useR! 2024

The sensory profiles of coffee products can be generated by using predictive models with inputs from specific electrodes immersed in the liquid coffee. The electrical signals are generated by molecules diffusing through selective bio-polymers and reaching electrodes. These signals are time-series data, and various features are calculated from them (such as transient peaks or steady-state averages). These features are then used to train models for the prediction of sensory profiles or proximity to a given reference product. Six features are calculated for each of the 15 pairs of electrodes resulting in 90 variables for the classification model (proximity to a reference) or the regression model (sensory profile). These variables can be correlated to a certain extent and lead to over-fitting or unnecessary recording. This work presents a method for reducing the number of variables to a minimum relevant set using clustering and random forest variable importance. It shows that a proper selection results in a more robust model across different experimental batches.

Selecting the appropriate number of clusters is a critical step in applying clustering methods. To assist in this process, various cluster validity indices (CVIs) have been developed. These indices are designed to identify the optimal number of clusters within a dataset. However, users may not always seek the absolute optimal number of clusters but rather a secondary option that better aligns with their contexts. This realization has led us to introduce a Bayesian cluster validity index (BCVI), which builds upon existing indices. The BCVI utilizes a Dirichlet prior, resulting in the same posterior distribution. We evaluate BCVI using the Wiroonsri index for hard clustering and the WP index for soft clustering as underlying indices. We compare the performance of BCVI with that of the original underlying indices and several other existing CVIs, including DB, STR, XB, and KWON2 indices. Our BCVI offers clear advantages in situations where users can specify their desired range for the final number of clusters. Additionally, we showcase the practical applicability of our approach through MRI images. These tools are also published as a new R package `BayesCVI' available on CRAN.