Prediction of rice texture by Spectral Stress Strain Analysis: A novel technique for treating instrumental extrusion data used for predicting sensorytexture profiles

Sensory texture characteristics of cooked rice for three: cultivars (74 sam ples) were predicted using an extrusion cell and a novel data analysis meth od (i.e. Spectral Stress Strain Analysis). Eight sensory texture characteri stics were evaluated and force values from the instrumental tests were used in combination with Partial Least Squares regression to evaluate Predictiv e models for each of the sensory attributes studied. Relative Ability of Pr ediction (RAP) values were evaluated for each model; they ranged from 0.06 to 0.85. Satisfactory models are proposed for the two major texture charact eristics of cooked rice, namely hardness (RAP=0.85) and stickiness:as evalu ated by adhesion to lips (RAP=0.76). Other sensory attributes such as rough ness of mass (RAP = 0.73) and toothpack (RAP = 0.81) were also satisfactori ly predicted. Sensory attributes such as toothpull (RAP=0.12) and loose par ticles, (RAP= 0.06) could not be predicted using the Spectral Stress Strain Analysis.