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PREDICTION OF SENSORY TEXTURE OF FETA CHEESE MADE FROM ULTRAFILTERED MILK BY UNIAXIAL COMPRESSION AND SHEAR TESTING
Author(s) -
WIUM H.,
QVIST K.B.
Publication year - 1998
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.1745-4603.1998.tb00165.x
Subject(s) - materials science , texture (cosmology) , partial least squares regression , shear (geology) , principal component analysis , compression (physics) , composite material , food science , mathematics , artificial intelligence , chemistry , statistics , computer science , image (mathematics)
The ability to predict sensory texture properties of Feta cheese made from ultrafiltered milk (UF‐Feta) from uniaxial compression, small shear deformation measurements (frequency sweep, strain sweep, relaxation) and indices of proteolysis was studied. In principal component analysis (PCA) some of the instrumental variables were highly correlated, e.g. the moduli from uniaxial compression and shear measurements; and strain at fracture from uniaxial compression and indices of proteolysis. PCA of the six sensory attributes determined by a trained panel showed that mainly one type of information was present in the sensory results. Partial Least Squares regression (PLS) of all results revealed that stress at fracture from uniaxial compression was the individual instrumental parameter having the highest correlation with the sensory texture attributes. Of these, the three firmness attributes were best predicted by the instrumental parameters. As the shear measurements were not very useful for prediction of sensory texture properties by themselves, and as the increase in prediction precision by inclusion of these measurements was marginal, it is suggested that either stress at fracture alone, or together with three other parameters from uniaxial compression should be used to describe texture properties of UF‐Feta cheese.