卷烟主流烟气中巴豆醛在线光谱分析方法研究

卷烟主流烟气是卷烟燃烧时被人体吸食到体内的主要气体，其减焦降害已成为全社会高度关注的问题。在各种卷烟主流烟气组分中，巴豆醛以其强烈的基因毒性，成为国家规定的卷烟中七种主要有害指标物之一。传统的巴豆醛分析方法大都采用高效液相色谱法等实验室分析方法，需繁琐的样品前处理过程，无法测量巴豆醛的实时浓度，难以准确评估巴豆醛对人体健康的影响。为了快速、准确地检测卷烟主流烟气中的巴豆醛组分，本研究搭建了一套可以直接与吸烟机耦合的傅里叶红外光谱分析系统（FTIR），并创新性开发过采样数据驱动光谱分析方法（ODDSA），从复杂、变动的卷烟主流烟气中准确提取巴豆醛的光谱组分信息。ODDSA方法从实验设计入手，采用随机设计的思路尽可能模拟实际卷烟样品的分布范围，以构建具备良好光谱数据结构的样品集。在此基础上，创新性地将高密度小波变换引入红外光谱数据的处理过程中，以时/频双域过采样的方式提升了光谱解析分辨率，进而降低了其他基质组分对巴豆醛光谱信息的干扰。最后，发展改良竞争自适应重加权采样方法，从多倍冗余的高密度小波系数中准确提取待测物质的最佳变量组合，由此构建高质量的巴豆醛光谱定量分析模型。为了验证ODDSA方法的有效性，实验中采集了15种典型市售卷烟品牌，每个品牌在线采集8支样品的主流烟气红外光谱，随后采用随机挑选的25个验证集样本对ODDSA方法进行验证。结果表明，检验集的线性拟合系数为0.971，相对均方根误差为5.5%，其预测精度能有效满足卷烟主流烟气中巴豆醛的在线分析需求，并可拓展到环境二手烟气中其他组分的在线监测，进而为吸烟与健康评估提供全新手段。

On-Line Spectral Analysis of Crotonaldehyde Content in Cigarette Mainstream Smoke

The cigarette mainstream smoke represents the main gas that is ingested by the human body when the cigarette is burned. The reduction of scorch and other hazardous components has become an issue of great concern to the whole society. Among various components in cigarette mainstream smoke, crotonaldehyde has become one of the seven main harmful indicators in cigarettes prescribed by the state due to its strong genotoxicity. Traditional analytical methods for crotonaldehyde usually rely on high-performance liquid chromatography and other laboratory methods, which requires complex sample pretreatment procedures. This makes it difficult to measure crotonaldehyde in real-time to evaluate its effects on health. In order to monitor the crotonaldehyde content in cigarette mainstream smoke efficiently, a Fourier Transform Infrared Spectrometer (FTIR) system was set up to a couple with a smoking machine. In this system, an innovative oversampling data driven spectral analysis (ODDSA) method was developed to accurately extract the spectral features of crotonaldehyde from the complex and fluctuating spectra of cigarette mainstream smoke. The ODDSA method started with experimental design and used the idea of random design to simulate the distribution range of actual cigarette samples, which constructed a good data structure to guide further data mining. Thereafter, the high-density wavelet transform (HDWT) was innovatively used to process the IR spectra, which enabled oversampling in time/frequency dual-domains to improve the spectral resolution. This would definitely suppress the effects of other matrix components on the analysis of crotonaldehyde. Finally, the strategy of modified competitive adaptive reweighted sampling was developed to accurately extract the interseting features from the multiple redundant HDWT coefficients, which was used to construct a qualified calibration model for the analysis of crotonaldehyde. In the experiment, 15 typical commercial cigarette brands were collected, in which 8 samples of each brand were prepared to collect their IR spectra of mainstream smoke. Thereafter, 25 samples were randomly selected to validate the performance of ODDSA. The calculation results showed that the regression coefficient of the test set was 0.971, and the relative root means square error is 5.5%. The satisfactory results indicate that the ODDSA is capable of on-line analysis of crotonaldehyde in cigarette mainstream smoke, which may well extend to on-line monitoring of other components in second-hand environmental smoke. This would provide a novel tool for the evaluation of cigarette effects on health.