Sensory traits prediction in dry-cured hams from fresh product via MRI and lipid composition

The aim of this paper is to describe a methodology that can predict Iberian dry-cured ham sensory traits from raw material characteristics, lipid composition and Magnetic Resonance Imaging-based analysis, by using Multiple Linear Regression statistics. Thus, 18 sensory traits are tried to be defined from 10 fatty acids and 17 computational texture features. Dependence linearity within each group of independent variables is determined. Then, Multiple Linear Regression (MLR) is applied, obtaining allowable statistical coefficients (adjusted coefficient of determination, R2¯ > 0.750 and p-value < 0.05) for five sensory traits defined from fatty acids (fat hardness, lean hardness, flavour intensity, brightness and juiciness), and four traits from computational texture features (marbling, odour intensity, flavour intensity and redness). Finally, prediction analysis is validated with a display of statistical data (R2¯LOO and p-valueLOO). Therefore, some sensory traits in Iberian dry-cured hams can be predicted from fatty acids and computational texture characteristics in fresh products.