Discernment of patterns within the families of curves presented in Figure is not feasible without employing numerical signal enhancement techniques. To this end, K-means clustering has been applied in this study. The first cluster extracted from K-means is represented by blue curves with overall higher intensities, while the second cluster consists of orange curves with overall lower intensity values (Figure ). It is important to note that the clustering process considers the behavior of the entire curve rather than individual intensity values at specific time points. For instance, if a noisy curve exhibits a few ouliers values but overall decreases in intensity, it is still assigned to the orange cluster (visible in Figure ). This approach of analyzing the entire curve with K-means differs significantly from simple thresholding, which relies on intensity values at specific time points and is unsuitable for this context due to the low signal-to-noise ratio.