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.

翻译：随着医疗服务的持续发展与完善，改善糖尿病诊断的需求日益增长。呼气分析以其快速、便捷、无创的特点，引领着诊断发展的趋势。研究表明，糖尿病患者呼气中的丙酮水平高于正常值，使得丙酮成为糖尿病呼气分析的基础。这为早期糖尿病预防与监测提供了一种更易被接受的方法。针对糖尿病检测存在的侵入性、疾病传播风险及复杂性等问题，本研究旨在设计一种以传感器阵列为核心的糖尿病气体生物标志物丙酮检测系统，该系统采用气体传感器与模式识别算法。研究涵盖传感器选型、传感器制备、电路设计、数据采集与处理以及检测模型建立，以实现对丙酮的准确识别。选用二氧化钛作为纳米气敏材料制备丙酮气体传感器，并利用STM32进行数据采集。对原始传感器数据进行滤波处理后，采用主成分分析进行特征提取。基于支持向量机算法的识别模型用于气体样本的定性识别，而基于反向传播神经网络的识别模型则用于气体样本浓度的定量检测。实验结果表明，对丙酮-乙醇和丙酮-甲醇混合气体的识别准确率分别为96%和97.5%，对丙酮、乙醇和甲醇三元混合气体的识别准确率达到90%。