ArticleFoods (Basel, Switzerland)2021
Bootstrap Resampling of Temporal Dominance of Sensations Curves to Compute Uncertainties.
Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Predicting Temporal Liking of Food Pairings from Temporal Dominance of Sensations Data via Reservoir Computing on Crackers and Spreads.Foods (Basel, Switzerland) · 2025Article
- Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.Lupus science & medicine · 2025Article
- Synergy Analysis Between the Temporal Dominance of Sensations and Temporal Liking Curves of Strawberries.Foods (Basel, Switzerland) · 2025Article
- Prediction of Temporal Liking from Temporal Dominance of Sensations by Using Reservoir Computing and Its Sensitivity Analysis.Foods (Basel, Switzerland) · 2024Article
- TDS Similarity: Outlier Analysis Using a Similarity Index to Compare Time-Series Responses of Temporal Dominance of Sensations Tasks.Foods (Basel, Switzerland) · 2023Article
- Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
In the last decade, temporal dominance of sensations (TDS) methods have proven to be potent approaches in the field of food sciences. Accordingly, thus far, methods for analyzing TDS curves, which are the major outputs of TDS methods, have been developed. This study proposes a method of bootstrap resampling for TDS tasks. The proposed method enables the production of random TDS curves to estimate the uncertainties, that is, the 95% confidence interval and standard error of the curves. Based on Monte Carlo simulation studies, the estimated uncertainties are considered valid and match those estimated by approximated normal distributions with the number of independent TDS tasks or samples being 50-100 or greater. The proposed resampling method enables researchers to apply statistical analyses and machine-learning approaches that require a large sample size of TDS curves.
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Registered trials
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