Development And Application Of LC-MS/MS Method For Phenolic Acids And Flavonoids In Maize

Phenolic acids and flavonoids,a class of secondary metabolites of plant-produced,play roles of antioxidant,anticancer,antitumor and so on to human health.Genetically modified(GM)crops have brought great contribution to agriculture and food industry,but a huge controversy accurs at the same time due to it's potential dangers to food safety and ecological environment.Metabolomics,a discipline of studing metabolic products of organism,has been successfully applied in food field.In this study,a LC-MS/MS method with high sensitivity,reproducibility and stability was developed for thirty-one phenolic acids and flavonoids may exist in maizes(Zea mays)including diosmetin,dihydrokaempferol,apigenin,luteolin,isorhamnetin,hesperetin,naringenin,rutin,quercetin,hyperoside,genistein,eriodictyol,phloretin,syringic acid,p-coumaric acid,4-hydroxybenzoic acid,sinapinic acid,benzoic acid,gentisic acid,trans-ferulic acid,vanilic acid,caffeic acid,gallic acid,procatechuic acid,coumaric acid,cinnamic acid,(-)-gallocatechin,3,4-dihydroxybenzeneacetic acid,homovanillic acid,catechin,L-epicatechin by adjusting liquid chromatogram and mass spectrum conditions.In addition,optimition of extraction and SPE conditions of these analytes in maizes was performed to make sure of high recoveries and good sensitivity,stability and reproducibility.30 GM and 14 non-GM maizes though the pretreatment were detected by LC-MS/MS to abtain their phenolic acids and flavonoids informations,on the basis of which metabolomics was applied then.Fold change and heat map from all maize species after data normalization demonstrated the variation range and the distribution.Multivariate statistical methods such as principal component analysis(PCA),partial least squares discriminant analysis(PLS-DA)and orthogonal partial least squares discriminant analysis(OPLS-DA)achieve the distinction between GM maize and non-GM maize groups.Different metabolites between the two groups founded in OPLS-DA model combining T-test verification were determined as statistically significant.