Global aridity changes affect wheat quality and stress induced metabolites: a pioneering study by multiblock PLS-R analysis

A wealth of studies suggested that, as a result of climate changes, global aridity will increase over the 21st century due to the thermodynamic response of a warming atmosphere. A way to predict the effect of climate changes is by using aridity indices that, taking into account thermo-pluviometric data, enable to classify the climate from the driest to the most humid ones. This study aimed to unravel the effect of aridity as described by two indices (De Martonne, and Pinna) on wheat quality and stress induced metabolites. Six common and durum wheat varieties were cultivated in four different locations over three years and climate data were monitored by several weather stations, eventually climate data were used to calculate aridity indices in all the fields. Wheat kernels were analysed at harvest for quality parameters, stress induced compounds (polyphenols, tocopherols and total antioxidant activity as well as mycotoxins) and volatile organic compounds (VOCs). Data were divided in two blocks and analysed together by using an edge-cutting multi-block approach, both in discrimination and regression. The two aridity indices showed significantly different performances. Discrimination permitted to correctly classify samples with an 89.9% considering De Martonne index and several compounds, among which terpenes, were found characterizing a specific climate. Conversely, regression selected only strictly linear-related compounds with aridity among which antioxidants, mycotoxins and several VOCs mainly related to oxidation pathways. Results show the potential of multi-block analysis to link wheat quality and metabolic responses, and to predict changes in wheat quality related to aridity changes. • Quality parameters, stress induced compounds and volatiles were analysed on wheat • Analytical results depended on the aridity of the fields where wheat was cultivated • An integrative multi-block approach was successfully applied to predict aridity • Discriminant analysis selected several terpenes characterizing specific climates • Aridity showed linear relation with antioxidants, mycotoxins and several VOCs