激光与光电子学进展, 2019, 56 (23): 233002, 网络出版: 2019-11-27   

基于CARS波段筛选的甲醇汽油中甲醇含量中红外光谱检测 下载: 941次

Mid-Infrared Spectroscopy Detection of Methanol Content in Methanol Gasoline Based on CARS Band Screening
作者单位
华东交通大学机电与车辆工程学院, 江西 南昌 330013
摘要
利用中红外光谱检测技术对甲醇汽油中的甲醇含量进行检测研究。由于中红外光谱易受外界环境干扰且数据量较大,为减小运算量并提高模型精度,采用无信息变量消除( UVE)法、竞争性自适应重加权取样(CARS)法以及遗传算法(GA算法)等来选择有效光谱波段,再建立对应的偏最小二乘(PLS)模型,最后分别建立PLS、UVE-PLS、GA-PLS和CARS-PLS模型,探索最优的甲醇含量检测模型。结果表明:CARS-PLS模型效果最好,预测相关系数和预测均方根误差分别为0.978和1.177。CARS算法是一种有效提取甲醇含量的中红外光谱检测方法,采用中红外光谱检测技术测定甲醇汽油中的甲醇含量是可行的,可以有效简化运算模型,提高模型检测精度。
Abstract
The mid-infrared spectroscopy detection can be used to the determination of methanol content in methanol gasoline. The mid-infrared spectra are susceptible to external interference and yield a large amount of data. To simplify the calculation and improve the accuracy of the model, the methods of uninformative variable elimination (UVE), competitive adaptive re-weighted sampling (CARS), and genetic algorithm (GA) are used to select effective spectral bands; then, a corresponding partial least squares (PLS) model is established. Finally, the PLS, UVE-PLS, GA-PLS, and CARS-PLS models are established to explore the optimal methanol content detection model for methanol gasoline. Results show that the CARS-PLS model performs the best, with the predicted correlation coefficient and root mean square error are 0.978 and 1.177, respectively. The CARS algorithm is a very effective wavelength extraction method for the methanol content in methanol gasoline, and detection technology utilizing the mid-infrared spectrum can be applied to determining the methanol content in methanol gasoline, which can effectively simplify calculations and improve the accuracy of the model detection.

胡军, 刘燕德, 欧阳爱国, 刘洪量. 基于CARS波段筛选的甲醇汽油中甲醇含量中红外光谱检测[J]. 激光与光电子学进展, 2019, 56(23): 233002. Jun Hu, Yande Liu, Aiguo Ouyang, Hongliang Liu. Mid-Infrared Spectroscopy Detection of Methanol Content in Methanol Gasoline Based on CARS Band Screening[J]. Laser & Optoelectronics Progress, 2019, 56(23): 233002.

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