Dataset Information

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

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.

SUBMITTER: Kuesten C

PROVIDER: S-EPMC6025064 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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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.

Kuesten Carla C Bi Jian J

Foods (Basel, Switzerland) 20180603 6

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. Dy ...[more]

PMID: 29865288

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Project description:BackgroundSensory perception is a temporal phenomenon highly present in food evaluation. Over the last decades, several sensory analysis methods have been developed to determine how our processing of the stimuli changes during tasting. These methods differ in several parameters: how attributes are characterized (intensity, dominance or applicability), the number of attributes evaluated, the moment of sample characterization (simultaneously with the tasting in continuous or discrete time, retrospectively), the required panel (trained subjects or consumers), etc. At the moment, there is no systematic review encompassing the full scope of this topic. This article presents the protocol for conducting a scoping review on multi-attribute temporal descriptive methods in sensory analysis in food science.MethodsThe protocol was developed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews checklist. The research question was "how have multi-attribute temporal descriptive methods been implemented, used and compared in sensory analysis?". The eligibility criteria were defined using the PICOS (Population, Intervention, Comparator, Outcome, Study design) framework. This protocol details how the articles of the final review will be retrieved, selected and analyzed. The search will be based on the querying of two academic research databases (Scopus and Web of Science). The main topics reported in research involving sensory analyses methods will be identified and summarized in a data extraction form. This form (detailed in the protocol) will be used to report pertinent information regarding the objectives of the review. It could also be reused as a guideline for carrying out and reporting results of future research in a more standardized way. A quality appraisal process was derived from literature. It will be applied on the included articles of the review, and could also be re-used to ensure that future publications meet higher quality levels. Finally, for the sake of transparency, the limitations of the protocol are discussed.

Dataset Information

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

Publications

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

Similar Datasets

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