近红外光谱法结合偏最小二乘法预测生物柴油中甲醇的含量

Prediction of Methanol Content in Biodiesel by Near Infrared Spectroscopy Combined with Partial Least Square Method

生物柴油中甲醇的定量分析对其品质监控具有重要科学意义与实用价值,因此进行了题示研究。制作44个含不同体积分数甲醇的生物柴油样品,采集了相应的近红外光谱。采用Savitzky-Golay平滑滤波(SG)和标准正态变换(SNV)相结合的方法对原始光谱进行预处理,并采用协同区间偏最小二乘法(SIPLS)提取特征变量,确定在6270~6640 cm^(−1)和10900~11240 cm^(−1)波段内采用偏最小二乘法(PLS)建模。结果显示,所建SG-SNV-SIPLS-PLS模型的留一交叉验证决定系数R2 cv为0.9994,均方根误差RMSECV为0.0488,预测集决定系数R_(p)^(2)为0.9996,均方根误差RMSEP为0.0563,变量数为207,优于PLS模型以及单变量线性回归模型的。

The quantitative analysis of methanol in biodiesel has important scientific significance and practical value for its quality monitoring,and the title study was conducted.The 44 biodiesel samples containing different volume fractions of methanol were prepared and their corresponding near infrared spectra were collected.The original spectra obtained were preprocessed using the combination of Savitzky Golay smoothing filtering(SG)and standard normal transformation(SNV),and feature variables were extracted using synergy interval partial least square method(SIPLS).Wavelength ranges of 6270-6640 cm^(−1) and 10900-11240 cm^(−1) were determined for modeling with partial least square method(PLS).It was shown that the determination coefficient R2 cv was 0.9994,and the root mean square error RMSECV of the SG-SNV-SIPLS-PLS model from leave-one-out cross validation was 0.0488.The determination coefficient R2p was 0.9996,the root mean square error RMSEP of prediction set was 0.0563,and the number of variables was 207,which were superior to those given by the PLS model and univariate linear regression model.