Cylindrical Cavity Resonating Sensor for Testing Moisture and Drug Content in Capsule Based on Machine Learning

Abstract

This article presents an improved cylindrical cavity sensor combined with machine learning techniques for the measurement of moisture and drug content (DC) in capsules. The sensor consists of a cylindrical cavity, two probe pins, and a transparent plastic tube that enables capsule passage. The cylindrical cavity, crafted with copper gilding, features inner dimensions of $\phi ~100\times 12$ mm, resulting in a minimum resonant frequency of 2.3 GHz. The proposed measurement method demonstrated an average sensitivity of 17 MHz per percentage of relative moisture content (MC). Two machine learning methods, namely, principal component analysis (PCA) and the Naive Bayes (NB) algorithms are applied to separate capsules with different DCs. Performing the ${S} _{{21}}$ amplitude and phase parameters analysis at 13.19–13.21 GHz, the proposed testing method combined with these two machine learning methods achieved 100% classification accuracy of capsules with different DCs in a single measurement. Furthermore, the classification accuracy of capsules with different DCs in five measurements reached 94%. This methodology offers a microwave sensor designed for the concurrent and accurate assessment of moisture and mass content in items such as cigarettes and coffee beans that can traverse the plastic tube, encompassing, but not restricted to capsules.