Food Metabolomics Using GC-FID

Introduction

Samples derived from organisms, including food, contain a great number of components. Research and development on comprehensively analyzing them to clarify the effects on samples, which are produced by differences among lots and manufacturing methods, and predicting the characteristics of unknown samples from their component patterns, has been actively pursued in recent years with the focus on the food field. This is commonly referred to as metabolomics, and is one of the most in-demand analytical methods, in conjunction with analysis software such as multivariate analysis and machine learning. Mass spectrometers such as LC-MS and GC-MS have been indispensable tools for metabolomics. Mass spectrometers have excellent qualitative analysis performance and are extremely powerful in identifying detected components. On the other hand, they are characterized by a susceptibility to accumulation of contamination in the instrument, along with large variation in the quality of data before and after updating the instrument’s tuning information, so they are not suited to obtaining a large amount of data over a long period of time. With GC-MS in particular, the common practice is to measure metabolites after their trimethylsilylation (TMS derivatization), but in many cases the intensity of the peaks of the TMS derivatized compounds decreases over time after derivatization, so in order to obtain good measurement results it can be important to complete the analysis as soon as possible after derivatization. In this paper, we examine whether metabolomics using GC- MS in this way can be accomplished using GC-FID as an alternative, and we found that a number of peaks comparable to those with GC-MS could be detected, and that the intensities of these peaks were more stable than with GC-MS over both hours and days. This stability of GC-FID could make it a very effective tool for acquiring large amounts of data over a long period of time in metabolomics.

October 30, 2019 GMT