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PREDICTION OF RICE TEXTURE BY SPECTRAL STRESS STRAIN ANALYSIS: A NOVEL TECHNIQUE FOR TREATING INSTRUMENTAL EXTRUSION DATA USED FOR PREDICTING SENSORY TEXTURE PROFILES
Author(s) -
MEULLENET JEANFRANCOIS C.,
SITAKALIN C.,
MARKS B. P.
Publication year - 1999
Publication title -
journal of texture studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.593
H-Index - 54
eISSN - 1745-4603
pISSN - 0022-4901
DOI - 10.1111/j.1745-4603.1999.tb00229.x
Subject(s) - texture (cosmology) , partial least squares regression , sensory system , sensory analysis , extrusion , biological system , mathematics , strain (injury) , food science , artificial intelligence , materials science , pattern recognition (psychology) , computer science , statistics , chemistry , biology , composite material , anatomy , neuroscience , image (mathematics)
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=0.85) and stickiness as evaluated by adhesion to lips (RAP=0.76). Other sensory attributes such as roughness of mass (RAP=0.73) and toothpack (RAP=0.81) were also satisfactorily predicted. Sensory attributes such as toothpull (RAP=0.12) and loose particles (RAP=0.06) could not be predicted using the Spectral Stress Strain Analysis.