光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2015年
6期
1577-1581
,共5页
张冰%邓之银%郑靖奎%王晓萍
張冰%鄧之銀%鄭靖奎%王曉萍
장빙%산지은%정정규%왕효평
拉曼光谱%汽油组分%多输出最小二乘支持向量回归机
拉曼光譜%汽油組分%多輸齣最小二乘支持嚮量迴歸機
랍만광보%기유조분%다수출최소이승지지향량회귀궤
Raman spectroscopy%Gasoline composition%Multi-output least squares support vector regression
为实现汽油中所含组分含量的快速测定，对93号、97号汽油，芳烃、烯烃、苯、甲醇、乙醇等几类物质，以及往汽油中添加几类物质后的410个汽油混合物进行拉曼光谱检测。将获取的原始拉曼光谱经过有效波段提取、平滑去噪、基线扣除、数据归一化等一系列预处理过程，最终提取出每个汽油混合样品光谱中所含的33个特征峰信息，依据现行的国标检测方法，以气相色谱法测定的汽油中各组分含量值为基础，结合化学计量学多重回归分析方法，建立了汽油组分含量测定模型。经过比较，使用多输出最小二乘支持向量回归机（MLS‐SVR）建立的模型优于偏最小二乘（PLS）模型。MLS‐SVR模型对汽油中芳烃、烯烃、苯、甲醇、乙醇测定精度均较好，预测均方根误差（RMSEP）分别为0.27％，0.30％，0.16％，0.17％，0.12％；相应的相关系数（ r）为0.9992，0.9984，0.9985，0.9926，0.9968。通过对未知混合汽油样品的测定，证明了该方法具有较好的推广预测精度，预测均方根误差不超过0.5％，能够满足工业中的测量需求。拉曼光谱结合多输出最小二乘支持向量机为汽油组分测定提供了一种高精确、快捷、方便的测定方法。
為實現汽油中所含組分含量的快速測定，對93號、97號汽油，芳烴、烯烴、苯、甲醇、乙醇等幾類物質，以及往汽油中添加幾類物質後的410箇汽油混閤物進行拉曼光譜檢測。將穫取的原始拉曼光譜經過有效波段提取、平滑去譟、基線釦除、數據歸一化等一繫列預處理過程，最終提取齣每箇汽油混閤樣品光譜中所含的33箇特徵峰信息，依據現行的國標檢測方法，以氣相色譜法測定的汽油中各組分含量值為基礎，結閤化學計量學多重迴歸分析方法，建立瞭汽油組分含量測定模型。經過比較，使用多輸齣最小二乘支持嚮量迴歸機（MLS‐SVR）建立的模型優于偏最小二乘（PLS）模型。MLS‐SVR模型對汽油中芳烴、烯烴、苯、甲醇、乙醇測定精度均較好，預測均方根誤差（RMSEP）分彆為0.27％，0.30％，0.16％，0.17％，0.12％；相應的相關繫數（ r）為0.9992，0.9984，0.9985，0.9926，0.9968。通過對未知混閤汽油樣品的測定，證明瞭該方法具有較好的推廣預測精度，預測均方根誤差不超過0.5％，能夠滿足工業中的測量需求。拉曼光譜結閤多輸齣最小二乘支持嚮量機為汽油組分測定提供瞭一種高精確、快捷、方便的測定方法。
위실현기유중소함조분함량적쾌속측정，대93호、97호기유，방경、희경、분、갑순、을순등궤류물질，이급왕기유중첨가궤류물질후적410개기유혼합물진행랍만광보검측。장획취적원시랍만광보경과유효파단제취、평활거조、기선구제、수거귀일화등일계렬예처리과정，최종제취출매개기유혼합양품광보중소함적33개특정봉신식，의거현행적국표검측방법，이기상색보법측정적기유중각조분함량치위기출，결합화학계량학다중회귀분석방법，건립료기유조분함량측정모형。경과비교，사용다수출최소이승지지향량회귀궤（MLS‐SVR）건립적모형우우편최소이승（PLS）모형。MLS‐SVR모형대기유중방경、희경、분、갑순、을순측정정도균교호，예측균방근오차（RMSEP）분별위0.27％，0.30％，0.16％，0.17％，0.12％；상응적상관계수（ r）위0.9992，0.9984，0.9985，0.9926，0.9968。통과대미지혼합기유양품적측정，증명료해방법구유교호적추엄예측정도，예측균방근오차불초과0.5％，능구만족공업중적측량수구。랍만광보결합다수출최소이승지지향량궤위기유조분측정제공료일충고정학、쾌첩、방편적측정방법。
For the purpose of the rapid prediction of every composition in gasoline ,the Raman spectra of the gasoline brand 93 and 97 ,a batch of one‐one mixtures with aromatic ,olefin ,ben ,methanol and ethanol with different ratios are measured ,410 mixture samples were measured totally in this research .The obtained Raman spectra were preprocessed by a series of process‐ing ,they were data smoothing ,baseline deduction and spectral normalized ,etc .After that 33 characteristic peaks were extracted to be the eigenvalues for the whole Raman spectra .According to the current national standard test method ,the values of every composition were measured by the gas chromatography .By using the eigenvalues as inputs ,and actual contents of aromatic ,ole‐fin ,ben ,methanol and ethanol got from gas chromatography as outputs ,two mathematical models of multi‐output least squares support vector regression and partial least squares combination with multiple regression analysis were established to predict the values of the above compositions of a sample ,respectively .The predicting results were compared with the values calculated from the gas chromatography measurement results and the mixture proportions ,the multi‐output least squares support vector regres‐sion has a better effects ,and the obtained root mean square error of prediction for aromatic ,olefin ,ben ,methanol and ethanol are 0.27% ,0.27% ,0.22% ,0.17% ,0.14% ;the correlation coefficients are 0.999 3 ,0.998 5 ,0.998 6 ,0.992 3 ,0.993 5 , respectively .This model is also applied to the detection of the unknown sample ,the root mean square error of the prediction for the results does not exceed 0.5% ,which can achieve the measurement requirements in the industry .Results show that the Ra‐man spectra analysis technology based on multi‐output least squares support vector regression can be a precise ,fast and conven‐ient new method for gasoline composition detection ,and can be applied to the quality control of the gasoline production process , transportation ,storage of the gasoline .
