Prediction of Rice Texture by Spectral Stress Strain Analysis: a Novel Technique for Treating Instrumental Extrusion Data Used for Predicting Sensory Texture Profiles

Sensory texture characteristics of cooked rice for three cultivars (74 samples) were predicted using an extrusion cell and a novel data analysis method (i. e. Spectral Stress Strain Analysis). Eight sensory texture characteristics were evaluated and force values from the instrumental tests were used in combination with Partial Least Squares regression to evaluate predictive models for each of the sensory attributes studied. Relative Ability of Prediction (RAP) values were evaluated for each model; they ranged from 0.06 to 0.85. Satisfactory models are proposed for the two major texture Characteristics of cooked rice, namely hardness (RAP=O.85) and stickiness as evaluated by adhesion to lips (RAP=O.76). Other sensory attributes such as roughness of mass (RAP= 0.73) and toothpack (RAP=O.81) were also satisfactorily predicted. Sensory attributes such as toothpull (RAP=O.12) and loose particles (RAP= 0.06) could not be predicted using the Spectral Stress Strain Analysis.