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Prediction of Rice Sensory Texture Attributes from a Single Compression Test, Multivariate Regression, and a Stepwise Model Optimization Method
Author(s) -
Sesmat A.,
Meullenet J.F.
Publication year - 2001
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.2001.tb15593.x
Subject(s) - partial least squares regression , multivariate statistics , stepwise regression , mathematics , sensory system , regression analysis , regression , texture (cosmology) , artificial intelligence , statistics , pattern recognition (psychology) , computer science , psychology , image (mathematics) , cognitive psychology
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).