光谱学与光谱分析, 2013, 33 (2): 390, 网络出版: 2013-03-27   

基于净信号的乙醇含量拉曼光谱分析方法研究

Research on Raman Spectra Quantitative Analysis Model of Ethanol Aqueous Solution Based on Net Analyte Signal
作者单位
北京航空航天大学仪器科学与光电工程学院, 精密光机电一体化技术教育部重点实验室, 北京100191
摘要
拉曼光谱检测技术由于其快速、 无损等优点可以满足工业现场测量的要求, 因此已经广泛地应用于各种定量定性的分析领域。 酒精度即乙醇含量的体积比是酒类产品品质检测中的关键参数, 开发乙醇含量实时、 便捷的检测系统对酒类产品生产具有重要的意义。 将净信号分析方法应用于乙醇水溶液拉曼光谱的定量分析中, 将乙醇的净信号与其浓度建立一元线性回归模型。 结果表明, 基于净信号回归的乙醇拉曼光谱定量分析方法, 相比较于特征峰强一元线性回归模型和偏最小二乘回归模型, 不仅提高了模型的预测精度, 增强了模型的稳健性, 便于模型传递, 而且模型算法简洁、 稳定, 便于实现便携式仪器的开发。
Abstract
Raman spectroscopy technique could satisfy industrial field measurement requirements because of the advantages of rapidity and nondestructiveness. Thus, It has been widely used in a variety of quantitative and qualitative analysis fields. The volume ration of alcohol and water in alcoholic beverages, i.e. the ethanol content, is a key factor in the detection of the quality of wine products. The development of the detection system for quick, easy and real-time ethanol content measurement is of great significance for national production. In the present paper, net signal analysis method (NAS) was used in the ethanol content quantitative analysis of Raman spectra from the ethanol aqueous solution. The linear regression model was developed between the net signal of ethanol and its concentration. The results showed that the qualitative analysis method based on net signal analysis was superior to the traditional linear regression method such as the MLR method based on the feature peak intensity and the PLS model. It not only improved the prediction accuracy of the model but also enhanced the robustness of the model. Because the model established by the NAS-MLR method is simple and stable, it is possible to develop the portable measurement instruments in future.

李庆波, 于超, 张倩暄. 基于净信号的乙醇含量拉曼光谱分析方法研究[J]. 光谱学与光谱分析, 2013, 33(2): 390. LI Qing-bo, YU Chao, ZHANG Qian-xuan. Research on Raman Spectra Quantitative Analysis Model of Ethanol Aqueous Solution Based on Net Analyte Signal[J]. Spectroscopy and Spectral Analysis, 2013, 33(2): 390.

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