journal article·2025-02-04Open access

Innovative EMD-Based Technique for Preventing Coffee Grinder Damage from Stones with FPGA Implementation

Chiang Liang Kok, Yuwei Dai, Yit Yan Koh, Maoyang Xiang, Tee Hui Teo

Applied Sciences· Volume 15· Issue 3

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Geophysical Methods and Applications · Non-Destructive Testing Techniques · Mineral Processing and Grinding

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW 2308, Australia

Engineering Product Development, Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore

References (56)

Show source-provided references
1. Coffeelab-USA (2024, October 13). Detecting Stones in Coffee Beans: Your Essential Guide. Available online: https://coffeelab-usa.com/detecting-stones-in-coffee-beans-your-essential-guide/.
2. Espressooutlet (2024, October 13). Stones in Coffee: Impact, Prevention, and What to Do. Available online: https://espressooutlet.com/blogs/news/stones-in-coffee-impact-prevention-and-what-to-do.
3. Wogancoffee (2024, October 27). Calibrating Your Grinder. Available online: https://wogancoffee.com/blogs/coffee-guides/calibrating-your-grinder.
4. Espressoparts (2024, October 27). Grinder Burr Maintenance. Available online: https://www.espressoparts.com/blogs/barista-basics-tutorials/grinder-burr-maintenance.
5. Quality Sorting of Green Coffee Beans from Wet Processing by Using The Principle of Machine Learning10.17509/ajse.v1i2.41078 ↗
6. An Efficient Real-Time Algorithm Using Shape and Cielab Color Space for Sorting Coffee Beans
7. Kok, C.L., Ho, C.K., Aung, T.H., Koh, Y.Y., and Teo, T.H. (2024). Transfer Learning and Deep Neural Networks for Robust Intersubject Hand Movement Detection from EEG Signals. Appl. Sci., 14.10.20944/preprints202408.1351.v1 ↗
8. An innovative multivariate strategy for HSI-NIR images to automatically detect defects in green coffee10.1016/j.talanta.2019.02.049 ↗
9. Development and Testing of Green Coffee Bean Quality Sorter using Image Processing and Artificial Neural Network10.61310/mndjsteect.1048.22 ↗
10. Kok, C.L., Ho, C.K., Tan, F.K., and Koh, Y.Y. (2024). Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control. Appl. Sci., 14.10.3390/app14135784 ↗
11. Farago, E., and Chan, A. (2022, January 16–19). Evaluation of interpolation methods for EMG arrays. Proceedings of the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada.10.1109/I2MTC48687.2022.9806595 ↗
12. Rubner, Y., Tomasi, C., and Guibas, L.J. (1998, January 4–7). A Metric for Distributions with Applications to Image Databases. Proceedings of the 6th International Conference on Computer Vision, Bombay, India.
13. Li, H., and Zheng, H. (2008, January 18–20). Bearing Fault Detection Using Envelope Spectrum Based on EMD and TKEO. Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China.10.1109/FSKD.2008.63 ↗
14. An Adaptive EMD Technique for Induction Motor Fault Detection
15. Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal10.1051/meca/2011150 ↗
16. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis10.1098/rspa.1998.0193 ↗
17. The application of empirical mode decomposition and Hilbert-Huang transform to the detection of rub-impact fault of the rotor system
18. EMD and Envelope Spectrum Based Bearing Fault Detection10.4028/www.scientific.net/AMR.459.233 ↗
19. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings10.1016/S0888-3270(03)00099-2 ↗
20. A new blind fault component separation algorithm for a single-channel mechanical signal mixture10.1016/j.jsv.2012.05.035 ↗
21. Göhringer, D., and Becker, J. (2010, January 5–7). FPGA-Based Runtime Adaptive Multiprocessor Approach for Embedded High Performance Computing Applications. Proceedings of the 2010 IEEE Computer Society Annual Symposium on VLSI, Lixouri, Greece.10.1109/ISVLSI.2010.30 ↗
22. Azegami, K., Kashiwakura, S., and Yamashita, K. (1996, January 11–13). Flexible FPGA Architecture realized of General Purpose SOG. Proceedings of the Fourth International ACM Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.10.1109/FPGA.1996.242251 ↗
23. A high-performance framework for a network programmable packet processor using P4 and FPGA10.1016/j.jnca.2020.102564 ↗
