« Previous
Next »
Journal of Dairy Science
Volume 90, Issue 5
, Pages
2091-2102
, May 2007
Identification of the Characteristics That Drive Consumer Liking of Butter
-
Principal components biplot of descriptive sensory analysis of commercial butters. Numbers represent samples (Table 2). Underlined numbers represent those chosen for consumer testing. PC1 = principal
Principal components biplot of descriptive sensory analysis of commercial butters. Numbers represent samples (Table 2). Underlined numbers represent those chosen for consumer testing. PC1 = principal component 1; PC2 = principal component 2.
-
Principal components biplot of descriptive sensory analysis of commercial butters. Numbers represent samples (Table 2). Underlined numbers represent those chosen for consumer testing. PC3 = principalPrincipal components biplot of descriptive sensory analysis of commercial butters. Numbers represent samples (Table 2). Underlined numbers represent those chosen for consumer testing. PC3 = principal component 3; PC4 = principal component 4.
-
Internal preference map of consumer results. Numbers represent samples (Table 2). PC1 = principal component 1; PC2 = principal component 2.Internal preference map of consumer results. Numbers represent samples (Table 2). PC1 = principal component 1; PC2 = principal component 2.
-
Partial least squares model of consumer scores. Loading plot of principal component 1 (PC1) vs. PC2. Principal component 1 explains 40%; PC2 explains 20%. Numbers indicate samples (Table 2). DifferentPartial least squares model of consumer scores. Loading plot of principal component 1 (PC1) vs. PC2. Principal component 1 explains 40%; PC2 explains 20%. Numbers indicate samples (Table 2). Different samples (by consumers) are located far apart from each other.
-
Sample partial least squares model of consumer scores. The loading plot of principal component 3 (PC3) vs. PC4. Principal component 3 explains 19%; PC4 explains 12%. Numbers indicate samples (Table 2)Sample partial least squares model of consumer scores. The loading plot of principal component 3 (PC3) vs. PC4. Principal component 3 explains 19%; PC4 explains 12%. Numbers indicate samples (Table 2). Different samples are located far apart from each other.
-
Correlation biplot of descriptive attributes using the partial least squares model. Principal component 1 (PC1) explains 40%; PC2 explains 20%. Attributes are descriptive attributes (Table 1). The strCorrelation biplot of descriptive attributes using the partial least squares model. Principal component 1 (PC1) explains 40%; PC2 explains 20%. Attributes are descriptive attributes (Table 1). The strength of the correlation is indicated by the distance from the origin. The inner ellipse indicates 50% variance and the outer ellipse indicates 100% variance.
-
Correlation biplot of descriptive attributes using the partial least squares model. Principal component 3 (PC3) explains 19%; PC4 explains 12%. Attributes are descriptive attributes (Table 1). The strCorrelation biplot of descriptive attributes using the partial least squares model. Principal component 3 (PC3) explains 19%; PC4 explains 12%. Attributes are descriptive attributes (Table 1). The strength of the correlation is indicated by the distance from the origin. The inner ellipse indicates 50% variance and the outer ellipse indicates 100% variance.
-
Overall acceptability scores for butter and spreads within different consumer segments. Acceptability was scored based on a 9-point hedonic scale where 1 = dislike extremely and 9 = like extremely. POverall acceptability scores for butter and spreads within different consumer segments. Acceptability was scored based on a 9-point hedonic scale where 1 = dislike extremely and 9 = like extremely. P = product.
PII: S0022-0302(07)71699-5
doi: 10.3168/jds.2006-823
© 2007 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
« Previous
Next »
Journal of Dairy Science
Volume 90, Issue 5
, Pages
2091-2102
, May 2007
