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Sensory texture characteristics of cooked rice were predicted with a texture analyzer using a full predictive model (partial least square regression; PLSR) and an optimized predictive model (jackknife resampling method; JRM). Texture parameters of 102 cooked rice samples were measured using a spectral stress strain analysis. Eleven sensory texture characteristics were evaluated using a trained descriptive panel. JRM showed slightly better prediction for sensory texture attributes than PLSR due to the removal of insignificant variables. The following four sensory attributes were strongly predicted by JRM based on the calibration model correlation coefficient (Rcal): cohesion of bolus (Rcal = 0.78), adhesion to lips (Rcal = 0.83), cohesiveness (Rcal = 0.69), and hardness (Rcal = 0.72). Cohesiveness, toothpull and toothpack were moderately predicted (Rcal ≥ 0.60). The results from the texture analyzer were able to estimate sensory texture attributes, which were directly related to texture characteristics such as hardness, stickiness, cohesiveness, etc.

Estimating sensory texture of cooked rice using full and optimized predictive regression models