For example, employed GC-MS and LC-MS to study the metabolic responses of Arabidopsis thaliana to the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. The authors identified several metabolites, including amino acids, organic acids, and secondary metabolites, that were differentially accumulated in resistant and susceptible Arabidopsis accessions and contributed to the plant's defense mechanisms against the pathogen. This study has been widely cited and has provided valuable insights into the metabolic basis of plant-pathogen interactions in the model plant Arabidopsis . Another study by used GC-MS to analyze the metabolic profiles of maize lines with different levels of resistance to the fungal pathogen Colletotrichum graminicola . The authors identified several metabolites, such as benzoxazinoids and phenylpropanoids, that were associated with resistance and could be used as targets for marker-assisted selection in maize breeding programs. Similarly, a study by Cuperlovic-Culf et al. (2016) used NMR spectroscopy to compare the metabolic profiles of wheat genotypes with different levels of resistance to Fusarium head blight . The authors identified several metabolites, such as choline and betaine, that were associated with resistance and could be used as biomarkers for screening wheat germplasm in breeding programs.