Preference Mapping of Commercial Chocolate Milks

Abstract

The dairy beverage market is a competitive and growing category in the food industry. Within this arena, chocolate milks vary widely in flavor, color, and viscosity. Understanding what sensory properties drive consumer liking is critical for maximum market share. This study was conducted to identify and define sensory characteristics of commercial chocolate milks and to link these differences to consumer preferences through the application of internal and external preference mapping. A sensory language was identified to document the sensory properties (visual, flavor, mouthfeel) of chocolate milks. Twenty-eight commercial chocolate milks were subsequently evaluated by descriptive sensory analysis using the identified sensory language. Thirteen representative milks were chosen for consumer acceptance testing followed by internal and external preference mapping to identify key drivers. Instrumental color and viscosity measurements were also taken. Two different techniques were used for external preference mapping: cluster analysis with generalized procrustes analysis and landscape segmentation analysis. Chocolate milks were differentiated by descriptive sensory analysis (P < 0.001). Wide variability was also observed in consumer acceptability of chocolate milks (P < 0.001). Correlations were observed among descriptive and consumer, descriptive and instrumental, and instrumental and consumer results (P < 0.001). Generalized procrustes analysis revealed 2 groups of consumers with 1 nondistinguishing driver of liking: cocoa aroma. Landscape segmentation analysis confirmed and clarified generalized procrustes analysis results by identifying 3 consumer segments with 3 drivers: cocoa aroma, malty, and cooked/eggy flavors.

Sensory characterization of doda burfi (Indian milk cake) using Principal Component Analysis

“…To compare the class means two at a time, critical difference (C.D), also called least significant difference (LSD) was calculated as per the method described by Rangaswamy (1995). The same set of data were statistically analyzed using Xlstat software (version 2009) for principal component analysis to find out the components of prime importance, maximum positive loadings and their interaction with other attributes (Thompson et al 2004). For all statistical analyses, a value of p<0.05 was used as the criterion for statistical significance.…”

“…Thus, defining the product with its characteristic features is an important criterion to evaluate the product precisely and specifically. Researchers have used descriptive analysis or specific attribute analysis (sensory characterization) to study aging and various processing parameters in cheese (Piggott and Mowat 1991;Drake et al 2001), whey and soy proteins (Drake et al 2007), emollients (Parente et al 2008), Malbec wines (Goldner and Zamora 2007), chocolate milks (Thompson et al 2004), beverages (Kappes et al 2006), soy milk (Keast and Lau 2006), and fermented food products (Ghosh and Chattopadhyay 2010) however, such studies have not been conducted on any of the traditional sweetmeats including doda burfi.…”

Chemical, Nutritional and Organoleptical Characteristics of Orange-Based Formulated Low-Calorie Jams

“…Yuyama et al [44] found reduction in the caloric value ranged from 25.9% to 37.9% in jambolão fruit jams using four types of sweeteners individually or in combination (saccharin, cyclamate, acesulfame and St). Moreover, [45] [46] reported that stevioside give low calories.…”

Chocolate Milk Sweetened With Stevia: Acceptance by Children

“…Table 6 gives sensory preference (color, flavor, sweetness, texture, overall liking) for the four chocolate milks. Color of chocolate milk sample containing sucrose and stevia -18g (B) and 20g (A) received 100% approval [21].…”