engleski

Prediction of wheat baking quality based on gliadin fractions and HMW-GS data by chemometric analysis (PLS modelling)

Gluten proteins composed of gliadins and glutenins are important contributors to the wheat quality properties. 28 winter wheat cultivars differing in bread processing quality were collected at the experimental fields of the Agricultural Institute Osijek, Croatia, in growing season 2006/2007. The HMW-GS composition and gliadins content were determined by SDS-PAGE and RP-HPLC, respectively, with the aim to determine their relationship with wheat quality properties. Based on gliadins and HMW-GS data for 28 wheat cultivars developed are PLS models for prediction of 15 baking quality parameters. Applied is NIPALS algorithm for evaluation of the latent variables and regression coefficient parameters. Obtained 4-th order models have average coefficients of determination R2 =0.80. Determined variable importance in projections (VIP) coefficients revealed that HMW- GS data have the dominant influence on the baking quality parameters. For extensographic and farinographic properties the Glu-D1 locus has the main VIP coefficient while Glu-B1 locus is the most important for the indirect quality parameters. The derived PLS models and VIP coefficients could be used in molecular based wheat selection and breeding program.

wheat cultivars; indirect quality; dough rheological properties; HMW-GS; gliadins; chemometric; PLS modelling

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