Development and Application of an Electronic Nose System for Classifying Coffee Varieties Based on Aromatic Profiles
Journal Title: Journal of Intelligent Systems and Control - Year 2024, Vol 3, Issue 3
Abstract
Indonesia, a significant exporter of coffee, faces persistent challenges in accurately identifying and classifying coffee varieties based on aromatic characteristics, primarily due to the subjective variability of human sensory evaluation. To address these limitations, an electronic nose (e-nose) system was developed for the classification of coffee varieties through the analysis of aromatic profiles. The system integrates a DHT-22 sensor and four gas sensors (MQ-5, MQ-4, MQ-3, and MQ-135) to measure humidity, temperature, and gas concentrations from coffee vapor. Data acquisition was facilitated by the Arduino Uno platform, while classification was conducted using the Naive Bayes Classifier (NBC) algorithm. The e-nose achieved a classification accuracy of 82.2%, as validated through a confusion matrix and performance metrics, including precision, recall, and F1-score. Among the gas sensors employed, the MQ-4 sensor, which detects methane, demonstrated the highest response sensitivity, whereas the MQ-3 sensor, designed to detect alcohol, exhibited the lowest. This system significantly mitigates the inherent subjectivity associated with traditional aroma assessment methods and offers considerable potential for enhancing quality control protocols in coffee production processes. Future work will focus on integrating advanced machine-learning algorithms, optimizing sensor array performance, and expanding the dataset to include a broader diversity of coffee samples. These advancements are expected to further refine the system's classification capabilities and contribute to more robust quality assurance in the coffee industry.
Authors and Affiliations
Danang Erwanto, Royb Fatkhur Rizal, Dian Efytra Yuliana, Misbahul Munir, Yuki Trisnoaji, Catur Harsito, Abram Anggit Mahadi, Singgih Dwi Prasetyo
Keywords
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- EP ID EP752432
- DOI https://doi.org/10.56578/jisc030305
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