Real-time monitoring of lube oil plays a crucial role in ensuring optimal machinery performance, preventing failures, and facilitating timely maintenance strategies. The approach proposed in this work, based on impedance spectroscopy and supervised machine learning, addresses this need by a novel solution to a multi-class classification problem of cross-contaminations in aviation lubricant. Impedance measurements were performed at room temperature by immersing a microfabricated sensor in sixteen aged oil samples containing increasing concentrations of water and aviation fuel. Two datasets were constructed, the first based on impedance components spectra, the second based on dissipation factor spectra. A data pre-processing and augmentation method was proposed for generating synthetic examples from the measured data. Both datasets were independently used to train three supervised classifiers …