Metabolomics (2020) 16:52 https://doi.org/10.1007/s11306-020-01673-3 ORIGINAL ARTICLE Cultivar, site or harvest date: the gordian knot of wine terroir L. M. Schmidtke1 · G. Antalick1,2 · K. Šuklje1,3 · J. W. Blackman1 · J. Boccard4 · A. Deloire1,5 Received: 17 July 2019 / Accepted: 30 March 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Introduction The complex interactions of vine cultivars, and localised regional climate associated with specifc vineyard sites are important attributes to the concept of terroir and signifcant contributors to grape maturity and wine sensory profles. An improved understanding of the infuence of each factor and their interactions is a challenging conundrum, and will enable more efcient production targeting specifc wine styles. Objectives To characterise the metabolic fux of grape berries and resulting wines to characterise the relative impact of site specifc climate, cultivar, and grape maturity based upon berry sugar accumulation models that consistently target specifc wine styles. Methods A spatial and temporal study of grape and wine composition was undertaken for two important cultivars in two distinct regions of New South Wales. Measures of composition and wine sensory ratings were simultaneously analysed using a multiblock algorithm taking advantage of the ANOVA framework to identify important contributions to wine style arising from grape maturity, vineyard site and cultivar. Results A consistent fux of grape and wine constituents is evident for wine made from sequentially harvested grapes from the same vineyard with increasing levels of grape maturity. Contributions of region and vineyard site to wine style could also be elucidated. Diferences in metabolite fux in grapes and resulting wines between cultivars growing in similar condi- tions are evident. Conclusions The combination of a metabolomics and multiblock data decomposition approach may be successfully used to profle and elucidate the contribution of abiotic factors to grape and wine composition and provide improved understanding of the terroir concept. Keywords Viticulture · Oenology · AMOPLS · Grapes · Climate · Shiraz · Cabernet sauvignon Electronic supplementary material The online version of this 1 Introduction article (https ://doi.org/10.1007/s1130 6-020-01673 -3) contains supplementary material, which is available to authorized users. The association of agricultural products to a provenance or * L. M. Schmidtke specifc region (terroir) that imparts typical sensorial quali- [email protected] ties is an important concept for providores of high value products. Increasing consumer demand and interest for foods 1 School of Agricultural and Wine Sciences, National Wine and wines associated with, and which identify with specifc and Grape Industry Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW 2678, Australia regions and places of production, are important economic and marketing factors for product diferentiation and uniqueness 2 Present Address: Wine Research Centre, Univerza v Novi Gorici, Vipavska 13, 5000 Nova Gorica, Slovenia (Charters et al. 2017). The concept of a terroir is complex and is broadly considered in terms of the interactions between 3 Present Address: Department of Fruit Growing, Viticulture and Oenology, Agricultural Institute of Slovenia, Hacquetova the environment, climate and the social interplay of people, 17, 1000 Ljubljana, Slovenia their history and traditional approaches to food production 4 Institute of Pharmaceutical Sciences of Western Switzerland, (Seguin 1986). In the wine and food industries several def- University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, nitions of terroir exist and all acknowledge the importance Switzerland of a local climate upon the composition of grapes, resulting 5 Present Address: L’Institut Agro (SupAgro), 2 Place P. Viala, wines or foods (Gladstones 2011). Viticultural management, 34060 Montpellier, France Vol.:(0123456789)1 3 52 Page 2 of 17 L. M. Schmidtke et al. including varying grape maturity at harvest, and wine mak- 2 Data analysis using analysis of variance ing practises will also signifcantly alter the fnal wine styles multiblock orthogonal partial least (Matthews 2015). Wine style depends principally on grape squares (AMOPLS) composition which is largely determined by abiotic factors and vineyard’s environmental characteristics. Diferences in Multiple analytical techniques are often used to deter- grape composition pertaining to site arise from vines adapt- mine sample composition resulting in contemporaneous ing to diferent biotic and abiotic environments (Tonietto and data sets that characterise specifc attributes. For example Carbonneau 2004) which in turn infuence vine physiology GCMS and LCMS, or