## 5 Discussion  
The predicted TL curves for the untrained brands exhibited mean prediction errors ranging from 0.59 to 0.71. Given that the TL values were rated on a scale from 1 to 9 and the TL curves peaked at approximately 6, the average prediction error was around 10% of the maximum TL values. While we cannot definitively conclude whether such errors are acceptable, as no precise standards have been established, these results suggest that TL curves can be reasonably predicted from TDS curves. This approach has the potential to significantly reduce the costs associated with sensory evaluation tasks performed by human assessors. The results of this study suggest that the framework for cross-brand prediction is effective when applied to different brands of the same food that share similar sensory profiles. However, this approach may not be applicable to food brands with significantly divergent characteristics. For instance, predicting TL curves for milk coffee using a model trained on black coffee may be challenging. Currently, the extent to which differences in food brands can be accommodated by this method remains unclear. As a next step, we aim to expand the range of food brands studied to evaluate the impact of such variations on the performance of cross-brand prediction. Predictive models can serve as a complement to, rather than a replacement for, human panels. For instance, consider a scenario where a fifth coffee brand is being developed in addition to the existing four brands. Suppose sufficient TDS and TL data have already been collected from a large number of panels for the four existing brands. A reservoir network could then be constructed using the data from these four brands. In this case, it would be beneficial to construct more than ten networks using resampled data. In this study, we utilized 16 networks. For the new brand prototypes, a small number of panels would conduct TDS tasks. Using these TDS curves as input, the reservoir networks would generate TL curves. Since 16 slightly different TL curves would be obtained from the 16 networks, their mean and variance should be used as references. In this way, the TL tasks for the prototypes could be omitted. In addition to the above method, it may also be beneficial to conduct TL tasks with a small number of panels for the prototypes to enhance the reliability of the predictions. The resulting data can then be used to further refine the reservoir networks. This concept is likely analogous to transfer learning in machine learning [46; 47], where a model trained on one domain is adapted to a different target domain. In such scenarios, it is common to fine-tune \begin{table}
\begin{tabular}{c c c} \hline
**Brand** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Kilimanjaro & \(0.59\pm 0.03\) & \(0.85\pm 0.01\) \\ Columbia & \(0.57\pm 0.02\) & \(0.86\pm 0.02\) \\ Brazil & \(0.53\pm 0.02\) & \(0.87\pm 0.01\) \\ Mocha & \(0.46\pm 0.02\) & \(0.89\pm 0.01\) \\ \hline \end{tabular}
\end{table} 
Table 4: Intra-brand prediction results with 95% confident intervals. \begin{table}
\begin{tabular}{c c c c} \hline
**Brand** & \begin{tabular}{c} _U-Value_ \\ **(Cross-Brand)** \\ \end{tabular} & \
\begin{tabular}{c} _U-Value_ \\ **(Intra-Brand)** \\ \end{tabular} &
\begin{tabular}{c} _p-Value_ \\ \end{tabular} \\ \hline Kilimanjaro & 55,507 & 46,893 & \(6.56\times 10^{-2}\) \\ Columbia & 36,272 & 66,128 & \(1.74\times 10^{-10}\) \\ Brazil & 41,433 & 60,967 & \(2.97\times 10^{-5}\) \\ Mocha & 32,865 & 69,535 & \(5.11\times 10^{-15}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Wilcoxon–Mann–Whitney test statistics between cross-brand RMSE and intra-brand RMSE of four brands. or retrain the model using data from the target domain. In the case of reservoir computing, retraining is not involved; instead, the approach relies on learning that integrates sufficient data from other brands with a small amount of data from the target brand. For this purpose, employing trained panels to obtain high-quality and unbiased data for the target brand may be effective.