Abstract
Coffee is one of the most widely consumed beverages globally, with Americans averaging 3.1 cups per day. However, before coffee beans can be brewed into a drinkable form, they undergo several critical stages, including harvesting, processing, roasting, grinding, and extraction. During the processing and roasting phases, a significant challenge arises: stones that are similar in size and shape to coffee beans can inadvertently mix into the batch. These stones are difficult to detect using conventional methods, and their presence can have severe consequences. When stones are ground alongside coffee beans, they can cause significant damage to the grinder's burrs. Commercial coffee grinders typically employ conical or flat burrs, which consist of two circular discs or an inner blade and a disc. These burrs undergo specialized heat treatment and surface processing to ensure durability and precision, making them highly expensive components. Replacing damaged burrs is not only costly but also requires meticulous calibration of the parallelism between the inner blade and the disc to maintain grinding quality. The introduction of stones into the grinding process can lead to equipment damage, resulting in operational downtime and financial losses. To address this issue, this paper proposes a novel method based on Empirical Mode Decomposition (EMD) for detecting stones in coffee beans. The approach analyzes the acoustic wave patterns generated when stones impact or rotate within the grinder.