Journal of Dairy Science
Volume 90, Issue 5 , Pages 2091-2102 , May 2007

Identification of the Characteristics That Drive Consumer Liking of Butter

Received 6 October 2006 ,Accepted 18 January 2007.

  • Image Result

    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.

  • Image Result
    Principal components biplot of descriptive sensory analysis of commercial butters. Numbers represent samples (Table 2). Underlined numbers represent those chosen for consumer testing. PC3 = principal

    Principal 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.

  • Image Result
    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.

  • Image Result
    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). Different

    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). Different samples (by consumers) are located far apart from each other.

  • Image Result
    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.

  • Image Result
    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 str

    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 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.

  • Image Result
    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 str

    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 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.

  • Image Result
    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. P

    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. P = product.

PII: S0022-0302(07)71699-5

doi: 10.3168/jds.2006-823

Journal of Dairy Science
Volume 90, Issue 5 , Pages 2091-2102 , May 2007