Low-Cost Electronics for Automatic Classification and Permittivity Estimation of Glycerin Solutions Using a Dielectric Resonator Sensor and Machine Learning Techniques

SIMPLE SUMMARY: Glycerin is an organic substance used as an ingredient for many industries, including pharmaceuticals and cosmetics, but also, glycerin is an important product during biodiesel refining. Accurate and real-time sensors are needed to improve the industrial process; therefore, we proposed a workflow to measure concentrations of glycerin using a microwave sensor enhanced by machine learning models. We tested this methodology with complex electronic instrumentation and a designed low-cost portable electronic reader. As a result, we found that both devices achieved excellent and similar performance. These findings are valuable since monitoring the glycerin concentration may help to increase efficiency and reduce costs in the industry. In addition, the methodology proposed in this study could be applied to any sensor, making it a valuable contribution to liquid analysis with microwave sensors. ABSTRACT: Glycerin is a versatile organic molecule widely used in the pharmaceutical, food, and cosmetic industries, but it also has a central role in biodiesel refining. This research proposes a dielectric resonator (DR) sensor with a small cavity to classify glycerin solutions. A commercial VNA and a novel low-cost portable electronic reader were tested and compared to evaluate the sensor performance. Within a relative permittivity range of 1 to 78.3, measurements of air and nine distinct glycerin concentrations were taken. Both devices achieved excellent accuracy (98–100%) using Principal Component Analysis (PCA) and Support Vector Machine (SVM). In addition, permittivity estimation using Support Vector Regressor (SVR) achieved low RMSE values, around 0.6 for the VNA dataset and between 1.2 for the electronic reader. These findings prove that low-cost electronics can match the results of commercial instrumentation using machine learning techniques.