基于拉曼光谱技术的可燃液体快速检测研究

拉曼光谱作为一种分子“指纹”图谱,能够根据物质分子间的振动对物质进行定性分析,广泛应用在很多领域。拉曼光谱可应用于便携式监测系统,但其数据量偏大,如果不对其数据处理,会增加后续的分析时间,影响自动识别的速度。文中选取九种常见可燃液体90#汽油、93#汽油、97#汽油、丙酮、二甲苯、甲醇、乙醇、乙二醇、叔丁醇为例,对其进行数据预处理分析,特征峰提取效果显著,进而对数据进行512点的压缩,然后选取支持向量机(support vector machine,SVM)分类算法和随机森林分类算法进行模型训练。研究结果表明,随机森林算法的识别可燃液体样品的交叉验证精度高于SVM算法,随机森林算法的均方误差的结果也都优于SVM算法。运用拉曼光谱技术可明显检测出可燃液体样品的谱峰,对数据进行压缩,提高分析速度,可为后续仪器小型化提供技术参考。

Research on Rapid Detection of Flammable Liquid Based on Raman Spectroscopy Technology

As a kind of molecular fingerprint spectrum,Raman spectroscopy can perform qualitative analysis of substances based on the vibration between the molecules of substances,and is widely used in many fields.Raman spectroscopy can be applied to portable monitoring systems,but the amount of data is too large.If the data is not processed,it will greatly increase the subsequent analysis time and affect the speed of automatic identification.Nine common flammable liquids such as 90#,93#and 97#gasoline,acetone,xylene,methanol,ethanol,ethylene glycol and tertbutanol are selected,data preprocessing analysis is performed on them,and the characteristic peak extraction effect is significantly,the data is compressed by 512 points,and then the support vector machine(SVM)and the random forest(RF)classification algorithms are selected for model training.Research results show that the crossvalidation accuracy of the RF algorithm for identifying combustible liquid samples is higher than that of the SVM algorithm,and the mean square error results of the RF algorithm are also better than that of the SVM algorithm.The use of Raman spectroscopy technology can clearly detect the peaks of combustible liquid samples,and compress the data to increase the analysis speed,which can provide technical references for subsequent instrument miniaturization.