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INSTRUMENTAL AND SENSORY CHARACTERIZATION OF COOKED POTATO TEXTURE
Author(s) -
THYBO ANETTE KISTRUP,
MARTENS MAGNI
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.tb00216.x
Subject(s) - partial least squares regression , principal component analysis , materials science , texture (cosmology) , sensory system , modulus , compression (physics) , mathematics , specific gravity , composite material , food science , chemistry , artificial intelligence , statistics , computer science , biology , image (mathematics) , neuroscience
Uniaxial compression, Texture Profile Analysis (TPA) and chemical measurements were related to sensory texture evaluation of potato quality during storage. Principal component analysis grouped the varieties into three types of variation: mealiness versus firmness and springiness (PC1), moistness versus adhesiveness (PC2) and hardness versus adhesiveness and moistness (PC3). In uniaxial compression the variable ‘stress', ‘work up to fracture’ and ‘total work during compression’ described the same type of information in the data. These uniaxial data and most of the TPA data were highly correlated. Uniaxial compression data (stress, strain, modulus of deformability), starch structural data (area, roundness, aspect ratio), specific gravity and pectin methyl esterase activity discriminated between the varieties and harvest times. Partial Least Squares Regression showed stress, strain, modulus of deformability and specific gravity to be the most important variables in distinguishing between two groups of sensory texture attributes explaining 65% of the total variance in the sensory data. Coefficients of correlation between predicted and measured sensory attributes were in the range 0.36–0.79. The TPA data were not found to be relevant substitutions for the sensory attributes.