测试技术学报2026，Vol.40Issue(3)：327-334,8.DOI:10.62756/csjs.1671-7449.2026049
用于GC-IMS的呼气分析数据处理方法
Data Processing Method for GC-IMS Exhaled Breath Analysis
摘要
Abstract
A novel data processing method of gas chromatography-ion mobility spectrometry(GC-IMS)is proposed to address the challenges of peak overlapping in GC-IMS spectra for exhaled breath analysis and the limited and continuously updated samples in applications.Breath samples simulating different physiological states were collected from healthy volunteers before and after drinking coffee,and were ana-lyzed by GC-IMS directly.Overlapping peaks in the GC-IMS two-dimensional spectra were resolved using a Gaussian second derivative peak sharpening algorithm,and a Mondrian forest(MF)incremental learning model was constructed for classification of the two states.The results indicate that the method successfully resolves overlapping peaks in the original GC-IMS spectra,increasing the peak signal-to-noise ratio(PSNR)to 50 dB.Features such as the positions and intensities of the resolved peaks in the GC-IMS spectra are extracted to build the MF incremental learning model,which maintain a high average classification accuracy of 93.75%as samples increase,significantly outperforming comparative models like random forest and Hoeffding trees.This exhaled breath analysis data processing method enhances classifi-cation accuracy,showing promising practical application prospects.关键词
气相色谱-离子迁移谱/呼气分析/重叠峰解析/蒙德里安森林Key words
gas chromatography-ion mobility spectrometry/exhaled breath analysis/overlapping peaks resolution/Mondrian forest分类
信息技术与安全科学引用本文复制引用
马睿,林建华,慕世龙,徐陈,贾建,何秀丽,高晓光..用于GC-IMS的呼气分析数据处理方法[J].测试技术学报,2026,40(3):327-334,8.基金项目
国家自然科学基金资助项目(62031022,61871364) （62031022,61871364）
