Optical emission spectroscopy (OES) data analysis with inert gas, called rare gas tracing method, has become a widely accepted method for the monitoring of plasma process. However, it is becoming less desirable due to the need for a higher hardmask selectivity in etch. Conventional OES analysis focuses on bulk plasma properties, such as electron temperature and density, but fail to capture the full complexity of etch rate changes influenced by both ohmic heating and ion acceleration. To address these limitations, we propose an alternative approach that incorporates multiple plasma information (PI), offering a more comprehensive view of plasma mechanisms. This new framework was applied to develop an OES-based monitoring technique without inert gases. By modulating source and bias powers to vary both ohmic heating and ion acceleration, the multiple PI model demonstrated a higher R2 score ( 0.97) compared to the traditional Ar-based PI model ( 0.8). In addition, explainable artificial intelligence (XAI) indicated that multiple PI had greater importance, demonstrating its effectiveness in monitoring etch rates in non-inert gas processes. It not only detects changes in the etch process, but also identifies whether the variations stem from chemical or physical reactions to be useful for advanced process control.