Additional file 1 of Integrating metabolomics and machine learning to forecast anti-inflammatory and antioxidant activities in D. officinale leaves

Supplementary material 1. Table S1 Samples information of D. officinale leaves. Table S2 Precursor/Product ion pairs and parameters for MRM of compounds used in this study. Table S3 Sequence of qPCR primers for RNA detection. Table S4 Identification list of secondary metabolites of D. officinale. Table S5 Relative content of secondary metabolites in leaves of D. officinale (n=2). Table S6 Linear regression data, precision, and repeatability for 17 amino acids. Table S7 The content of 17 kinds of amino acids in 60 batches of D. officinale leaves (mg·g-1, n=2). Table S8 Vitamin content of 60 batches of D. officinale leaves (μg·g-1, n=2). Figure S1 Amino acid determination chromatogram (1: Asp; 2: Ser; 3: Glu; 4: Gly; 5: His; 6: Arg; 7: Thr; 8: Ala; 9: Pro; 10: Cys; 11: Tyr; 12: Val; 13: Met; 14: Lys; 15: Ile; 16: Leu; 17: Phe). Figure S2 In vitro validation results of anti-inflammatory activity. Figure S3 Effect of variety 630 on the activity of HEK-293T-NFkB cells. Figure S4 Effect of variety Y2 on the activity of HEK-293T-NFkB cells. Figure S5 Effect of variety Y3 on the activity of HEK-293T-NFkB cells. Figure S6 Effect of variety B1 on the activity of HEK-293T-NFkB cells. Figure S7 Effect of variety B3 on the activity of HEK-293T-NFkB cells