Entwicklung und Test neuronaler Netze mithilfe von Edge Impulse auf einem Mikrocontroller zur Detektion und Klassifikation von flüchtigen organischen Verbindungen (VOCs)

A neural‑network model was trained with the low‑code platform Edge Impulse to recognise volatile organic compounds (VOCs) from data gathered by a cheap metal‑oxide gas sensor. The model was then deployed on an Arduino Nicla Sense ME microcontroller, enabling on‑device detection and classification of VOC sources such as cigarette smoke, 3D‑printer emissions and cooking fumes without any network connection. The results demonstrate that inexpensive embedded hardware can reliably identify common indoor VOCs, while also highlighting the limits of sensor sensitivity and the need for careful measurement conditions.