  With the continuous development and improvement of medical services, there is
a growing demand for improving diabetes diagnosis. Exhaled breath analysis,
characterized by its speed, convenience, and non-invasive nature, is leading
the trend in diagnostic development. Studies have shown that the acetone levels
in the breath of diabetes patients are higher than normal, making acetone a
basis for diabetes breath analysis. This provides a more readily accepted
method for early diabetes prevention and monitoring. Addressing issues such as
the invasive nature, disease transmission risks, and complexity of diabetes
testing, this study aims to design a diabetes gas biomarker acetone detection
system centered around a sensor array using gas sensors and pattern recognition
algorithms. The research covers sensor selection, sensor preparation, circuit
design, data acquisition and processing, and detection model establishment to
accurately identify acetone. Titanium dioxide was chosen as the nano
gas-sensitive material to prepare the acetone gas sensor, with data collection
conducted using STM32. Filtering was applied to process the raw sensor data,
followed by feature extraction using principal component analysis. A
recognition model based on support vector machine algorithm was used for
qualitative identification of gas samples, while a recognition model based on
backpropagation neural network was employed for quantitative detection of gas
sample concentrations. Experimental results demonstrated recognition accuracies
of 96% and 97.5% for acetone-ethanol and acetone-methanol mixed gases, and 90%
for ternary acetone, ethanol, and methanol mixed gases.
