“…HCA can calculate the similarity between nodes to categorize the variables or samples into several categories, which are presented in the form of a genealogical graph. 32 In this study, in order to make the evaluation results of the four test fingerprints obtained from the similarity evaluation by the SQFM more intuitive, the differences seen in the sample batches were calculated using HCA, which can classify the variables or samples into several categories according to the intrinsic characteristics of the data, and present them in the form of a spectrogram. The two parameters S m and P m were used for DSC, FT-IR, UV and EC, respectively, and the between-group linkage was used as the clustering method, and the Euclidean distance was chosen for the measurement interval, and the clustering results are shown in Fig.…”