least Squares Discriminant Analysismentioning
confidence: 99%