基于近红外光谱技术的气体浓度检测研究
Study on gas concentration detection based on near infrared spectroscopy
梁良 1杜雨馨 1杨子建1
作者信息
- 1. 徐州工程学院机电工程学院,江苏徐州 221018
- 折叠
摘要
气体浓度在各领域分析中具有重要意义,由于在气体浓度检测过程中,受光谱维度的影响导致检测结果出现较大的误差,为了降低在测量过程中产生的不利影响,提出基于近红外光谱技术的气体浓度检测研究.通过去势-标准正态变换,校正近红外光谱基线.联合广义S变换和奇异值分解共同去噪近红外光谱,提升光谱质量.基于主成分分析提出偏最小二乘降维法用于降维近红外光谱.以朗伯比尔定律为基础,引入Lorenz线性拟合近红外光谱吸收谱线,采用梯度下降法直接拟合预处理近红外光谱吸收信号,计算得到最终气体浓度检测结果.实验结果表明,所提方法在检测甲苯、丙烷和丙烯气体浓度时,检测结果与实际气体浓度基本一致,有效降低了残差平方和与均方根误差,且检测时间低于2.3 s.
Abstract
The gas concentration is of great significance in the analysis of various fields.Because in the process of gas concentration detection,the impact of spectral dimensions leads to large errors in the detection results.In order to reduce the adverse effects in the measurement process,a gas concentration detection research based on near-infrared spectroscopy technology is proposed..The near-infrared spectral baseline was corrected by castration-standard nor-mal transformation.Combine the generalized S transform and singular value to decompose and denoise the near-infra-red spectrum to improve the spectral quality.Based on principal component analysis(PCA),the partial least squares(PLS)dimensionality reduction method is proposed for the dimensionality reduction of near-infrared spectroscopy.Based on Lambert Beer's law,Lorenz linear fitting of near-infrared spectral absorption line is introduced,and gradient descent method is used to directly fit the pre-processed near-infrared spectral absorption signal,and the final gas con-centration detection result is calculated.The experimental results show that the detection results of the proposed meth-od are basically consistent with the actual gas concentrations when detecting the concentrations of toluene,propane and propylene,effectively reducing the residual sum of squares and root mean square error,and the detection time is less than 2.3 s.
关键词
近红外光谱技术/气体浓度检测/奇异值分解/偏最小二乘降维/梯度下降法Key words
near infrared spectroscopy/gas concentration detection/singular value decomposition/partial least squares dimension reduction/gradient descent method引用本文复制引用
基金项目
国家重点研发计划(2019YFC1805404)
江苏省高等学校自然科学研究面上项目(20KJB510050)
徐州工程学院校级科研项目(XKY2018237)
出版年
2024
NETL
NSTL
万方数据