Identifying ideal products using three different consumer profiling methodologies. Comparison with external preference mapping

Ares, Gastón; Varela, Paula; Rado, Germán; Giménez, Ana

doi:10.1016/j.foodqual.2011.04.004

Cited by 73 publications

(41 citation statements)

References 27 publications

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“…As shown in Tables 6 and 7, the ideal product was described in a similar manner using intensity scales and CATA questions. Similar results have been reported by Ares, Varela, Rado, and Giménez (2011a) when identifying the ideal orange-flavoured powdered drink.…”

Section: Discussionsupporting

confidence: 89%

“…In their evaluation of the ideal product, consumers probably imagined a milk dessert with low sugar and fat content with similar sensory characteristics to regular desserts. Similar results have been obtained when asking consumers to describe the characteristics of their ideal orange-flavoured powdered drink (Ares et al, 2011a). In this study, consumers expected a product similar to natural orange juice, which was not feasible to obtain from a technological point of view.…”

Section: Discussionsupporting

confidence: 77%

See 1 more Smart Citation

Comparison of intensity scales and CATA questions in new product development: Sensory characterisation and directions for product reformulation of milk desserts

Bruzzone

Vidal

Antúnez

et al. 2015

Food Quality and Preference

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 77%

Comparison of intensity scales and CATA questions in new product development: Sensory characterisation and directions for product reformulation of milk desserts

Bruzzone

Vidal

Antúnez

et al. 2015

Food Quality and Preference

Self Cite

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“…Optimization is an efficient and practical tool for product developers (Ares, Varela, Rado, & Gimenez, 2011; Dutcosky, Grossmann, Silva, & Welsch, 2006) to achieve a competitive product in the market (Stone & Sidel, 2004; Villegas, Tarrega, Carbonell, & Costell, 2010). Not only can an optimization technique define an optimal product (Dutcosky et al, 2006), but also help evaluate effects of independent variables on the response variables.…”

Section: Introductionmentioning

confidence: 99%

Interpreting consumer preferences: Physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage

Li¹,

Hayes²,

Ziegler³

2014

Food Quality and Preference

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Designed experiments provide product developers feedback on the relationship between formulation and consumer acceptability. While actionable, this approach typically assumes a simple psychophysical relationship between ingredient concentration and perceived intensity. This assumption may not be valid, especially in cases where perceptual interactions occur. Additional information can be gained by considering the liking-intensity function, as single ingredients can influence more than one perceptual attribute. Here, 20 coffee-flavored dairy beverages were formulated using a fractional mixture design that varied the amount of coffee extract, fluid milk, sucrose, and water. Overall liking (liking) was assessed by 388 consumers using an incomplete block design (4 out of 20 prototypes) to limit fatigue; all participants also rated the samples for intensity of coffee flavor (coffee), milk flavor (milk), sweetness (sweetness) and thickness (thickness). Across product means, the concentration variables explained 52% of the variance in liking in main effects multiple regression. The amount of sucrose (β = 0.46) and milk (β = 0.46) contributed significantly to the model (p’s <0.02) while coffee extract (β = −0.17; p = 0.35) did not. A comparable model based on the perceived intensity explained 63% of the variance in mean liking; sweetness (β = 0.53) and milk (β = 0.69) contributed significantly to the model (p’s <0.04), while the influence of coffee flavor (β = 0.48) was positive but marginally (p = 0.09). Since a strong linear relationship existed between coffee extract concentration and coffee flavor, this discrepancy between the two models was unexpected, and probably indicates that adding more coffee extract also adds a negative attribute, e.g. too much bitterness. In summary, modeling liking as a function of both perceived intensity and physical concentration provides a richer interpretation of consumer data.

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“…They can be grouped together if they have very similar characteristics. This technique has been employed in products such as citrus fruit (Nestrud & Lawless, 2010), coupled with ultra-flash profiling in wines (Perrin et al, 2008), hot beverages (Moussaoui & Varela, 2010), fish nuggets (Albert, Varela, Salvador, Hough, & Fiszman, 2011) and orange-flavoured powdered drinks (Ares, Varela, Rado, & Giménez, 2011). In addition, previous research on consumers' perceptions of enriched and reduced-calorie biscuits has been conducted using the projective mapping technique (Carrillo, Varela, Salvador, & Fiszman, 2011).…”

Section: Introductionmentioning

confidence: 99%

Effects of food package information and sensory characteristics on the perception of healthiness and the acceptability of enriched biscuits

Carrillo

Varela

Fiszman

2012

Food Research International

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101

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Identifying ideal products using three different consumer profiling methodologies. Comparison with external preference mapping

Cited by 73 publications

References 27 publications

Comparison of intensity scales and CATA questions in new product development: Sensory characterisation and directions for product reformulation of milk desserts

Comparison of intensity scales and CATA questions in new product development: Sensory characterisation and directions for product reformulation of milk desserts

Interpreting consumer preferences: Physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage

Effects of food package information and sensory characteristics on the perception of healthiness and the acceptability of enriched biscuits

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