Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Food Composition and Methods Development Laboratory » Research » Publications at this Location » Publication #326917

Research Project: Metabolite Profiling and Chemical Fingerprinting Methods for Characterization of Foods, Botanical Supplements, and Biological Materials

Location: Food Composition and Methods Development Laboratory

Title: GLS-Finder: An Automated Data-Mining System for Fast Profiling Glucosinolates and its Application in Brassica Vegetables

Author
item Sun, Jianghao
item Zhang, Mengliang - Ohio University
item Chen, Pei

Submitted to: Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2016
Publication Date: 5/18/2016
Citation: Sun, J., Zhang, M., Chen, P. 2016. GLS-Finder: An Automated Data-Mining System for Fast Profiling Glucosinolates and its Application in Brassica Vegetables. Analytical Chemistry. 4407:4415.

Interpretive Summary: Glucosinolates are important to human health and are believed to have anti-cancer capabilities. However, profiling glucosinolates in vegetables is quite difficult. In this study, a rapid computer-aided program for identifying glucosinolates, “GLS-Finder", was developed. It is capable of qualitative and semi-quantitative analysis of glucosinolates in samples using data generated by ultra-high performance liquid chromatography high accuracy mass spectrometry with multi-stage mass fragmentation (UHPLC-HRAM/MSn). GLS-Finder was successfully applied to identify glucosinolates in 49 commonly consumed brassica vegetable samples in the United States.

Technical Abstract: A rapid computer-aided program for profiling glucosinolates, “GLS-Finder", was developed. GLS-Finder is a Matlab script based expert system that is capable for qualitative and semi-quantitative analysis of glucosinolates in samples using data generated by ultra-high performance liquid chromatography high accuracy mass spectrometry with multi-stage mass fragmentation (UHPLC-HRAM/MSn). The script can also be used for targeted metabolomic studies. A suite of bioinformatics tools were integrated into the "GLS-Finder" to perform raw data deconvolution, peak alignment, glucosinolate putative assignments, semi-quantitation, and unsupervised principal component analysis (PCA). GLS-Finder was successfully applied to identify glucosinolates in 49 commonly consumed brassica vegetable samples in the United States.