Biochemical Science Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899-8362, United States.
Anal Chem. 2012 Nov 20;84(22):9774-81. doi: 10.1021/ac301687j. Epub 2012 Nov 6.
Monitoring of chemical species in breath offers an approach for the detection of disease and other conditions that cause homeostatic imbalance. Here, we demonstrate the use of microsensor-based devices for detecting select biomarkers in simulated exhaled breath as a step toward enabling fast and inexpensive breath-screening technology. Microhotplate elements functionalized with three chemiresistive metal-oxide films (SnO(2), In(2)O(3), and CuO) were used to acquire data in simulated breath containing single targets [(5 to 20) μmol/mol ammonia, methanol, and acetone], as well as mixtures of those species. All devices were operated with programmed thermal cycles featuring rapid temperature excursions, during which film resistances were measured. Material-specific temperature programs were optimized to achieve temperature-dependent metal-oxide sensing film conductance levels and target selectivity. A supervised hierarchical machine-learning algorithm using linear discriminant analysis for dimensional reduction of sensing data and discrimination was developed. This algorithm was employed in the classification and quantification of biomarkers. This approach to microsensor data collection and processing was successful in classifying and quantifying the model biomarkers in validation-set mixtures.
监测呼吸中的化学物质为检测疾病和其他导致体内平衡失调的情况提供了一种方法。在这里，我们展示了基于微传感器的设备在模拟呼气中检测选定生物标志物的应用，这是实现快速、廉价呼吸筛选技术的一步。微热板元件功能化的三种电阻式金属氧化物薄膜（SnO2、In2O3 和 CuO）用于采集含有单个目标[（5 到 20）μmol/mol 氨、甲醇和丙酮]以及这些物质混合物的模拟呼吸中的数据。所有设备都采用具有快速温度骤变的程控热循环进行操作，在此期间测量薄膜电阻。优化了特定于材料的温度程序，以实现温度依赖性金属氧化物传感薄膜电导率水平和目标选择性。开发了一种使用线性判别分析进行传感数据降维和判别监督分层机器学习算法。该算法用于生物标志物的分类和定量。这种微传感器数据采集和处理方法成功地对验证集混合物中的模型生物标志物进行了分类和定量。
