Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews

Carla Kuesten; Jian Bi

doi:10.3390/foods7060084

Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews

Foods. 2018 Jun 3;7(6):84. doi: 10.3390/foods7060084.

Authors

Carla Kuesten¹, Jian Bi²

Affiliations

¹ Amway, Ada, MI 49355, USA. carla.kuesten@amway.com.
² Sensometrics Research and Service, Richmond, VA 23236, USA. bbdjcy@aol.com.

Abstract

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages 'fda', 'kappalab' and 'relaimpo' were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews.

Keywords: Choquet integral; LMG statistic; Shapley value; fruit chews; functional data analysis; fuzzy measure; interaction indices; multi-attribute time-intensity (MATI) data; multi-criteria decision-making; multicollinearity; relative importance of attributes to liking; temporal drivers of liking (TDOL).