Prediction of Rice Sensory Texture Attributes from a Single Compression Test, Multivariate Regression, and a Stepwise Model Optimization Method

Abstract

ABSTRACT: Sensory texture characteristics of cooked rice (92 samples) were predicted using a compression test and a novel multivariate analysis method (that is, Partial Least Squares Regression optimized by a stepwise method). 11 sensory texture characteristics were evaluated via a trained descriptive panel, and 14 instrumental parameters from a compression test were used in combination with Partial Least Squares Regression to evaluate predictive models for each of the sensory attributes studied.Among the texture attributes evaluated by the panel, 7 (cohesion of bolus, adhesion to lips, hardness, cohesiveness of mass, roughness of mass, toothpull, and toothpack) were satisfactorily predicted after the optimization by the stepwise method (optimized Rcal > 0.6).