Fluidized bed granulation (FBG) is a widely used granulation technology in the pharmaceutical industry. However, defluidization caused by the formation of large aggregates poses a challenge to FBG, particularly in traditional Chinese medicine (TCM) due to its complex physicochemical properties of aqueous extracts. Therefore, this study aims to identify the complex relationships between physicochemical characteristics and defluidization using data mining methods. Initially, 50 types of TCM were decocted and assessed for their potential influence on defluidization using a set of 11 physical properties and 10 chemical components, utilizing the loss rate as an evaluation index. Subsequently, the random forest (RF) and Apriori algorithms were utilized to uncover intricate association rules among physicochemical characteristics and defluidization. The RF algorithm analysis revealed the top 8 critical factors associated with defluidization. These factors include physical properties like glass transition temperature (Tg) and dynamic surface tension (DST) of DST100ms, DST1000ms, DST10ms and conductivity, in addition to chemical components such as fructose, glucose and protein contents. The results from Apriori algorithm demonstrated that lower Tg and conductivity were associated with an increased risk of defluidization, resulting in a higher loss rate. Moreover, DST100ms, DST1000ms and DST10ms exhibited a contrasting trend in the physical properties Specifically, defluidization probability increases when Tg and conductivity dip below 29.04℃ and 6.21 ms/m respectively, coupled with DST10ms, DST100ms and DST1000ms values exceeding 70.40 mN/m, 66.66 mN/m and 61.58 mN/m, respectively. Moreover, an elevated content of low molecular weight saccharides was associated with a higher occurrence of defluidization, accompanied by an increased loss rate. In contrast, protein content displayed an opposite trend regarding chemical properties. Precisely, the defluidization likelihood amplifies when fructose and glucose contents surpass 20.35 mg/g and 34.05 mg/g respectively, and protein concentration is less than 1.63 mg/g. Finally, evaluation criteria for defluidization were proposed based on these results, which could be used to avoid this situation during the granulation process. This study demonstrated that the RF and Apriori algorithms are effective data mining methods capable of uncovering key factors affecting defluidization.
Keywords: association rules; defluidization; fluidized bed granulation; physicochemical properties; random forest.