Quantitative Metabolomics Unveils the Impact of Agricultural Practices in the Grape Metabolome

Integration of multiple data sets in agricultural and food practices is critical for decision making, particularly if it affects product qualitative characteristics that influence decisions of producers and consumers about production strategies and purchases. Herein, we apply multidimensional data analysis to evaluate the grape chemical composition obtained via high-resolution metabolomics and vine growth characteristics following the application of early leaf removal (ELR), a canopy management technique implemented in cool climate viticulture. The application of discriminant analysis using a supervised PLS (sPLS-DA) algorithm along with multivariate analysis of variance reveals that ELR enhanced the concentration of several secondary metabolites, without compromising other berry attributes that are pivotal for the vinification process, such as sugar content. Overall, this study paves the way for highly effective integrated strategies (metabolomics and agricultural practices) that link laboratory analysis with vineyard management decisions.