| Autor: |
Martins MCM; Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil., Caldana C; Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil. caldana@mpimp-golm.mpg.de.; Max-Planck Partner Group, Brazilian Bioethanol Science and Technology Laboratory (CTBE/CNPEM), Campinas, Brazil. caldana@mpimp-golm.mpg.de.; Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. caldana@mpimp-golm.mpg.de., Wolf LD; Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil., de Abreu LGF; Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil. |
| Abstrakt: |
The output of metabolomics relies to a great extent upon the methods and instrumentation to identify, quantify, and access spatial information on as many metabolites as possible. However, the most modern machines and sophisticated tools for data analysis cannot compensate for inappropriate harvesting and/or sample preparation procedures that modify metabolic composition and can lead to erroneous interpretation of results. In addition, plant metabolism has a remarkable degree of complexity, and the number of identified compounds easily surpasses the number of samples in metabolomics analyses, increasing false discovery risk. These aspects pose a large challenge when carrying out plant metabolomics experiments. In this chapter, we address the importance of a proper experimental design taking into consideration preventable complications and unavoidable factors to achieve success in metabolomics analysis. We also focus on quality control and standardized procedures during the metabolomics workflow. |
