Tracking Wheat Variety and Origin by the Shape Analysis of the Volatiles Fingerprint of Wheat Kernels and Wheat Beers

Influence of wheat content and origin on the volatilome of craft wheat beer: An investigation by combined multivariate statistical approaches

A deeper knowledge of the effect of wheat origin on the volatile organic compounds (VOCs) profile of craft wheat beer is crucial for its quality improvement and local product valorisation. The VOCs profile of 17 craft wheat beers obtained by common and durum, heritage and modern, wheat varieties grown in different fields sited at different altitudes was analysed. Data were processed by multivariate analysis using different approaches. Partial least square (PLS) analysis evidenced that wheat concentration was the highest source of VOCs variance, followed by, wheat species, wheat ancientness, and altitude of cultivation. An insight into the effect of wheat concentration was given by sparse PLS analysis (sPLS). The effect of wheat variety was explored by linear discriminant analysis (LDA), which permitted to correctly classify craft beers made with wheat of different origin (species and variety) on the basis of their VOCs profile. sPLS regression analysis permitted to find a combination of VOCs able to predict the altitude of wheat cultivation as well as to correctly classify wheat beers made with wheat cultivated at different altitudes. A further 'one versus all' approach by Soft Independent Modelling of Class Analogies (SIMCA) permitted to correctly authenticate beers made with different cereal species. Finally, shape analysis by generalized Procrustes analysis (GPA) revealed that the differences among samples were conserved and reflected from wheat kernels to wheat beers. This study suggests a promising use of volatiles fingerprinting with a combination of different statistical approaches to authenticate beer made with wheat of different origin and cultivated at different altitudes, thus stressing out the importance of territory in craft beer production, which, until now, was a neglected topic.

An integrative multi-omics approach aimed to gain insight on the effect of composition, style, yeast, and wheat species on wheat craft beer flavour

This study was aimed to unravel the effect of raw materials (barley and wheat), wheat concentration (0, 25, 40, and 100 %), wheat species (common and durum), beer style (Blanche and Weiss), and yeast (US-05 and WB-06) on the chemical composition, volatiles, and sensory profile of wheat craft beers by using a multivariate statistical approach. Beer samples were analysed for their composition, volatiles and sensory profile and data were processed using unsupervised multivariate analyses, PLS regression and a multi-omics approach using multi-block PLS-DA. Multi-block variable sparsification was used as an embedded dimension reduction step. The adopted multi-omics approach permitted to correctly classify beers with different styles and wheat concentration, and to accurate classify (95 % accuracy) beers according to yeast type. Wheat species was of lower importance since it permitted a classification with 49 % accuracy which increased to 74 % in Blanche beers, thus suggesting that malting flattened differences determined by wheat species.

A unifying approach to wheat beer flavour by chemometric analyses. Could we speak of 'terroir'?

Raw materials are recognized to affect the sensory profile of 'Blanche' craft beers and their 'terroir'. Two common wheat (Triticum aestivum, L.) were harvested in three experimental fields with different pedo-climatic conditions and altitudes, and then used for beer production. The taste and flavour of wheat beers were analysed by sensory (panel and consumer test) and SPME GC-MS analyses. Panel dataset was processed by multivariate statistical analyses: a principal component analysis (PCA) revealed that formulation was the main source of variation of sensory profile in wheat beers and a generalized Procrustes analysis (GPA) showed how wheat origin affected the sensory profiles of wheat craft beers based on the consensus among panelists. Moreover, a partial least-squares discriminant analysis (PLS-DA) on VOCs permitted to discriminate and characterize beers selected by a panel-driven approach. By comparing panel and VOCs results, it was possible to highlight that higher altitudes of wheat cultivation determine an increase of pleasant notes such as fruity and herbal. A PCA on consumer test data confirmed that formulation was the main factor affecting liking scores and that the preferences were affected by age, involvement and frequency of use. An internal preference map combining panel and consumer data suggested that the majority of preferences are driven by a few key sensory attributes. Differences in liking among the considered beers revealed two main consumer groups.