Rapid Classification of Coffee Varieties Using Single-Bean Hot Gas Extraction Ion-Mobility Spectrometry with Machine
Nathanael Aaron Prayoga1, Chamarthi Maheswar Raju1, Pawel L Urban1
1Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan.
ACS Measurement Science Au
|June 22, 2026
Summary
A new non-destructive coffee analysis platform uses ion-mobility spectrometry and a convolutional neural network (CNN) to rapidly classify coffee bean varieties and detect adulterants with high accuracy.
Area of Science:
- Analytical Chemistry
- Food Science
- Machine Learning
Background:
- Coffee adulteration is a significant risk due to global consumption.
- Traditional analytical methods for coffee are costly and time-consuming.
Purpose of the Study:
- To develop a rapid, non-destructive method for coffee bean classification and adulterant detection.
- To demonstrate the efficacy of integrated ion-mobility spectrometry and machine learning for coffee analysis.
Main Methods:
- A novel platform combining ion-mobility spectrometry with online hot-gas extraction was developed.
- A 1D convolutional neural network (CNN) model was integrated for automated data analysis.
- Single coffee beans were analyzed non-destructively.
Main Results:
- 100% accuracy in classifying four coffee varieties and ~92% accuracy for ten varieties.
- The system accurately monitored aroma degradation and predicted degradation patterns.
- 90% accuracy was achieved in detecting adulterants in coffee beans.
Conclusions:
- The integrated platform offers a rapid and non-destructive solution for coffee quality control and authenticity verification.
- The CNN model effectively differentiates coffee varieties and identifies adulteration.
- This approach provides a valuable tool for the commercial coffee industry.
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