<![CDATA[This research focused on the development and evaluation of machine learning algorithms for aroma classification using sensor data, implemented within the e-Trainose system. Various algorithms, including Neural Network, Support Vector Machines, and Random Forest, were tested to determine their effectiveness in distinguishing between different aroma samples, namely alcohol, coffee, and tea. The study utilized an array of metal oxide semiconductor sensors to capture the volatile organic compounds associated with each aroma. The features tested included sensor responses such as resistance changes and Gaussian smoothing of sensor data. Among the algorithms tested, Neural Network demonstrated the highest accuracy (98.89%), precision (99.10%), recall (99.10%), and F1 score (99.10%), making it the most reliable model for this task. These results highlight the potential of using machine learning with e-Trainose for real-time aroma detection and classification. The research paves the way for future advancements in the field, including the development of hybrid models and further optimization of sensor-based classification systems.]]>
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