Data from: Nutrient composition and functional constituents of daylily from different producing areas based on widely targeted metabolomics
Data files
Mar 24, 2024 version files 770.59 KB
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README.md
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Table_S4.xlsx
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Table_S5.xlsx
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Table_S6.xlsx
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Table_S7.xlsx
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Table_S8.xlsx
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Table_S9.xlsx
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Abstract
Daylily is a functional food with high nutritional value in China. Datong (DT) in Shanxi Province is one of the four main production areas of daylily. Therefore, Linfen (LF), Lvliang (LL), and Yangquan (YQ) in Shanxi Province have also introduced daylily from DT. However, geographical and climatic conditions and producing patterns cause variations in the vegetable quality. In the present study, we determined the quality of daylilies from different producing areas and found that the nutrient composition of daylilies from different producing areas varied. The widely targeted metabolomics was performed, and the results showed that 1642 metabolites were found in daylily. The differential metabolites between DT and YQ, LL and LF were 557, 667, and 359, respectively. Notably, 9 metabolic pathways and 76 metabolite markers could be associated with daylily from different areas. This study provides a theoretical basis for the quality maintenance and health efficacy research of daylily.
(https://doi.org/10.5061/dryad.dbrv15f77)
Table S4: Information of metabolites identified in daylily.
Table S5: Information of metabolites identified in daylily of all three comparison groups.
Table S6. Information of metabolites identified in daylily from DT and YQ.
Table S7. Information of metabolites identified in daylily from DT and LL.
Table S8. Information of metabolites identified in daylily from DT and LF.
Table S9. The KEGG pathways responding to the differential metabolites among all three comparison groups.
The file “Table S4.csv” includes the variables:
Index-The ID of the substance in the Metware database
Q1 (Da)-the molecular weight of the parent ion after the material is added with ions through the electric spray ion source
Q3 (Da)-characteristic fragment ions
Molecular weight (Da)-relative molecular weight
Formula-Material molecular formula
Ionization model-Ionization mode (M+H is positively charged, M-H is negatively charged)
Compounds-Names of substances
Class I-First level category of substances
Class II-Secondary classification of substances
DT-1,2,3-Samples in DT
YQ-1,2,3-Samples in YQ
LL-1,2,3-Samples in LL
LF-1,2,3-Samples in LF
The file “Table S5.csv” includes the variables:
Index-The ID of the substance in the Metware database
Compounds-Names of substances
Class I-First level category of substances
Class II-Secondary classification of substances
DT-1,2,3-Samples in DT
YQ-1,2,3-Samples in YQ
LL-1,2,3-Samples in LL
LF-1,2,3-Samples in LF
The file “Table S6.csv” includes the variables:
Index-The ID of the substance in the Metware database
Formula-Material molecular formula
Compounds-Names of substances
Class I-First level category of substances
Class II-Secondary classification of substances
DT-1,2,3-Samples in DT
YQ-1,2,3-Samples in YQ
VIP-projection of variable importance
P-value-significance test P-value
Fold_Change-multiple of differences
Log2FC-The logarithm of the difference multiple is taken as the base of 2
Type-Metabolite upregulation and downregulation type.
The file “Table S7.csv” includes the variables:
Index-The ID of the substance in the Metware database
Formula-Material molecular formula
Compounds-Names of substances
Class I-First level category of substances
Class II-Secondary classification of substances
DT-1,2,3-Samples in DT
LL-1,2,3-Samples in LL
VIP-projection of variable importance
P-value-significance test P-value
Fold_Change-multiple of differences
Log2FC-The logarithm of the difference multiple is taken as the base of 2
Type-Metabolite upregulation and downregulation type.
The file “Table S8.csv” includes the variables:
Index-The ID of the substance in the Metware database
Formula-Material molecular formula
Compounds-Names of substances
Class I-First level category of substances
Class II-Secondary classification of substances
DT-1,2,3-Samples in DT
LF-1,2,3-Samples in LF
VIP-projection of variable importance
P-value-significance test P-value
Fold_Change-multiple of differences
Log2FC-The logarithm of the difference multiple is taken as the base of 2
Type-Metabolite upregulation and downregulation type.
The file “Table S9.csv” includes the variables:
Kegg_pathway-The name of the pathway
KoID-The ko number of the pathway in the KEGG database
Cluster_frequency-The quantity and proportion of differential metabolites annotated in this entry compared to those annotated in the KEGG pathway
Metabolome_frequency-The quantity and ratio of differential metabolites annotated to this entry to differential metabolites annotated to all KEGG entries
P-value-Significance of the pathway
The dataset included metabolite information from four regions in Shanxi, Datong, Luliang, Linfen and Yangquan, and was analyzed using widely targeted metabolomices.