24. Sheldon, D., Kumar, R., Lysecky, R., Vahid, F., and Tullsen, D. (2006, January 5–9). Application-Specific Customization of Parameterized FPGA Soft-Core Processors. Proceedings of the 2006 IEEE/ACM International Conference on Computer Aided Design, San Jose, CA, USA.10.1109/ICCAD.2006.320146 ↗
25. Koch, D., Dao, N., Healy, B., Yu, J., and Attwood, A. (March, January 28). FABulous: An Embedded FPGA Framework. Proceedings of the 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Virtual Event.10.1145/3431920.3439302 ↗
26. Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration10.14778/3357377.3357383 ↗
27. FPGA Acceleration for Big Data Analytics: Challenges and Opportunities10.1109/MCAS.2021.3071608 ↗
28. Spectral entropy: A complementary index for rolling element bearing performance degradation assessment
29. Configurable Multimode Embedded Floating-Point Units for FPGAs10.1109/TVLSI.2010.2072996 ↗
30. Architectural Modifications to Enhance the Floating-Point Performance of FPGAs10.1109/TVLSI.2007.912041 ↗
31. FPGA and ASIC realisation of EMD algorithm for real-time signal processing10.1049/iet-cds.2019.0322 ↗
32. Novel Downsampling Empirical Mode Decomposition Approach for Power Quality Analysis10.1109/TIE.2015.2506619 ↗
33. Detection of Narrow-Band Signals Through the FFT and Polyphase FFT Filter Banks: Noncoherent Versus Coherent Integration10.1109/TIM.2009.2038294 ↗
34. Experimental Investigation of Signal Sensing with Overlapped FFT Based Energy Detection10.1007/s11277-013-1522-0 ↗
35. Low-Complexity Methodology for Complex Square-Root Computation10.1109/TVLSI.2017.2740343 ↗
36. The CORDIC Trigonometric Computing Technique10.1109/TEC.1959.5222693 ↗
37. Ito, M., Takagi, N., and Yajima, S. (1995, January 19–21). Efficient initial approximation and fast converging methods for division and square root. Proceedings of the 12th Symposium on Computer Arithmetic, Bath, England.
38. A linear approximation based hybrid approach for binary logarithmic conversion10.1016/S0141-9331(02)00045-5 ↗
39. Increasing processor performance by implementing deeper pipelines10.1145/545214.545219 ↗
40. A new view of nonlinear water waves: The Hilbert Spectrum10.1146/annurev.fluid.31.1.417 ↗
41. Optimization techniques for FPGA-based wave-pipelined DSP blocks10.1109/TVLSI.2005.850086 ↗
42. High-Performance Reduction Circuits Using Deeply Pipelined Operators on FPGAs10.1109/TPDS.2007.1068 ↗
43. Kim, J., Yang, H., Ryu, K., and Kim, H. (2008, January 13–15). FPGA Low Power Technology Mapping for Reuse Module Design under the Time Constraint. Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking, Hainan, China.10.1109/FGCN.2008.109 ↗
44. Kusse, E., and Rabaey, J. (1998, January 10–12). Low-energy embedded FPGA structures. Proceedings of the 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379), Monterey, CA, USA.10.1145/280756.280873 ↗
45. Eguro, K., and Hauck, S. (2008, January 8–13). Enhancing timing-driven FPGA placement for pipelined netlists. Proceedings of the 2008 45th ACM/IEEE Design Automation Conference, Anaheim, CA, USA.10.1145/1391469.1391480 ↗
46. FPGA Pipeline Synthesis Design Exploration Using Module Selection and Resource Sharing10.1109/TCAD.2006.887923 ↗
47. Pipelined Multipliers and FPGA Architectures
48. Kok, C.L., Teo, T.H., Koh, Y.Y., Dai, Y., Ang, B.K., and Chai, J.P. (2024, January 19–22). Development and Evaluation of an IoT-Driven Auto-Infusion System with Advanced Monitoring and Alarm Functionalities. Proceedings of the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore.10.1109/ISCAS58744.2024.10558602 ↗
49. Optimized co-scheduling of mixed-precision neural network accelerator for real-time multitasking applications10.1016/j.sysarc.2020.101775 ↗
50. Precedence Research (2025, January 27). Coffee Market Size, Share and Trends 2024 to 2034. Available online: https://www.precedenceresearch.com/coffee-market.
51. Empirical Wavelet Transform Based ECG Signal Filtering Method
52. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection10.1016/j.engappai.2003.09.006 ↗
53. FPGA implementation for real-time empirical mode decomposition10.1109/TIM.2012.2211460 ↗
54. Hardware design and implementation for empirical mode decomposition10.1109/TIE.2016.2531018 ↗
55. FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture10.1109/ACCESS.2019.2957819 ↗
56. Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise