Abstract:         This article is about the study of portable fiber optic spectrometer CO detection technology. The detection system were built by used small optical fiber spectrometer as the core ，and the spectrum signal used wavelet transform to preprocess. Processed CO spectral feature extraction based on the information using a gaussian fitting method and CO in the characterization of eigenvalues are obtained by fitting the spectral information， finally a precise mathematical model were established by using partial least squares method and principal component analysis (PCA) . The method can fast and extractly characterize information from spectral signal and build the CO detection model .

Key words: Mathematical modeling,    , Signal preprocessing,    , Feature point extraction,    , Near Infrared Spectrum

摘要：         针对便携式光纤光谱仪的CO 检测技术进行了研究。CO 检测系统采用小型光纤光谱仪为核心进行搭建,光谱信号采用小波变换进行预处理。处理后的CO 光谱信息采用高斯拟合算法进行特征提取,利用拟合得到的特征值来表征CO 光谱信息,最后利用偏最小二乘方法和主成分回归法建立准确的数学模型。该方法能够快速准确地从光谱信号中提取特征信息并建立CO 检测模型。

关键词: 数学建模,   ,  信号预处理,   ,  特征点提取,    , 近红外光谱