2022
DOI: 10.3390/metabo12020106
|View full text |Cite
|
Sign up to set email alerts
|

Untargeted Metabolomics Coupled with Chemometrics for Leaves and Stem Barks of Dioecious Morus alba L.

Abstract: The differences in metabolites in male and female individuals of dioecious Morus alba L. (Moraceae) are usually ignored and lack study. In the present study, 58 leaves and 61 stem barks from male and female individuals were analyzed by untargeted metabolomics via UPLC-Q-TOF-MS coupled with chemometrics, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). A total of 66 and 44 metabolites were identified from leaves and stem barks, respectively. Four… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
5
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8

Relationship

1
7

Authors

Journals

citations
Cited by 11 publications
(6 citation statements)
references
References 71 publications
0
5
0
Order By: Relevance
“… 18 It has been reported that the untargeted metabolomics has been applied to analyze the change rules of metabolites in different stages, tissues, and processing modes of different plants. 19–21 These studies proved that metabolomics analysis could be an authentic tool for comparing active compounds from different organs of U. parvifolia .…”
Section: Introductionmentioning
confidence: 86%
“… 18 It has been reported that the untargeted metabolomics has been applied to analyze the change rules of metabolites in different stages, tissues, and processing modes of different plants. 19–21 These studies proved that metabolomics analysis could be an authentic tool for comparing active compounds from different organs of U. parvifolia .…”
Section: Introductionmentioning
confidence: 86%
“…PCA is an unsupervised recognition pattern, which transforms the original variable into a new set of independent variables by reducing the dimension of the data matrix. This independent variable is called principal components (PCs) [32] . All the analyses were performed in triplicate.…”
Section: Methodsmentioning
confidence: 99%
“…Subsequently, the algorithm of ANOVA p value and max fold change were used to filter. The specific filtration was as described by Wu et al [ 39 ]. As for the identification of compounds, it was carried out based on their mass spectral data using the Metlin database, relevant published literature, and mixed standard solution based on their retention times and fragmentation patterns.…”
Section: Methodsmentioning
confidence: 99%