The initial quality control (QC) assessment in our system is monitoring material sufficiency at the electrolyte exchange stage for each measurement. A key component of this process is the Droplet Detection routine, designed to verify proper flushing. This detection is implemented using OpenCV library for computer vision tasks and analyzes a video streams to detect the presence of a drop . The algorithm selects a specific region of interest (ROI) in the video frame, converts this segment into grayscale and applies a Gaussian blur to minimize noise. By continuously calculating the absolute difference between the initial frame and subsequent frames within the ROI, the system can detect motion. A non-zero sum in the thresholded image within this area is indicative of a drop's presence. In addition, visual feedback is provided on the monitor for user verification and oversight.