other analytical techniques, may and ultimately berry composition (Deloire et al. 2008). Sin- be used to measure the volatile and non-volatile compo- gle markers that defne wine style, terroir or regionality are sition of grape and wine samples made when vines are simplistic and identifcation of an array of markers is required exposed to several treatments (Šuklje et al. 2016). Classic to determine objective and predictive wine sensory features experimental designs that make use of Analysis of Vari- (Schmidtke et al. 2013). Determining the impact of specifc ance (ANOVA) partitioning of data variation, enable the drivers of terroir is a challenging analytical task requiring combined efect of several experimental factors (EF) upon robust experimental design, careful site selection, longi- sample composition to be determined. In the present study, tudinal studies and application of sophisticated data driven an ANOVA multiblock orthogonal partial least squares algorithms to tease out vineyard, vintage and abiotic factors (AMOPLS) approach, first described by Boccard and associated with wine style and typicality. Untargeted metabo- Rudaz (2016) was used to analyse multiple data sets per- lomic approaches characterising the volatile or non-volatile taining to the composition of grapes, wines and their sen- signatures of grapes (Anesi et al. 2015; Cramer et al. 2014); sory features to determine the impact of factors associated and wine components (Roullier-Gall et al. 2014) have sug- to the concept of terroir. A general outline of the AMO- gested some measures of terroir. However, very few studies PLS approach is shown in Supplementary Fig. 1 represent- have characterised the relative contributions and importance ing an experiment with n samples and several data blocks of aspects of terroir being site, cultivar, mesoclimate and grape with ki variables. All data must be collated and organised maturity to wine style. Mesoclimate is especially interesting such that sample order in each data block is identical. in the context of terroir and refers to the scale of climate infu- The frst part of the AMOPLS approach (Supplementary enced by local geographies and landscapes within hundreds of Fig. 1, panel A (1)) consists of block scaling and concat- metres to several kilometres according to altitude and topog- enation. Each centred data block is normalized for vari- raphy, and is therefore an important consideration to vineyard ance to ensure equal contributions to the overall model. ×Σ site selection (Carbonneau et al. 2015). In this investigation Once each data block is scaled, a data superblock ( n ki ) we report on a designed experiment, with targeted measures is created by horizontal concatenation of all data blocks of grape and wine composition, to characterise the infuence (Supplementary Fig. 1, panel A (2)). The second part of of multiple vineyard sites in two distinct wine regions of Aus- the AMOPLS data decomposition (Supplementary Fig. 2, tralia (Orange and Grifth). This allowed the investigation of panel B (3)) is to partition sources of variation according diferent growing conditions, and grape maturity at harvest, on to an ANOVA model of explanatory factors which creates ×Σ grape and resulting wine composition and sensory domains for a series of equally sized data tables ( n ki ) by computing two important cultivars (Vitis Vinifera cv. Shiraz and Cabernet the mean values of all measurements for each level of the Sauvignon). One region is warm (Grifth), characterised by a explanatory factor. Once an explanatory factor data table fat topography with little variation between sites and mesocli- has been determined, it is subtracted from the experimen- mate, whereas the second region (Orange) is characterised by tal matrix prior to computation of the next explanatory variable vineyard elevation enabling inferences on the infu- factor table, thus each table is additive and orthogonal and ence of climate associated with cooler growing conditions. represents the experimental design structure summaris- Using a data-driven multiblock approach within a multivariate ing the explanatory factor main efects and interactions Analysis of Variance framework that exploits balanced experi- according to the general linear model equation: mental designs, the infuences of grape maturity, cultivar and X = X + X + X + X + X mesoclimate at vineyard site on the resulting wine chemical res (1) and sensory domains is demonstrated. where X is the overall mean value for each variable, X , X and X contain the mean values for each level of the explanatory factors , and thier interaction , and residu- als represented by Xres respectively. The residual data table therefore contains the measured variable responses that are