Particle Size Distribution Model for Optimizing Coffee Grind Consistency

Abstract

The particle size distribution (PSD) of ground coffee significantly influences its extraction, flavor, and overall beverage quality. This study aimed to develop, validate, and optimize PSD models for the coffee grinding process. Arabica coffee beans subjected to light, medium, and dark roasting were ground to 12 distinct levels ranging from fine to coarse. The PSDs were examined using laser diffraction. The Rosin–Rammler (RR) model was applied to the data by employing quasi-Newton (QN) and Levenberg–Marquardt (LM) optimization methods. Indicators of uniformity, including the uniformity index $\left(k\right)$, coefficient of uniformity (Cu), size span (Span), and coefficient of variation (CV), were computed and subsequently compared across various grinding levels and roasting types. Both the QN and LM methodologies demonstrated an excellent fit to the PSD data, evidenced by high R² values across all grinding levels. The medium grinding level exhibited optimal uniformity, as indicated by the high $k$ and low Cu, Span, and CV values. Although the medium roast displayed slightly superior uniformity, the Kruskal–Wallis analysis revealed no statistically significant differences in grind consistency across the various roast types. This study demonstrated the effectiveness of PSD modeling for characterizing coffee grind consistency. The results provide insights for optimizing grinding parameters to improve coffee quality, while suggesting that roast type may have a limited influence on grind uniformity compared to grinder settings. The developed models and approaches can inform coffee grinding processes and quality control.