Pure substances dataset acquired with a temperature-modulated redundant MOX gas sensor array (DS-PS)

This dataset contains measurements of three pure volatile compounds (ethanol, acetone, and butanone) acquired with a temperature-modulated metal oxide (MOX) gas sensor array with high redundancy. The sensing platform combines: multiple sensor types (8), sensor replication (12), temperature modulation, and multiple load resistances (16), resulting in a highly redundant representation of the chemical space. Each compound is measured at seven concentration levels (0 to 120 ppm), covering a typical operating range for gas sensing applications. The full experimental sequence is repeated 10 times, with randomized exposure order to reduce systematic biases. A baseline measurement using synthetic dry air is acquired before each exposure. The dataset captures both transient and steady-state responses of the sensor array under controlled conditions, making it suitable for studies on: sensor redundancy and diversity feature extraction in high-dimensional chemical sensing calibration transfer robustness to sensor drift or damage machine learning for gas sensing Data are provided in MATLAB .mat format. Scripts are included to load and process the data in Python and R. This dataset complements the dataset 'Binary Mixtures dataset acquired with a temperature-modulated redundant MOX gas sensor array': https://doi.org/10.5281/zenodo.19564927