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Prediction of Cooked Rice Texture Using an Extrusion Test in Combination with Partial Least Squares Regression and Artificial Neural Networks
Author(s) -
Sitakalin Chanintorn,
Meullenet JeanFrancois C.
Publication year - 2001
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.2001.78.4.391
Subject(s) - partial least squares regression , artificial neural network , discriminative model , texture (cosmology) , artificial intelligence , pattern recognition (psychology) , chemistry , biological system , regression analysis , statistics , mathematics , computer science , image (mathematics) , biology
Spectral stress strain analysis was used in combination with partial least squares (PLS) regression and artificial neural networks (ANN) to predict nine sensory texture attributes of cooked rice. The models calculated with ANN were significantly more accurate in predicting most of the sensory texture characteristics evaluated than the PLS models. Furthermore, ANN models were more robust and discriminative than PLS models.