Characterization of Bimodal Particle Size Distribution of Ground Coffee Powder

The particle size distribution (PSD) of coffee grounds plays a crucial role in determining the extraction behavior. Existing research on the PSD of coffee grounds is primarily based on a single characteristic diameter, which is insufficient to represent a typical bimodal PSD of coffee grounds. The objective of this study was to develop a model that can accurately describe the PSD of coffee grounds. Ten different five‐parameter combination models were compared based on their fit performance in describing the bimodal PSD of coffee grounds produced from different grinders, grinding levels, and beans. Results indicated that the Lognormal/Weibull (LW), Weibull/Betaprime (WB), and Gamma/Weibull (GW) models consistently resulted in the best fit performance. The commonality among the three models was the second component, that is, the Weibull model, which accurately captured the coarser particles. The LW model performed better for PSDs with a broader and lower distribution of fine particles, while GW performed better for PSDs with a narrow and higher distribution of fine particles. The WB and GW models resulted in similar fitting performance, except for one sample. The model fit accuracy was significantly influenced by the grinder type and grinding level, with bean roast degree having minimal influence. Overall, the use of the three models, LW, GW, and WB, lays a foundation for future studies by reducing the complexity of a bimodal PSD to a set of five parameters. This enables a data‐driven approach by incorporating PSD information into predictive models to provide broader insights into extraction behavior and sensory attributes.