Lubrication oil is a vital component of heavy rotating machinery defining the machine's health, operational safety and effectiveness. Recently, the focus has been on developing sensors that provide real-time/online monitoring of oil condition/lubricity. Industrial practices and standards for assessing oil condition involve various analytical methods. Most these techniques are unsuitable for online applications. The paper presents the results of studying degradation of antioxidant additives in machinery lubricants using Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy and Machine Learning techniques. EEM Spectroscopy is capable of rapid and even standoff sensing; it is potentially applicable to real-time online monitoring.