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Activity Number: 78 - Statistical Consulting Applications
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Consulting
Abstract #322646
Title: Statistical Modeling on Ingredients for Sugar Reduction
Author(s): Shankang Qu* and Patricia Delease
Companies: PepsiCo and PepsiCo
Keywords: experimental design ; sugar reduction ; sensory attributes ; beverage
Abstract:

We conducted a study to reduce sugar content in a beverage while keeping essential sensorial properties similar to commercially available control product. Effect of the three calorie-reducing ingredients was evaluated. A factorial design included 10 blinded variants with different ingredient levels. The bench respondents compared sensorial properties of individual samples to unmasked control under Overall Difference, Sweetness, Mouthfeel Syrupy, and Aftertaste. The gap was recorded as a Degree of Difference on the scale 0-7 with zero for No Difference to the control. Furthermore, masked control was added to the mix, to measure the "noise" in Degree of Difference for each attribute for bias reduction and model accuracy adjusting. Response Optimizer and PCA BiPlot were applied to map variants and attributes. The mapping was also evaluated by ordinal logistic modelling where normality was less concerned. Results suggested the two ingredients were significant on Overall Difference and Sweetness. Specifically, at the optimal points of these two ingredients 2.7 Degree of Difference was achieved for Sweetness, comparing to 1.4 of overall "noise" level.


Authors who are presenting talks have a * after their name.

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