Low-Cost Electronic Nose for Robusta and Arabica Coffee Classification Using 1-D Convolutional Neural Networks

Abstract

Abstract The use of an electronic nose (e-nose) employing MQ-series sensors has become increasingly popular due to its cost-effectiveness. However, the impact of varying sampling durations on its performance, particularly in distinguishing between Robusta and Arabica coffee, has not been extensively studied. This research investigates how different sampling durations affect the e-nose’s classification accuracy. The study utilizes a 1D Convolutional Neural Network (1D-CNN) and a Support Vector Machine (SVM), with both models enhanced by a Savitzky-Golay filter to reduce noise and improve data quality. Feature selection techniques are applied to address data complexity and improve model performance. The experimental results indicate that the 1D-CNN model achieves optimal accuracy of up to 100% at a sampling duration of 200 seconds, while the SVM reaches approximately 92% accuracy under the same conditions. Notably, for applications requiring faster processing times, the SVM proves to be more effective, achieving 70% accuracy at a 20-second sampling interval, compared to 60% for the 1D-CNN. The study demonstrates that integrating MQ sensors in e-nose systems can yield effective classification results for coffee varieties, even with shorter sampling times. These findings have the potential to influence the development of cost-effective e-nose devices, making them more accessible to small and medium-sized industrial enterprises.