Evidence map›Paper›PMID 37238843›Full record

ArticleFoods (Basel, Switzerland)2023

TDS Similarity: Outlier Analysis Using a Similarity Index to Compare Time-Series Responses of Temporal Dominance of Sensations Tasks.

Hiroharu Natsume, Shogo Okamoto, Hikaru Nagano

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Hiroharu NatsumeDepartment of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan.
Shogo OkamotoDepartment of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan.ORCID 0000-0003-2116-7734
Hikaru NaganoDepartment of Mechanical Engineering, Kobe University, Kobe 657-8501, Japan.ORCID 0000-0001-5230-6288

Funding

Lotte Foundation 2023-B-1
6 · The paper itself

Abstract

Temporal dominance of sensations (TDS) methods are used to record temporally developing sensations while eating food samples. Results of TDS tasks are typically discussed using averages across multiple trials and panels, and few methods have been developed to analyze differences between individual trials. We defined a similarity index between two time-series responses of TDS tasks. This index adopts a dynamic level to determine the importance of the timing of attribute selection. With a small dynamic level, the index focuses on the duration for attributes to be selected rather than on the timing of the attribute selection. With a large dynamic level, the index focuses on the temporal similarity between two TDS tasks. We performed an outlier analysis based on the developed similarity index using the results of TDS tasks performed in an earlier study. Certain samples were categorized as outliers irrespective of the dynamic level, whereas the categorization of a few samples depended on the level. The similarity index developed in this study achieved individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to TDS methods.

Indexed as

discrete timeindividual analysissimilarity indexstrawberrytemporal dominance of sensations

Identifiers

PMID37238843
PMCPMC10217113

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.