光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2013年
11期
2997-3001
,共5页
邓之银%张冰%董伟%王晓萍
鄧之銀%張冰%董偉%王曉萍
산지은%장빙%동위%왕효평
食用植物油%拉曼光谱%脂肪酸含量%多输出最小二乘支持向量回归机
食用植物油%拉曼光譜%脂肪痠含量%多輸齣最小二乘支持嚮量迴歸機
식용식물유%랍만광보%지방산함량%다수출최소이승지지향량회귀궤
Edible vegetable oil%Raman spectroscopy%Fatty acid content%Multi-output least squares support vector regression (MLS-SVR)
为实现食用植物油中饱和脂肪酸、油酸、亚油酸含量的快速预测，对一批纯食用油以及不同比例两两混合油共91个样品进行了拉曼光谱检测，在800～2000 cm-1范围内，通过基于寻峰算法的自动确定支点的基线拟合方法，对获得的光谱数据进行预处理，提取八个特征峰作为拉曼光谱的特征值。以这些特征值为输入，以样品油中实际饱和脂肪酸、油酸、亚油酸含量为输出，运用偏最小二乘回归（PLS ）和多输出最小二乘支持向量回归机（MLS-SVR）方法，分别建立了可以同时预测三种脂肪酸含量的数学模型，结果表明MLS-SVR方法具有较好的效果。将MLS-SVR模型的预测结果与气相色谱法结果相比较，可得到三种脂肪酸的预测均方根误差分别为0.4967％，0.8400％和1.0199％，相关系数分别为0.8133，0.9992和0.9981；对未知样品三种脂肪酸的预测均方根误差不超过5％。表明，拉曼光谱和MLS-SVR相结合的食用油脂肪酸含量预测方法，具有快速、简便、无损、准确等优点，为食用油脂肪酸含量分析提供了一种可行的方法。
為實現食用植物油中飽和脂肪痠、油痠、亞油痠含量的快速預測，對一批純食用油以及不同比例兩兩混閤油共91箇樣品進行瞭拉曼光譜檢測，在800～2000 cm-1範圍內，通過基于尋峰算法的自動確定支點的基線擬閤方法，對穫得的光譜數據進行預處理，提取八箇特徵峰作為拉曼光譜的特徵值。以這些特徵值為輸入，以樣品油中實際飽和脂肪痠、油痠、亞油痠含量為輸齣，運用偏最小二乘迴歸（PLS ）和多輸齣最小二乘支持嚮量迴歸機（MLS-SVR）方法，分彆建立瞭可以同時預測三種脂肪痠含量的數學模型，結果錶明MLS-SVR方法具有較好的效果。將MLS-SVR模型的預測結果與氣相色譜法結果相比較，可得到三種脂肪痠的預測均方根誤差分彆為0.4967％，0.8400％和1.0199％，相關繫數分彆為0.8133，0.9992和0.9981；對未知樣品三種脂肪痠的預測均方根誤差不超過5％。錶明，拉曼光譜和MLS-SVR相結閤的食用油脂肪痠含量預測方法，具有快速、簡便、無損、準確等優點，為食用油脂肪痠含量分析提供瞭一種可行的方法。
위실현식용식물유중포화지방산、유산、아유산함량적쾌속예측，대일비순식용유이급불동비례량량혼합유공91개양품진행료랍만광보검측，재800～2000 cm-1범위내，통과기우심봉산법적자동학정지점적기선의합방법，대획득적광보수거진행예처리，제취팔개특정봉작위랍만광보적특정치。이저사특정치위수입，이양품유중실제포화지방산、유산、아유산함량위수출，운용편최소이승회귀（PLS ）화다수출최소이승지지향량회귀궤（MLS-SVR）방법，분별건립료가이동시예측삼충지방산함량적수학모형，결과표명MLS-SVR방법구유교호적효과。장MLS-SVR모형적예측결과여기상색보법결과상비교，가득도삼충지방산적예측균방근오차분별위0.4967％，0.8400％화1.0199％，상관계수분별위0.8133，0.9992화0.9981；대미지양품삼충지방산적예측균방근오차불초과5％。표명，랍만광보화MLS-SVR상결합적식용유지방산함량예측방법，구유쾌속、간편、무손、준학등우점，위식용유지방산함량분석제공료일충가행적방법。
For the purpose of the rapid prediction of saturated fatty acid ,oleic acid ,linoleic acid content in edible vegetable oil , the Raman spectra of a batch of edible vegetable oils and their one-one mixtures with different ratios were measured in the range of 800~2 000 cm -1 ,91 samples were measured totally in this research ,the obtained Raman spectra data were preprocessed by a new method proposed in this paper called auto-set fulcrums baseline fitting method based on peak-seeking algorithm ,and 8 characteristic peak values (872 cm -1 [ν(C-C )] , 972 cm -1 [δ(C C) trans ] , 1 082 cm-1 [ν(C-C )] , 1 267 cm -1 [δ( C- H) cis] ,1 303 cm -1 [δ(CH2 )twisting] ,1 442 cm-1 [δ(CH2 ) scissoring] ,1 658 cm -1 [ν(C C) cis] ,1 748 cm-1 [ν(C O)]) were extracted to be the eigenvalues for the whole spectra ,among the 8 peaks there are three peaks(972 ,1 267 , 1 658 cm-1 ) that play an important role in the establishment of mathematical model ,they are closely concerned with C C band which distinguishes the three fatty acid types .By using these eigenvalues as inputs ,and actual saturated fatty acid ,oleic acid ,linoleic acid contents of sample oils as outputs ,a prediction mathematical model that predicts simultaneously the three fatty acid contents was established using multiple regression analysis :multi-output least squares support vector regression machine (MLS-SVR) and partial least squares(PLS) .Results show that the MLS-SVR has better effects .The predicting results are compared with results of gas chromatography (GC) ,and the obtained root mean square error of prediction (RMSEP) for saturat-ed fatty acid ,oleic acid ,linoleic acid are 0.496 7% ,0.840 0% and 1.019 9% ,and the correlation coefficients (r) are 0.813 3 , 0.999 2 and 0.998 1 ,respectively .When this model is applied in the detection of new unknown oil samples ,the prediction error does not exceed 5% .Results show that the Raman spectra analysis technology based on MLS-SVR can be a convenient ,fast , non-destructive ,and precise new method for oil detection .
