Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves
Abstract
:1. Introduction
2. Sensory Evaluation: TDS and TL Methods
2.1. Food Stimuli: Four Coffee Brands
2.2. Panels
2.3. Procedures of TDS Method
2.4. Sensory Attributes Used in TDS Tasks
2.5. Procedures of TL Method
2.6. TDS and TL Curves
2.7. Results of TDS and TL Tasks
3. Predictive Models
3.1. Reservoir Computing
3.2. Model Specification
3.3. Data Augmentation Using Bootstrap Resampling
3.4. Training and Validation
4. Results
4.1. Cross-Brand Prediction
4.2. Intra-Brand Prediction
4.3. Comparison Between Cross-Brand and Intra-Brand Predictions
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Attribute | Description |
---|---|
Astringent | Dry taste like unripe fruit. |
Bitter | Bitter (the basic taste). |
Caramel-like | Flavor of caramel or burnt sugar. |
Cocoa-like | Flavor of cacao products and chocolates. |
Nutty | Taste and flavor of dried nuts. |
Rich | Complex, thick and harmonious flavors. |
Roasted | Toasty, fully roasted, and not raw or green. |
Smoky | Flavor of smoked or charcoal-grilled foods. |
Smooth | Smooth texture, not powdery or grainy. |
Sour | Sour (the basic taste). |
Sweet | Sweet (the basic taste). |
Leaking Rate | Spectral Radius | Input Scaling |
---|---|---|
0.01–0.06 | 0.70–0.99 | 0.34–9.47 |
Brand | RMSE | |
---|---|---|
Kilimanjaro | ||
Columbia | ||
Brazil | ||
Mocha |
Brand | RMSE | |
---|---|---|
Kilimanjaro | ||
Columbia | ||
Brazil | ||
Mocha |
Brand | U-Value (Cross-Brand) | U-Value (Intra-Brand) | p-Value |
---|---|---|---|
Kilimanjaro | 55,507 | 46,893 | |
Columbia | 36,272 | 66,128 | |
Brazil | 41,433 | 60,967 | |
Mocha | 32,865 | 69,535 |
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Natsume, H.; Okamoto, S. Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves. Appl. Sci. 2025, 15, 948. https://doi.org/10.3390/app15020948
Natsume H, Okamoto S. Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves. Applied Sciences. 2025; 15(2):948. https://doi.org/10.3390/app15020948
Chicago/Turabian StyleNatsume, Hiroharu, and Shogo Okamoto. 2025. "Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves" Applied Sciences 15, no. 2: 948. https://doi.org/10.3390/app15020948
APA StyleNatsume, H., & Okamoto, S. (2025). Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves. Applied Sciences, 15(2), 948. https://doi.org/10.3390/app15020948