Development and application of a data processing method for food metabolomics analysis

Abstract

Abstract Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although while the technology exists to analyze this data and different tools exist in various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualizations. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow were included in this method. As compared with conventional approaches, combining the result of MS1&MS2 spectra-based identification workflow via the tolerance of retention time and mass to charge ratio (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while keeping 90% of the correct identification. The result demonstrated that the developed method is rapid and reliable method for food metabolomics data processing.