The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

From mood to food and from food to mood: A psychological perspective on the measurement of food-related emotions in consumer research

Functional Data Analysis With R And Matlab

Temporal Check-All-That-Apply (TCATA): A novel dynamic method for characterizing products

What reported food-evoked emotions may add : A model to predict consumer food choice

Exploring taffy product consumption experiences using a multi-attribute time–intensity (MATI) method

A global study using PANAS (PA and NA) scales to measure consumer emotions associated with aromas of phytonutrient supplements

Sensory measurement underlies sensory science. Sensory analysis and decision-making heavily depend on the quality of sensory data, which is determined by the performance of trained sensory panels and panelists. Various methods have been proposed for monitoring and assessing the performance. A weakness of the currently used methods is lack of a unified framework for various criteria and a variety of experiments with different types of data. This paper proposes to use accuracy, validity and reliability as general terminologies to describe sensory measurement and to apply the intraclass correlation coefficient (ICC) as a framework for monitoring and assessing performance. ICC can measure both similarity among panelists and sensitivity of panels and panelists. Hence, ICC can handle the problems of both reliability and validity. ICC can be obtained from different types of data for diverse experiments. This paper provides the equations and R and S-Plus functions for estimations of ICCs from continuous data (ratings), multivariate continuous data, ordinal data, ranking data, binary-choice data, multiple-choice data and forced-choice data. Confidence intervals, variances of the estimators, comparison with a fixed value and difference and similarity tests for multiple ICCs are also provided. The relationship between Cronbach's coefficient alpha and ICC is discussed.



Practical Applications

Intraclass correlation coefficient (ICC) may play a framework role in monitoring and assessing performance of trained sensory panels and panelists. It can be used as an index of the quality of sensory data. The larger the ICC or Cronbach's coefficient alpha value, the better the performance of panels and panelists. If and only if an ICC is significantly larger than a specified value, can the quality of the data for that attribute be regarded as acceptable. A statistical test that fails to show that an ICC or Cronbach's coefficient alpha is significantly larger than a specified lowest limit, e.g., 0.1, suggests that the products are undiscriminating, or the sensory data, at least for that attribute, might not be valid and reliable. We should use that data with caution.

Intraclass correlation coefficient (icc): a framework for monitoring and assessing performance of trained sensory panels and panelists

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.

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

Measuring and analyzing time-intensity (T-I) of sensory attributes is an important subject of sensory analysis. This paper proposes using functional data analysis (FDA), an emerging field in statistical research, as a new strategy and framework for sensory T-I data analysis. This paper discusses and illustrates how to apply the well-developed FDA techniques and use the available R package “fda” to analyze sensory T-I data. The analyses include data smoothing, descriptions of functional data, functional regression analysis, functional analysis of variance and permutation tests of functional hypotheses (functional F-tests and t-tests). Some conclusions about applications of FDA to sensory T-I data are reached.



Practical Applications

The currently used statistical analysis for sensory T-I data is not well developed. There are some limitations of using univariate and multivariate data analyses for sensory T-I data. FDA is a promising alternative. This paper shows some advantages using FDA and the R package “fda” for the analysis of sensory T-I data. It is expected that the introduction and application of FDA in the sensory field will dramatically improve data analysis for T-I evaluation of sensory attributes.

Jian Bi

Papers

Exploring taffy product consumption experiences using a multi-attribute time–intensity (MATI) method

A global study using PANAS (PA and NA) scales to measure consumer emotions associated with aromas of phytonutrient supplements

Intraclass correlation coefficient (icc): a framework for monitoring and assessing performance of trained sensory panels and panelists

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

Using Functional Data Analysis (FDA) Methodology and the R Package “fda” for Sensory Time-Intensity Evaluation