Abstract

Ginger has been used as consumed food spice and folk medicine in daily life for thousands of years in various regions of the world. Considerable antioxidation is one of the major activities for Ginger to exhibit health-promoting effects. In this study, a bioinformatic workflow was developed to generate activity labelled molecular networking (ALMN) to fuel the antioxidation active molecules profile of Ginger. In ALMN, antioxidation activity data, which was defined as correlation (r and p value) between the relative abundance of a molecule in fractions and the activity level of each fraction, was labelled to feature-based molecular network to profile out antioxidation active molecules visually. Fragmentation tree was further computed as a complementary way to conduct high confidence structure annotations of antioxidation active molecules. Consequently, 48 molecules were prioritized as antioxidation active molecules from 11,720 metabolite molecules of Ginger in a systematical way.

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