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

Youngseung Lee,Han Kwak,Jean Meullenet,Marura Lenjo

doi:10.9755/ejfa.2015-09-793

Youngseung Lee, Han Kwak + Show 2 more

Open Access

https://doi.org/10.9755/ejfa.2015-09-793

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Abstract

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.

Highlights

Estimating food quality by correlating sensory evaluation results and instrumental measurement has been widely studied (Chen, et al, 2005; Jeong, et al, 2013; Lee, et al, 2014)
Estimating cooked rice quality by instrumental measurements – Toyo taste meter and eating quality indicator – has been conducted by correlating consumer acceptance ratings in order to find out which instrumental parameters were related with consumer acceptance
Two regression models – partial least square regression (PLSR) and Jackknife resampling method (JRM) – were applied to estimate sensory texture attributes of cooked rice using the results from the texture analyzer

Summary

Introduction

Estimating food quality by correlating sensory evaluation results and instrumental measurement has been widely studied (Chen, et al, 2005; Jeong, et al, 2013; Lee, et al, 2014). Kwak et al (2015a) have shown differences in drivers’ liking and disliking of aseptic-packaged cooked rice based on consumer acceptance and two instrumental measurements using partial least square regression (PLSR) analysis. The instrumental measurements were not highly correlated with consumer acceptability or many descriptive attributes in aseptic-packaged cooked rice. For frozen-cooked rice, hardness and stickiness based on the texture analyzer were highly correlated with consumer acceptance (Kwak et al, 2015b). These studies were mainly focused on the relationship between consumer acceptance and instrumental quality measurement rather than the relationship between attribute intensities and texture profiles

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