Evidence map›Paper›PMID 40231995›Full record

ArticleFoods (Basel, Switzerland)2025

Synergy Analysis Between the Temporal Dominance of Sensations and Temporal Liking Curves of Strawberries.

Shogo Okamoto, Hiroharu Natsume, Hiroki Watanabe

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Shogo OkamotoDepartment of Computer Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan.ORCID 0000-0003-2116-7734
Hiroharu NatsumeDepartment of Computer Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan.
Hiroki WatanabeDepartment of Computer Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan.

Funding

Lotte Foundation 2022-B1
6 · The paper itself

Abstract

The Temporal Dominance of Sensations (TDS) method allows for the real-time tracking of changes in multiple sensory attributes, such as taste, aroma, and texture, during food tasting. Over the past decade, it has become an essential tool in sensory evaluation, offering novel insights into temporal sensory perception. When combined with the Temporal Liking (TL) method, TDS enables the investigation of how sensory changes influence instantaneous liking. Existing methods in time-series sensory evaluation have not simultaneously achieved the following two key objectives: (1) predicting TL curves from TDS curves and (2) identifying shared sensory-liking synergies across samples. In this study, we address this gap by applying supervised non-negative matrix factorization, which enables both precise prediction and interpretable synergy extraction. This novel approach has the potential to extend the applicability of TDS analysis to broader sensory evaluation contexts. We validated the method using the data for strawberries recorded in an earlier study. Our model, utilizing three latent synergy components accounting for 94% of the data variation, accurately predicted the TL curves from TDS curves with a median RMSE of 0.36 in cross-validation, approximately 1/16 of the maximum TL score. Moreover, these synergy components were highly interpretable, suggesting some key factors that explain individual variations in sensory perception. These findings highlight the effectiveness of synergy analysis in time-series sensory evaluation, leading to deeper understanding of the connections between temporal sensory and liking responses.

Indexed as

non-negative matrix factorizationprincipal motionstrawberrysupervised learning

Identifiers

PMID40231995
PMCPMC11941800

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Registered trials

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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.