Unveiling the capacitive sensor performance developed for Epicatechin detection in Green tea: A Clustering Approach

In this present treatise, a selective and economical capacitive sensor performance study has been presented using an unsupervised clustering method. The sensor response, derived from a capacitive sensor modified with a molecularly imprinted polymer (MIP), was analyzed using two popular clustering algorithms: K-Means and Agglomerative clustering. To measure the clustering efficacy of the sensor, Silhouette, and Davies-Bouldin scores were employed in this work. The maximum Silhouette scores achieved were 0.8568 and 0.8465 for the two algorithms respectively, with corresponding minimum Davies-Bouldin scores of 0.2016 and 0.2013. These outcomes demonstrate the sensor's proficiency in discerning EC content differences among the green tea samples.