Quantitative metabolomics unveils the impact of agricultural practices in grape metabolome.

Abstract

Abstract Integration of multiple data set in agricultural and food practices is critical for decision-making, particularly if affecting product qualitative characteristics that influence producers and consumers decisions on production strategies and purchases. Herein, we apply a multidimensional data analysis to evaluate 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 MANOVA reveals that ELR enhanced the concentration of several secondary metabolites, without compromising other berry attributes 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.