近红外光谱定量检测丙烷和异丁烷

Quantitative Detection of Propane and Isobutene Based on NIR Spectroscopy

近红外光谱分析技术结合偏最小二乘法（PLS）对多成分挥发性有机物（VOCs）进行连续的在线监测具有重要意义．用傅里叶变换红外光谱仪（FTIR）分析了丙烷和异丁烷2种挥发性有机物的近红外光谱特征．采用线性回归建模方法——偏最小二乘法在丙烷和异丁烷混合气体的近红外光谱范围（5600～6200cm^-1）内建立了预测模型．基于该模型预测了验证集样品中2种气体的含量，并对模型进行了评价．结果表明，对2种气体浓度的预测比较准确，相对误差基本在5％以内．

Continuous and on-line monitoring volatile organic compounds （VOCs） based on NIR spectroscopy combined with partial least square （ PLS ）modeling has great significance. The characteristic spectra of two types of VOCs--propane and isobutene were analyzed using Fourier transnlitted infrared spectroscopy （ FFIR ）. A prediction model was established using linear regression modeling method--PLS in the NIR wavelength range of 5 600--6 200 cm^-1. The two gases＇ concentration in the validation set was predicted based on the model, and the model was evaluated. Results show that the prediction of the two gases＇ concentration is accurate, and the relative error is less than 5%.