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We developed a program that visualizes the raw input data and ML-based smell detection analysis of the SmartNanotubes Smell Inspector. The Smell Inspector is based on electronic nose (E-nose) technology that uses nanomaterial elements to detect odors or volatile organic compounds (VOCs). Classification of smells occurs through pattern recognition algorithms incorporated as trained ML models. There are diverse potential applications particularly in a health care setting, such as disease detection through breath sampling.

The program consists of a user-friendly GUI application made using the Python Tkinter library. It continuously checks the sensor’s connection status, allows the user to initiate the sensing process, and displays the raw signals on a bar plot as well as the probabilities of the detected smells updating in real-time. The current program uses a neural network trained model to detect the smell of coffee. As we progress, we plan to improve the quality of the interface and expand the range of trained models to encompass a wide range of scent classifications.

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