Peak Detection Assisted Classification of VOCs Using Temperature Modulated SnO2-ZnO Sensor

In the present work, a novel peak detection-based model has been proposed to extract the prominent features from the raw data acquired from the temperature modulated sensor's response. The raw data has been acquired from single composite SnO 2 -ZnO based sensor modulated at different temperatures ranging from 120°C to 250 °C. The supervised learning models have been employed on the pre-processed data for classification of target analytes. The pre-processing of the data has been done to convert the time series data into static data using proposed peak detection method. The results obtained from the classification using supervised learning algorithms have shown high accuracy up to 100 %.