2022
DOI: 10.1016/j.foodchem.2021.131688
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Comparison analysis of widely-targeted metabolomics revealed the variation of potential astringent ingredients and their dynamic accumulation in the seed coats of both Carya cathayensis and Carya illinoinensis

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Cited by 37 publications
(22 citation statements)
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“…All of the quality control samples clustered together, indicating the satisfactory quality of the data. Recently, untargeted metabolomics has been employed to investigate the molecular mechanisms behind scab and salt resistance in pecans. , In addition, a widely targeted metabolomics analysis was employed to reveal the difference in astringent components in the seed coats of pecans and C. cathayensis . However, the overall changes in the metabolites during pecan kernel development have not been fully elucidated.…”
Section: Resultsmentioning
confidence: 99%
“…All of the quality control samples clustered together, indicating the satisfactory quality of the data. Recently, untargeted metabolomics has been employed to investigate the molecular mechanisms behind scab and salt resistance in pecans. , In addition, a widely targeted metabolomics analysis was employed to reveal the difference in astringent components in the seed coats of pecans and C. cathayensis . However, the overall changes in the metabolites during pecan kernel development have not been fully elucidated.…”
Section: Resultsmentioning
confidence: 99%
“…The tannins, especially hydrolyzable tannins (HTs), constitute a major class of plant secondary metabolites, including simple gallic acid derivatives, gallotannins, and ellagitannins [ 37 ]. Tannins, especially hydrolyzable tannins, have attracted attention in recent decades because of their antioxidant potential and biological activity [ 38 ]. Many ellagitannins and hydrolyzable tannins, including ellagic acid, gallic acid, methyl gallate, ethyl gallate, chebulinic acid, corilagin, penta galloyl glucose, tetra galloyl beta-D-glucose, and casuarinin from the dichloromethane fraction of T .…”
Section: Discussionmentioning
confidence: 99%
“…The statistical function prcomp function in R (version 3.6.3) was used to perform unsupervised principal component analysis (PCA). The data were unit variance scaled [24] (i.e., z-score normalization) before unsupervised PCA by setting the prcomp function parameter scale = True. The hierarchical cluster analysis was carried out by R package Com-plexHeatmap.…”
Section: Lipid Extraction and Widely Targeted Lipidomics Analysismentioning
confidence: 99%