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IVES 9 IVES Conference Series 9 OENO IVAS 9 OENO IVAS 2019 9 Analytical developments from grape to wine, spirits : omics, chemometrics approaches… 9 Integrated multiblock data analysis for improved understanding of grape maturity and vineyard site contributions to wine composition and sensory domains

Integrated multiblock data analysis for improved understanding of grape maturity and vineyard site contributions to wine composition and sensory domains


Much research has sought to define the complex contribution of terroir (varieties x site x cultural practices) on wine composition. This investigation applied recent advances in chemometrics to determine relative contributions of vine growth, berry maturity and site mesoclimate to wine composition and sensory profiles of Shiraz and Cabernet Sauvignon for two vintages. 

Grape maturation was monitored using a berry sugar accumulation model and wines made from sequentially harvested grapes at three stages for each variety and vintage. Comprehensive targeted grape analysis of amino acids, carotenoids, sugars, organic acids, anthocyanins and volatile compounds were combined with targeted wine volatile and non-volatile chemical measures of composition and sensory descriptive analysis. Chemometric models of balanced sample sets derived from the pool samples were used in an ANOVA multiblock framework with orthogonal projection to latent structures (Boccard and Rudaz, 2016) to elucidate the relative importance of model design factors. 

Multiple data matrices derived from the experimental design factors are subtracted from the original data matrix to obtain pure effects and interaction submatrices with structured orthogonal data. A response matrix is derived from the positive eigenvalues associated with SVD of each effect matrix and residuals are then added to each submatrix prior to kernel OPLS. Model performance evaluated from residual structure ratio (RSR), goodness of fit (R2Y) and permutation testing identified the significant factors from each model. Projection of sample scores of significant factors against scores of the residual matrix is used to assess sample clusters with confidence intervals based on Hotelling T2. 

Loadings from significant experimental factors of each model were used for hierarchical cluster analysis (HCA) with Euclidean distance measures and Wards grouping criteria. Prior to HCA scores and loadings are rotated to consistent presentation of factor levels in model plots. A conservative interpretation of loadings heat maps was considered appropriate and a summary heat map for explanatory factors is presented that enable interpretation of the impact of cultivar, site (soil x mesoclimate), grape maturity and region on grape and wine composition. The integrated data driven approach used in this investigation may be of assistance for other investigators for omics based experiments.

Ref: Boccard, J. & Rudaz, S. 2016. Anal Chim Acta. 920:18-28.


Publication date: June 19, 2020

Issue: OENO IVAS 2019

Type: Article


Leigh Schmidtke, Guillaume Antalick, Katja Suklje, John Blackman, Alain Deloire

National Wine and Grape Industry Centre, Charles Sturt University, Locked Bag 588 – Wagga Wagga – New South Wales 2678 – AUSTRALIA
Wine Research Centre, University of Nova Gorica, Vipavska, 5000 Nova Gorica, Slovenia
Agricultrual Institute of Solvenia, Lubljana, 1000, Slovenia
Montpellier SupAgro, Montpellier 34060,

Contact the author


AMOPLS, sequential harvest, berry sugar accumulation, targeted metabolomics 


IVES Conference Series | OENO IVAS 2019


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