Recent methodological advances in both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) have facilitated the profiling highly complex mixtures of primary and secondary metabolites in order to investigate a diverse range of biological questions. These techniques usually face a large number of potential sources of technical and biological variation. In this chapter we describe guidelines and normalization procedures to reduce the analytical variation, which are essential for the high-throughput evaluation of metabolic variance used in broad genetic populations which commonly entail the evaluation of hundreds or thousands of samples. This chapter specifically deals with handling of large-scale plant samples for metabolomics analysis of quantitative trait loci (mQTL) in order to reduce analytical error as well as batch-to-batch variation.
Keywords: Batch normalization; GC-MS; LC-MS; Large-scale metabolomics; Natural genetic variation; QTL mapping; Variation.